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Combinatorial optimization
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naturally characterized as an optimization problem. An NP-optimization problem (NPO) is a combinatorial optimization problem with the following additionalBellman equation (3,990 words) [view diff] exact match in snippet view article find links to article
E. Bellman, is a technique in dynamic programming which breaks a optimization problem into a sequence of simpler subproblems, as Bellman's “principle ofDifferential evolution (1,589 words) [view diff] exact match in snippet view article find links to article
solution has the best score or fitness on the optimization problem at hand. In this way, the optimization problem is treated as a black box that merely providesConstraint (mathematics) (815 words) [view diff] exact match in snippet view article
In mathematics, a constraint is a condition of an optimization problem that the solution must satisfy. There are several types of constraints—primarilyConstrained optimization (1,844 words) [view diff] exact match in snippet view article find links to article
the conditions on the variables are not satisfied. The constrained-optimization problem (COP) is a significant generalization of the classic constraint-satisfactionTrajectory optimization (3,522 words) [view diff] exact match in snippet view article find links to article
solution is not required, impractical or impossible. If a trajectory optimization problem can be solved at a rate given by the inverse of the Lipschitz constantBarrier function (596 words) [view diff] exact match in snippet view article find links to article
its argument approaches the boundary of the feasible region of an optimization problem. Such functions are used to replace inequality constraints by a penalizingQuantum optimization algorithms (3,576 words) [view diff] exact match in snippet view article find links to article
to some criteria) from a set of possible solutions. Mostly, the optimization problem is formulated as a minimization problem, where one tries to minimizeFuzzy finite element (174 words) [view diff] exact match in snippet view article find links to article
fuzziness (uncertainty). This outer-level loop comes down to solving an optimization problem. If the inner-level deterministic module produces monotonic behaviorMinimum-distance estimation (696 words) [view diff] exact match in snippet view article find links to article
however, substantially reduces the computational complexity of the optimization problem. Let X 1 , … , X n {\displaystyle \displaystyle X_{1},\ldots ,X_{n}}Discrete optimization (174 words) [view diff] exact match in snippet view article find links to article
continuous optimization, some or all of the variables used in a discrete optimization problem are restricted to be discrete variables—that is, to assume only aCouenne (410 words) [view diff] exact match in snippet view article find links to article
also termed mixed integer nonlinear optimization problems. A global optimization problem requires to minimize a function, called objective function, subjectFLAME clustering (617 words) [view diff] no match in snippet view article find links to article
Fuzzy clustering by Local Approximation of MEmberships (FLAME) is a data clustering algorithm that defines clusters in the dense parts of a dataset andSequential minimal optimization (1,010 words) [view diff] exact match in snippet view article find links to article
Lagrange multipliers. SMO is an iterative algorithm for solving the optimization problem described above. SMO breaks this problem into a series of smallestGraduated optimization (783 words) [view diff] exact match in snippet view article find links to article
global optimization technique that attempts to solve a difficult optimization problem by initially solving a greatly simplified problem, and progressivelyOptimal decision (705 words) [view diff] exact match in snippet view article find links to article
car. The problem of finding the optimal decision is a mathematical optimization problem. In practice, few people verify that their decisions are optimalInfinite-dimensional optimization (336 words) [view diff] exact match in snippet view article find links to article
or the shape of a body. Such a problem is an infinite-dimensional optimization problem, because, a continuous quantity cannot be determined by a finiteRegiomontanus' angle maximization problem (1,172 words) [view diff] exact match in snippet view article find links to article
mathematics, the Regiomontanus's angle maximization problem, is a famous optimization problem posed by the 15th-century German mathematician Johannes Müller (alsoExact algorithm (127 words) [view diff] exact match in snippet view article find links to article
that always solve an optimization problem to optimality. Unless P = NP, an exact algorithm for an NP-hard optimization problem cannot run in worst-casePortfolio (finance) (593 words) [view diff] exact match in snippet view article
return and minimise the risk. This is an example of a multi-objective optimization problem: many efficient solutions are available and the preferred solutionRobust optimization (3,410 words) [view diff] no match in snippet view article find links to article
2 {\displaystyle \mathbb {R} ^{2}} . What makes this a 'robust optimization' problem is the ∀ ( c , d ) ∈ P {\displaystyle \forall (c,d)\in P} clauseConstraint programming (2,324 words) [view diff] exact match in snippet view article find links to article
problem; proving the unsatisfiability of the problem. A constraint optimization problem (COP) is a constraint satisfaction problem associated to an objectiveArtelys Knitro (567 words) [view diff] exact match in snippet view article find links to article
derivatives is beneficial). An often easier approach is to develop the optimization problem in an algebraic modeling language. The modeling environment computesPenalty method (922 words) [view diff] exact match in snippet view article find links to article
constrained optimization problems. A penalty method replaces a constrained optimization problem by a series of unconstrained problems whose solutions ideally convergeDistributed constraint optimization (3,425 words) [view diff] no match in snippet view article find links to article
Distributed constraint optimization (DCOP or DisCOP) is the distributed analogue to constraint optimization. A DCOP is a problem in which a group of agentsBilevel optimization (2,183 words) [view diff] exact match in snippet view article find links to article
the lower-level variables. A general formulation of the bilevel optimization problem can be written as follows: min x ∈ X , y ∈ Y F ( x , y ) {\displaystyleGekko (optimization software) (1,988 words) [view diff] exact match in snippet view article
the performance of nonlinear programming solvers. This particular optimization problem has an objective function min x ∈ R x 1 x 4 ( x 1 + x 2 + x 3 ) +Random optimization (613 words) [view diff] exact match in snippet view article find links to article
numerical optimization methods that do not require the gradient of the optimization problem and RO can hence be used on functions that are not continuous orComplementarity theory (461 words) [view diff] exact match in snippet view article find links to article
A complementarity problem is a type of mathematical optimization problem. It is the problem of optimizing (minimizing or maximizing) a function of twoSum-of-squares optimization (2,695 words) [view diff] exact match in snippet view article find links to article
A sum-of-squares optimization program is an optimization problem with a linear cost function and a particular type of constraint on the decision variablesOskar Perron (524 words) [view diff] exact match in snippet view article find links to article
paradox to illustrate the danger of assuming that the solution of an optimization problem exists: Let N be the largest positive integer. If N > 1, then N2Varignon frame (1,193 words) [view diff] exact match in snippet view article find links to article
{x} )=\sum _{i=1}^{n}m_{i}\|\mathbf {x} _{i}-\mathbf {x} \|} . The optimization problem is called Weber problem. If the holes have locations x 1 , … , xSlater's condition (650 words) [view diff] exact match in snippet view article find links to article
is a sufficient condition for strong duality to hold for a convex optimization problem, named after Morton L. Slater. Informally, Slater's condition statesBenson's algorithm (370 words) [view diff] exact match in snippet view article find links to article
Benson's algorithm is to evaluate the upper image of the vector optimization problem by cutting planes. Consider a vector linear program min C P x subjectRegularization (mathematics) (4,628 words) [view diff] exact match in snippet view article
regularization is regularization whenever one explicitly adds a term to the optimization problem. These terms could be priors, penalties, or constraints. ExplicitStrong duality (265 words) [view diff] exact match in snippet view article find links to article
theorem) the primal problem is a linear optimization problem Slater's condition for a convex optimization problem. Under certain conditions (called "constraintBasis pursuit (559 words) [view diff] exact match in snippet view article find links to article
Basis pursuit is the mathematical optimization problem of the form min x ‖ x ‖ 1 subject to y = A x , {\displaystyle \min _{x}\|x\|_{1}\quad {\text{subjectSecond-order cone programming (1,417 words) [view diff] exact match in snippet view article find links to article
A second-order cone program (SOCP) is a convex optimization problem of the form minimize f T x {\displaystyle \ f^{T}x\ } subject to ‖ A i x + b iOptimal substructure (742 words) [view diff] exact match in snippet view article find links to article
Optimality is based on the idea that in order to solve a dynamic optimization problem from some starting period t to some ending period T, one implicitlyBlind deconvolution (1,057 words) [view diff] exact match in snippet view article find links to article
However, blind deconvolution remains a very challenging non-convex optimization problem even with this assumption. In image processing, blind deconvolutionShape optimization (1,709 words) [view diff] exact match in snippet view article find links to article
uniqueness of the solution. Shape optimization is an infinite-dimensional optimization problem. Furthermore, the space of allowable shapes over which the optimizationBilinear program (33 words) [view diff] exact match in snippet view article find links to article
In mathematics, a bilinear program is a nonlinear optimization problem whose objective or constraint functions are bilinear. An example is the poolingAugmented Lagrangian method (1,940 words) [view diff] exact match in snippet view article find links to article
similarities to penalty methods in that they replace a constrained optimization problem by a series of unconstrained problems and add a penalty term to theDeterministic global optimization (1,115 words) [view diff] exact match in snippet view article find links to article
optimization which focuses on finding the global solutions of an optimization problem whilst providing theoretical guarantees that the reported solutionConditional factor demands (665 words) [view diff] exact match in snippet view article find links to article
problem, two inputs are used (often labor and capital), and the optimization problem seeks to minimize the total cost (amount spent on factors of productionOmega ratio (862 words) [view diff] exact match in snippet view article find links to article
(r)-\theta \over {\operatorname {E} [(\theta -w^{T}r)_{+}]}}+1} The optimization problem that maximizes the Omega ratio is given by: max w w T E ( r ) −Well-posed problem (1,573 words) [view diff] exact match in snippet view article find links to article
the sense above are termed ill-posed. A simple example is a global optimization problem, because the location of the optima is generally not a continuousExtremal graph theory (1,360 words) [view diff] exact match in snippet view article find links to article
graph has to satisfy? A graph that is an optimal solution to such an optimization problem is called an extremal graph, and extremal graphs are important objectsSupport vector machine (9,071 words) [view diff] exact match in snippet view article find links to article
margin. This can be rewritten as We can put this together to get the optimization problem: minimize w , b 1 2 ‖ w ‖ 2 subject to y i ( w ⊤ x i − b ) ≥ 1 ∀Image destriping (447 words) [view diff] exact match in snippet view article find links to article
class of approaches leverage compressed sensing, to regularize an optimization problem, and recover stripe free images. In many cases, these destriped imagesPortfolio optimization (2,702 words) [view diff] exact match in snippet view article find links to article
minimizes costs like financial risk, resulting in a multi-objective optimization problem. Factors being considered may range from tangible (such as assetsSpecial ordered set (819 words) [view diff] exact match in snippet view article find links to article
the branch and bound algorithm a more intelligent way to face the optimization problem, helping to speed up the search procedure. The members of a specialZionts–Wallenius method (103 words) [view diff] exact match in snippet view article find links to article
interactive method used to find a best solution to a multi-criteria optimization problem. Specifically it can help a user solve a linear programming problemWatchman route problem (253 words) [view diff] exact match in snippet view article find links to article
The Watchman Problem is an optimization problem in computational geometry where the objective is to compute the shortest route a watchman should take toAPMonitor (1,899 words) [view diff] exact match in snippet view article find links to article
analysis. # Python example for solving an optimization problem from APMonitor.apm import * # Solve optimization problem sol = apm_solve("hs71", 3) # AccessParticle swarm optimization (5,222 words) [view diff] exact match in snippet view article find links to article
problem being optimized, which means PSO does not require that the optimization problem be differentiable as is required by classic optimization methodsMinimum k-cut (847 words) [view diff] exact match in snippet view article find links to article
In mathematics, the minimum k-cut is a combinatorial optimization problem that requires finding a set of edges whose removal would partition the graphResource leveling (546 words) [view diff] exact match in snippet view article find links to article
available supply." Resource leveling problem could be formulated as an optimization problem. The problem could be solved by different optimization algorithmsGeometric programming (612 words) [view diff] exact match in snippet view article find links to article
A geometric program (GP) is an optimization problem of the form minimize f 0 ( x ) subject to f i ( x ) ≤ 1 , i = 1 , … , m g i ( x ) = 1 , i = 1 , …Cutting-plane method (1,570 words) [view diff] exact match in snippet view article find links to article
the application of the Dantzig–Wolfe decomposition to a structured optimization problem in which formulations with an exponential number of variables areRegina S. Burachik (225 words) [view diff] exact match in snippet view article find links to article
inequalities, the latter being a generalization of the convex constrained optimization problem." with A. N. Iusem and B. F. Svaiter. "Enlargement of monotone operatorsAssignment problem (2,960 words) [view diff] exact match in snippet view article find links to article
The assignment problem is a fundamental combinatorial optimization problem. In its most general form, the problem is as follows: The problem instance hasFagnano's problem (497 words) [view diff] exact match in snippet view article find links to article
In geometry, Fagnano's problem is an optimization problem that was first stated by Giovanni Fagnano in 1775: For a given acute triangle determine the inscribedActive-set method (639 words) [view diff] exact match in snippet view article find links to article
inequality-constrained problem into a simpler equality-constrained subproblem. An optimization problem is defined using an objective function to minimize or maximize, andFilter design (3,300 words) [view diff] exact match in snippet view article find links to article
acceptable degree. The filter design process can be described as an optimization problem. Certain parts of the design process can be automated, but an experiencedStrong NP-completeness (714 words) [view diff] exact match in snippet view article find links to article
programming. From a theoretical perspective any strongly NP-hard optimization problem with a polynomially bounded objective function cannot have a fully1-center problem (467 words) [view diff] exact match in snippet view article find links to article
problem or minmax location problem, is a classical combinatorial optimization problem in operations research of facilities location type. In its most generalCatallactics (1,300 words) [view diff] exact match in snippet view article find links to article
from many mainstream economic models that treat the economy as an optimization problem to be solved or engineered toward efficiency. Instead of offeringAdjoint state method (1,928 words) [view diff] exact match in snippet view article find links to article
computing the gradient of a function or operator in a numerical optimization problem. It has applications in geophysics, seismic imaging, photonics andRepresentative agent (1,141 words) [view diff] exact match in snippet view article find links to article
macroeconomic models today are characterized by an explicitly stated optimization problem of the representative agent, which may be either a consumer or aSocial planner (294 words) [view diff] exact match in snippet view article find links to article
This so-called planner's problem is a mathematical constrained optimization problem. Solving the planner's problem for all possible Pareto weights (iConsensus clustering (2,951 words) [view diff] exact match in snippet view article find links to article
sources or from different runs of the same algorithm. When cast as an optimization problem, consensus clustering is known as median partition, and has beenVariable (296 words) [view diff] exact match in snippet view article find links to article
Slack variable, inserted to transform an inequality constraint in an optimization problem into an equality Dependent and independent variables, a variableEarth mover's distance (2,199 words) [view diff] exact match in snippet view article find links to article
the measures are uniform over a set of discrete elements, the same optimization problem is known as minimum weight bipartite matching. The EMD between probabilityCutting stock problem (2,422 words) [view diff] exact match in snippet view article find links to article
pieces of specified sizes while minimizing material wasted. It is an optimization problem in mathematics that arises from applications in industry. In termsIncome–consumption curve (1,330 words) [view diff] exact match in snippet view article find links to article
income and prices play an important role in solving the consumer's optimization problem (choosing how much of various goods to consume so as to maximizeTraveling tournament problem (153 words) [view diff] exact match in snippet view article find links to article
The traveling tournament problem (TTP) is a mathematical optimization problem. The question involves scheduling a series of teams such that: Each teamBasis pursuit denoising (598 words) [view diff] exact match in snippet view article find links to article
statistics, basis pursuit denoising (BPDN) refers to a mathematical optimization problem of the form min x ( 1 2 ‖ y − A x ‖ 2 2 + λ ‖ x ‖ 1 ) , {\displaystyleH-infinity methods in control theory (1,097 words) [view diff] exact match in snippet view article find links to article
control designer expresses the control problem as a mathematical optimization problem and then finds the controller that solves this optimization. H∞ techniquesSteiner point (computational geometry) (213 words) [view diff] exact match in snippet view article
Steiner point is a point that is not part of the input to a geometric optimization problem but is added during the solution of the problem, to create a betterEvolutionary multimodal optimization (1,292 words) [view diff] exact match in snippet view article find links to article
discover hidden properties (or relationships) of the underlying optimization problem, which makes them important for obtaining domain knowledge. In additionSimultaneous equations model (3,353 words) [view diff] exact match in snippet view article find links to article
at once, this often leads to a computationally costly non-linear optimization problem even for the simplest system of linear equations. This situationGeneralized semi-infinite programming (202 words) [view diff] exact match in snippet view article find links to article
In mathematics, a semi-infinite programming (SIP) problem is an optimization problem with a finite number of variables and an infinite number of constraintsProtein design (7,724 words) [view diff] exact match in snippet view article find links to article
Hence, it is also termed inverse folding. Protein design is then an optimization problem: using some scoring criteria, an optimized sequence that will foldCaustic (optics) (1,125 words) [view diff] exact match in snippet view article
certain transparent material. This is done by solving an inverse optimization problem based on optimal transport. Given a reference image of an object/patternGeneralized tree alignment (182 words) [view diff] exact match in snippet view article find links to article
separately. Formally, Generalized tree alignment is the following optimization problem. Input: A set S {\displaystyle S} and an edit distance function dImmanuel Bomze (743 words) [view diff] exact match in snippet view article find links to article
problems as a linear optimization problem over a closed convex cone of symmetric matrices, a so-called conic optimization problem. In this type of problemsIntertemporal consumption (607 words) [view diff] exact match in snippet view article find links to article
the question of inter-temporal consumption as a lifetime income optimization problem. Solving this problem mathematically, assuming that individuals areReinforced solid (2,131 words) [view diff] exact match in snippet view article find links to article
stresses on a small cube (Fig. 1). This can be formulated as an optimization problem. The reinforcement is directed in the x, y and z direction. The reinforcementRandom search (1,003 words) [view diff] exact match in snippet view article find links to article
numerical optimization methods that do not require the gradient of the optimization problem, and RS can hence be used on functions that are not continuous orWater supply network (4,158 words) [view diff] exact match in snippet view article find links to article
problem changes from a single objective optimization problem (minimizing cost), to a multi-objective optimization problem (minimizing cost and maximizing flowLabor demand (900 words) [view diff] exact match in snippet view article find links to article
of various combinations of quantities of labor and capital. This optimization problem involves simultaneously choosing the levels of labor, capital, andKnee of a curve (615 words) [view diff] exact match in snippet view article find links to article
explicit objective function is used, and depends on the particular optimization problem. A knee may also be defined purely geometrically, in terms of theRanking SVM (2,306 words) [view diff] exact match in snippet view article find links to article
distances between any two of the vectors obtained in step 1. It forms an optimization problem which is similar to a standard SVM classification and solves thisBiconvex optimization (383 words) [view diff] exact match in snippet view article find links to article
x,y} by fixing one of them and solving the corresponding convex optimization problem. The generalization to functions of more than two arguments is calledExtension complexity (582 words) [view diff] exact match in snippet view article find links to article
a polytope describing the feasible solutions to a combinatorial optimization problem has low extension complexity, this could potentially be used to deviseActivity selection problem (1,172 words) [view diff] exact match in snippet view article find links to article
The activity selection problem is a combinatorial optimization problem concerning the selection of non-conflicting activities to perform within a givenLinear programming relaxation (2,414 words) [view diff] exact match in snippet view article find links to article
hence the name. This relaxation technique transforms an NP-hard optimization problem (integer programming) into a related problem that is solvable inRegularized least squares (4,910 words) [view diff] exact match in snippet view article find links to article
ill-posed and is therefore impossible to fit because the associated optimization problem has infinitely many solutions. RLS allows the introduction of furtherLarge margin nearest neighbor (1,428 words) [view diff] exact match in snippet view article find links to article
{\displaystyle M} by a factor of 1 / c {\displaystyle 1/c} . The final optimization problem becomes: min M ∑ i , j ∈ N i d ( x → i , x → j ) + λ ∑ i , j , lTalent (314 words) [view diff] exact match in snippet view article find links to article
entertainment or broadcasting, as in talent agent Talent scheduling, an optimization problem in computer science and operations research. All pages with titlesInverse problem (9,362 words) [view diff] exact match in snippet view article find links to article
can be cumbersome. The numerical method to be used for solving the optimization problem depends in particular on the cost required for computing the solutionSubspace identification method (637 words) [view diff] exact match in snippet view article find links to article
user parametrizes the system matrices before solving a parametric optimization problem and, as a consequence, SID methods do not suffer from problems relatedRobust control (2,112 words) [view diff] exact match in snippet view article find links to article
are sought to be optimized by casting control design as a suitable optimization problem. The ability of feedback to cope with uncertainty has been the mainModel predictive control (3,642 words) [view diff] exact match in snippet view article find links to article
algorithms (or "real-time iterations") that never attempt to iterate any optimization problem to convergence, but instead only take a few iterations towards theChebyshev center (1,507 words) [view diff] exact match in snippet view article find links to article
properties, finding the Chebyshev center may be a hard numerical optimization problem. For example, in the second representation above, the inner maximizationStochastic programming (6,069 words) [view diff] exact match in snippet view article find links to article
optimization problems that involve uncertainty. A stochastic program is an optimization problem in which some or all problem parameters are uncertain, but followIOSO (1,120 words) [view diff] exact match in snippet view article find links to article
system efficiency. An efficiency extreme value, obtained during the optimization problem while solving in traditional (deterministic) approach, is simplyTruthful job scheduling (1,748 words) [view diff] exact match in snippet view article find links to article
problem, the timings of all workers are known, so we have a standard optimization problem. In contrast, in the truthful job scheduling problem, the timingsQuantum annealing (3,462 words) [view diff] exact match in snippet view article find links to article
classical Ising model that corresponds to the solution to the original optimization problem. An experimental demonstration of the success of quantum annealingSearch-based software engineering (2,361 words) [view diff] exact match in snippet view article find links to article
for example, assigning people to tasks (a typical combinatorial optimization problem). white-box problems where operations on source code need to be consideredInformation projection (481 words) [view diff] exact match in snippet view article find links to article
closed and non-empty, then there exists at least one minimizer to the optimization problem framed above. Furthermore, if P is convex, then the optimum distributionResource smoothing (395 words) [view diff] exact match in snippet view article find links to article
leveling, a resource smoothing problem could be formulated as an optimization problem. The problem could be solved by different optimization algorithmsGeneralized least squares (2,846 words) [view diff] exact match in snippet view article find links to article
the maximum likelihood estimate (MLE), which is equivalent to the optimization problem from above, β ^ = argmax b p ( b | ε ) = argmax b log p ( b | εRobust fuzzy programming (816 words) [view diff] exact match in snippet view article find links to article
constraints and goals. ROFP is able to achieve a robust solution for an optimization problem under uncertainty. Robust solution is defined as a solution whichOptimal control (4,734 words) [view diff] exact match in snippet view article find links to article
optimization variables and the problem is "transcribed" to a nonlinear optimization problem of the form: Minimize F ( z ) {\displaystyle F(\mathbf {z} )} subjectConstraint composite graph (711 words) [view diff] exact match in snippet view article find links to article
node-weighted undirected graph associated with a given combinatorial optimization problem posed as a weighted constraint satisfaction problem. Developed andSatisficing (3,663 words) [view diff] exact match in snippet view article find links to article
Any such satisficing problem can be formulated as an (equivalent) optimization problem using the indicator function of the satisficing requirements as anCluster analysis (9,510 words) [view diff] exact match in snippet view article find links to article
distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter settings (includingMultidimensional assignment problem (830 words) [view diff] exact match in snippet view article find links to article
multidimensional assignment problem (MAP) is a fundamental combinatorial optimization problem which was introduced by William Pierskalla. This problem can be seenIterative rational Krylov algorithm (1,733 words) [view diff] exact match in snippet view article find links to article
}|G(ja)|^{2}\,da.} This is known as the H 2 {\displaystyle H_{2}} optimization problem. This problem has been studied extensively, and it is known to beIterative rational Krylov algorithm (1,733 words) [view diff] exact match in snippet view article find links to article
}|G(ja)|^{2}\,da.} This is known as the H 2 {\displaystyle H_{2}} optimization problem. This problem has been studied extensively, and it is known to beMulti-task learning (6,154 words) [view diff] exact match in snippet view article find links to article
separately. Inherently, Multi-task learning is a multi-objective optimization problem having trade-offs between different tasks. Early versions of MTLExtended Mathematical Programming (1,022 words) [view diff] exact match in snippet view article find links to article
optimization problems are mathematical programs with an additional optimization problem in their constraints. A simple example is the bilevel programmingOcean reanalysis (616 words) [view diff] exact match in snippet view article find links to article
of the equations of motion through iterative solution of a giant optimization problem. ISHII and LEVITUS begin with a first guess of the climatologicalAnderson acceleration (3,111 words) [view diff] exact match in snippet view article find links to article
g(x^{*})={\vec {0}}} . We can therefore rephrase the problem as an optimization problem where we want to minimize ‖ g ( x ) ‖ 2 {\displaystyle \|g(x)\|_{2}}Constrained conditional model (1,502 words) [view diff] exact match in snippet view article find links to article
it is natural to formulate the decision problem as a constrained optimization problem, with an objective function that is composed of learned models, subjectFeature selection (6,931 words) [view diff] exact match in snippet view article find links to article
{\displaystyle a_{ij}=I(f_{i};f_{j})} . The above may then be written as an optimization problem: m R M R = max x ∈ { 0 , 1 } n [ ∑ i = 1 n c i x i ∑ i = 1 n x iCorrelation gap (512 words) [view diff] exact match in snippet view article find links to article
variables are independent. As an example,: 6 consider the following optimization problem. A teacher wants to know whether to come to class or not. There areComplementarity (179 words) [view diff] exact match in snippet view article find links to article
measured simultaneously Complementarity theory, a type of mathematical optimization problem Quark–lepton complementarity, a possible fundamental symmetry betweenInterior-point method (4,691 words) [view diff] exact match in snippet view article find links to article
self-concordant barrier function used to encode the convex set. Any convex optimization problem can be transformed into minimizing (or maximizing) a linear functionSimultaneous perturbation stochastic approximation (1,555 words) [view diff] exact match in snippet view article find links to article
measurements of the objective function, regardless of the dimension of the optimization problem. Recall that we want to find the optimal control u ∗ {\displaystyleEnvelope theorem (3,981 words) [view diff] exact match in snippet view article find links to article
differentiability properties of the value function of a parameterized optimization problem. As we change parameters of the objective, the envelope theorem showsProcess simulation (1,078 words) [view diff] exact match in snippet view article find links to article
to find optimal conditions for a process. This is essentially an optimization problem which has to be solved in an iterative process. In the example abovePolynomial SOS (1,886 words) [view diff] exact match in snippet view article find links to article
establish whether a form h(x) is SOS amounts to solving a convex optimization problem. Indeed, any h(x) can be written as h ( x ) = x { m } ′ ( H + L (Metric k-center (3,613 words) [view diff] exact match in snippet view article find links to article
problem or vertex k-center problem is a classical combinatorial optimization problem studied in theoretical computer science that is NP-hard. Given nBatch normalization (5,892 words) [view diff] exact match in snippet view article find links to article
the Perceptron, which is the simplest form of neural network. The optimization problem in this case is m i n w ~ ∈ R d f L H ( w ~ ) = E y , x [ ϕ ( z TConvex analysis (2,605 words) [view diff] exact match in snippet view article find links to article
needed] the primal problem is a linear optimization problem; Slater's condition for a convex optimization problem. For a convex minimization problem withTransit route network design problem (179 words) [view diff] exact match in snippet view article find links to article
The transit route network design problem is a mathematical optimization problem in the context of transportation networks with well-defined stops, routesOptimJ (1,709 words) [view diff] exact match in snippet view article find links to article
unknown quantity whose value one is searching. A solution to an optimization problem is a set of values for all its decision variables that respects theAssemble-to-order system (475 words) [view diff] exact match in snippet view article find links to article
following notation In the final stage when demands are known the optimization problem faced is to minimize G ( y , d ) = h ′ x + p ′ w subject to A zKnight's tour (2,318 words) [view diff] exact match in snippet view article find links to article
ISBN 9781118659502, The knight's tour problem is a classic combinatorial optimization problem. ... The cardinality Nx of x (the size of the search space) is overQuadratic (431 words) [view diff] exact match in snippet view article find links to article
variables Quadratic programming, a special type of mathematical optimization problem Quadratic growth, an asymptotic growth rate proportional to a quadraticSeparation (550 words) [view diff] exact match in snippet view article find links to article
for example: Mutual fund separation theorem, allowing a portfolio optimization problem to be separated into smaller problems Point-pair separation, in anHighway network optimization (225 words) [view diff] exact match in snippet view article find links to article
learning. Recent research has included work on treating the highway optimization problem as a dynamic system. Sakhapov, R L; Nikolaeva, R V; GatiyatullinTSP (257 words) [view diff] exact match in snippet view article find links to article
Transit signal priority, for buses Travelling salesman problem, optimization problem Trimethylsilyl-2,2,3,3-tetradeuteropropionic acid, derivative ofLiner shipping network design and scheduling problem (279 words) [view diff] exact match in snippet view article find links to article
network design and scheduling problem (LSNDSP) is a mathematical optimization problem in operations research that models maritime transport logistic problemsRecursion (3,669 words) [view diff] exact match in snippet view article find links to article
multistep optimization problem in recursive form. The key result in dynamic programming is the Bellman equation, which writes the value of the optimization problemMaximum theorem (1,875 words) [view diff] exact match in snippet view article find links to article
is typically interpreted as providing conditions for a parametric optimization problem to have continuous solutions with regard to the parameter. In thisLoss functions for classification (4,212 words) [view diff] exact match in snippet view article find links to article
and solving for the optimal solution is an NP-hard combinatorial optimization problem. As a result, it is better to substitute loss function surrogatesSIP (657 words) [view diff] exact match in snippet view article find links to article
machinery manufacturer Semi-infinite programming, a type of mathematical optimization problem Spectral induced polarisation, in geophysics Stable-isotope probingBranch and bound (2,416 words) [view diff] exact match in snippet view article find links to article
Turning these principles into a concrete algorithm for a specific optimization problem requires some kind of data structure that represents sets of candidateAverage (3,355 words) [view diff] exact match in snippet view article find links to article
generalizes the median) can similarly be expressed as a solution to the optimization problem argmin x ∈ R ∑ i = 1 n max ( ( 1 − τ ) ( x i − x ) , τ ( x − x iMatching (graph theory) (3,032 words) [view diff] exact match in snippet view article
different classes of graphs. In an unweighted bipartite graph, the optimization problem is to find a maximum cardinality matching. The problem is solvedBroyden–Fletcher–Goldfarb–Shanno algorithm (2,987 words) [view diff] exact match in snippet view article find links to article
George Broyden, Roger Fletcher, Donald Goldfarb and David Shanno. The optimization problem is to minimize f ( x ) {\displaystyle f(\mathbf {x} )} , where xMatrix regularization (2,510 words) [view diff] exact match in snippet view article find links to article
{\displaystyle X_{i}} will have different forms, but for each of these the optimization problem to infer W {\displaystyle W} can be written as min W ∈ H E ( W )Cost-sensitive machine learning (517 words) [view diff] exact match in snippet view article find links to article
minimizing different kinds of classification errors is a multi-objective optimization problem. Cost-sensitive machine learning optimizes models based on the specificMaximum entropy probability distribution (4,495 words) [view diff] exact match in snippet view article find links to article
{\lambda }}=(\lambda _{1},\ldots ,\lambda _{n})} solve the constrained optimization problem with a 0 = 1 {\displaystyle a_{0}=1} (which ensures that p {\displaystyleOptimal computing budget allocation (5,036 words) [view diff] no match in snippet view article find links to article
In Computer Science, Optimal Computing Budget Allocation (OCBA) is a simulation optimization method designed to maximize the Probability of Correct SelectionK q-flats (2,218 words) [view diff] exact match in snippet view article find links to article
m × n , {\displaystyle A\in R^{m\times n},} solve the quadratic optimization problem where A ∈ R m × n {\displaystyle A\in \mathbb {R} ^{m\times n}} isAutomatic label placement (1,569 words) [view diff] exact match in snippet view article find links to article
particular label placement problem can be formulated as a mathematical optimization problem, using mathematics to solve the problem is usually better than usingTruncated tetrahedron (1,483 words) [view diff] exact match in snippet view article find links to article
number of faces (16 in this case) and a given surface area. For this optimization problem, the optimal geometric form for the polyhedron is one in which theBounding sphere (1,736 words) [view diff] exact match in snippet view article find links to article
require each point to lie within or on the sphere. Explicitly, the optimization problem is: minimize: r subject to: ||xi − c||₂ ≤ r, for all i where theLinear classifier (1,146 words) [view diff] exact match in snippet view article find links to article
and the desired outputs. Thus, the learning algorithm solves an optimization problem of the form arg min w R ( w ) + C ∑ i = 1 N L ( y i , w T x i )FICO Xpress (584 words) [view diff] exact match in snippet view article find links to article
distributed computing features to solve multiple scenarios of an optimization problem in parallel. Uncertainty in the input data can be handled via robustMutual fund separation theorem (1,874 words) [view diff] exact match in snippet view article find links to article
individual assets' returns. The Lagrangian for this constrained optimization problem (whose second-order conditions can be shown to be satisfied) is LDrift plus penalty (7,204 words) [view diff] exact match in snippet view article find links to article
around the Lagrange multiplier of a corresponding deterministic optimization problem. This clustering result can be used to modify the drift-plus-penaltyList of terms relating to algorithms and data structures (3,135 words) [view diff] exact match in snippet view article find links to article
Galil–Giancarlo Galil–Seiferas gamma function GBD-tree geometric optimization problem global optimum gnome sort goobi graph graph coloring graph concentrationEntropic value at risk (2,016 words) [view diff] exact match in snippet view article find links to article
1)} . Let ρ {\displaystyle \rho } be a risk measure. Consider the optimization problem where w ∈ W ⊆ R n {\displaystyle {\boldsymbol {w}}\in {\boldsymbolDynamic discrete choice (2,949 words) [view diff] exact match in snippet view article find links to article
_{i}&&\;+\;&&\varepsilon _{nit}\end{alignedat}}} 2. The optimization problem can be written as a Bellman equation Define by V n t ( x n t ) {\displaystyleLemoine point (526 words) [view diff] exact match in snippet view article find links to article
squares method gives the coordinates of the point. It also solves the optimization problem to find the point with a minimal sum of squared distances from theCarry-skip adder (1,487 words) [view diff] exact match in snippet view article find links to article
physically fastest carry-skip adder is known as the 'carry-skip adder optimization problem'. This problem is made complex by the fact that a carry-skip addersComparative statics (2,388 words) [view diff] exact match in snippet view article find links to article
is a vector of m exogenous parameters. Consider the unconstrained optimization problem x ∗ ( q ) = arg max p ( x ; q ) {\displaystyle x^{*}(q)=\arg \maxXaoS (610 words) [view diff] exact match in snippet view article find links to article
heuristics were tried, eventually the problem was treated as an optimization problem. The remaining rows and columns are colored in the same as the closestLinear matrix inequality (334 words) [view diff] exact match in snippet view article find links to article
there exists a vector y such that LMI(y) ≥ 0), or to solve a convex optimization problem with LMI constraints. Many optimization problems in control theorySolution set (572 words) [view diff] exact match in snippet view article find links to article
∅ {\displaystyle \emptyset } . The solution set of a constrained optimization problem is its feasible region. The truth set of the predicate P ( n ) :Space (disambiguation) (827 words) [view diff] exact match in snippet view article
Solution space, the set of all possible candidate solutions in an optimization problem Anatomic space or spatium, a space (cavity or gap) in anatomy SpectroscopicNetwork planning and design (1,127 words) [view diff] exact match in snippet view article find links to article
Core-and-pod Network Partition for Optimization Optimal network design - an optimization problem of constructing a network which minimizes the total travel cost.Hamiltonian (control theory) (3,975 words) [view diff] exact match in snippet view article
function and the state equations much like a Lagrangian in a static optimization problem, only that the multipliers λ ( t ) {\displaystyle \mathbf {\lambdaEnsemble averaging (machine learning) (912 words) [view diff] exact match in snippet view article
where α {\displaystyle \mathbf {\alpha } } is a set of weights. The optimization problem of finding alpha is readily solved through neural networks, henceTracking error (973 words) [view diff] exact match in snippet view article find links to article
vector of active portfolio weights relative to the benchmark. The optimization problem of maximizing the return, subject to tracking error and linear constraintsList of metaphor-based metaheuristics (4,798 words) [view diff] exact match in snippet view article find links to article
generating a set of random candidate solutions in the search space of the optimization problem. The generated random points are called the initial Countries. CountriesReproducing kernel Hilbert space (6,325 words) [view diff] exact match in snippet view article find links to article
minimization problem from an infinite dimensional to a finite dimensional optimization problem. For ease of understanding, we provide the framework for real-valuedStacker crane problem (625 words) [view diff] exact match in snippet view article find links to article
In combinatorial optimization, the stacker crane problem is an optimization problem closely related to the traveling salesperson problem. Its input consistsLinear (disambiguation) (276 words) [view diff] exact match in snippet view article
function Linear functional Linear map Linear programming, a type of optimization problem Linear system Linear system of equations Linear transformation ParticularlyEllipsoid method (3,704 words) [view diff] exact match in snippet view article find links to article
equations. Step 3: the decision problem can be reduced to a different optimization problem. Define the residual function f(z) := max[(Rz)1-r1, (Rz)2-r2, (Rz)3-r3Darts (6,602 words) [view diff] case mismatch in snippet view article find links to article
World Eiselt, H; Laporte, Gilbert (February 1991). "A Combinatorial Optimization Problem Arising in Dartboard Design". The Journal of the Operational ResearchKogge–Stone adder (4,539 words) [view diff] exact match in snippet view article find links to article
identical to the variable block size, multi level, carry-skip adder optimization problem, a solution of which is found in Thomas Lynch's thesis of 1996. LikeRSO (375 words) [view diff] no match in snippet view article find links to article
which received mail directly from rail transport Reactive Search Optimization, problem-solving methods with integrated online machine-learning capabilitiesMultidisciplinary design optimization (2,868 words) [view diff] exact match in snippet view article find links to article
changes the constrained optimization problem associated with reliability-based optimization into an unconstrained optimization problem, it often leads to computationallySideways Arithmetic from Wayside School (527 words) [view diff] exact match in snippet view article find links to article
on the scores each student got. Chapter 7 presents an algebraic optimization problem: lunch lady Miss Mush's meals become more and more disgusting theParametric search (3,699 words) [view diff] exact match in snippet view article find links to article
Megiddo (1983) for transforming a decision algorithm (does this optimization problem have a solution with quality better than some given threshold?) intoKnapsack problem (7,744 words) [view diff] exact match in snippet view article find links to article
"decision" problem, then one can find the maximum value for the optimization problem in polynomial time by applying this algorithm iteratively while increasingOptimus platform (907 words) [view diff] exact match in snippet view article find links to article
methods - searching for an optimum based on local information of the optimization problem (such as gradient information). Methods include * SQP (SequentialGraph cut optimization (4,236 words) [view diff] exact match in snippet view article find links to article
represented by a flow network, and in the general case the global optimization problem is NP-hard. There exist sufficient conditions to characterise familiesLexicographic optimization (1,552 words) [view diff] exact match in snippet view article find links to article
algorithms for solving lexicographic optimization problems. A leximin optimization problem with n objectives can be solved using a sequence of n single-objectiveNewton's method in optimization (1,864 words) [view diff] exact match in snippet view article find links to article
{\displaystyle f:\mathbb {R} \to \mathbb {R} } , we seek to solve the optimization problem min x ∈ R f ( x ) . {\displaystyle \min _{x\in \mathbb {R} }f(x)Math Kernel Library (998 words) [view diff] case mismatch in snippet view article find links to article
approximation applications. Partial Differential Equations Nonlinear Optimization Problem Solvers Once, oneMKL included Deep Neural Network functions, butMath Kernel Library (998 words) [view diff] case mismatch in snippet view article find links to article
approximation applications. Partial Differential Equations Nonlinear Optimization Problem Solvers Once, oneMKL included Deep Neural Network functions, butTruncated triakis tetrahedron (641 words) [view diff] exact match in snippet view article find links to article
number of faces (16 in this case) and a given surface area. For this optimization problem, the optimal geometric form for the polyhedron is one in which theAndrey Tikhonov (mathematician) (652 words) [view diff] exact match in snippet view article
Tikhonov, A. N. (1966). "On the stability of the functional optimization problem". USSR Computational Mathematics and Mathematical Physics. 6 (4):Mathematical program (104 words) [view diff] exact match in snippet view article find links to article
are the subject of numerical analysis A problem formulation of an optimization problem in terms of an objective function and constraint (mathematics) (inStephen Cook (1,540 words) [view diff] exact match in snippet view article find links to article
problem. Informally, the "P vs. NP" question asks whether every optimization problem whose answers can be efficiently verified for correctness/optimalitySpace launch (2,425 words) [view diff] exact match in snippet view article find links to article
solution is not required, impractical or impossible. If a trajectory optimization problem can be solved at a rate given by the inverse of the Lipschitz constantSemidefinite programming (4,698 words) [view diff] exact match in snippet view article find links to article
duality to hold for a SDP problem (and in general, for any convex optimization problem) is the Slater's condition. It is also possible to attain strongWolfe duality (450 words) [view diff] exact match in snippet view article find links to article
nonlinear in general, so the Wolfe dual problem may be a nonconvex optimization problem. In any case, weak duality holds. Lagrangian duality Fenchel dualityLagrange multipliers on Banach spaces (683 words) [view diff] exact match in snippet view article find links to article
all x between −1 and +1. One could also consider the constrained optimization problem, to minimize f among all those u ∈ X such that the mean value ofEmpirical risk minimization (1,618 words) [view diff] exact match in snippet view article find links to article
empirical risk minimization principle consists in solving the above optimization problem. Guarantees for the performance of empirical risk minimization dependNNLS (51 words) [view diff] exact match in snippet view article find links to article
NNLS may refer to Non-negative least squares, an optimization problem in mathematics New North London Synagogue, see Sternberg Centre This disambiguationSubgradient method (1,496 words) [view diff] exact match in snippet view article find links to article
is the projected subgradient method, which solves the constrained optimization problem minimize f ( x ) {\displaystyle f(x)\ } subject to x ∈ C {\displaystyleBundle adjustment (1,042 words) [view diff] exact match in snippet view article find links to article
of feature-based 3D reconstruction algorithms. It amounts to an optimization problem on the 3D structure and viewing parameters (i.e., camera pose andGeometry processing (4,136 words) [view diff] exact match in snippet view article find links to article
are minimized. In this manner, parameterization can be seen as an optimization problem. One of the major applications of mesh parameterization is textureRandom walk model of consumption (843 words) [view diff] exact match in snippet view article find links to article
conventional methods. This avoids the need to solve the consumer's optimization problem and is the most appealing element of using Euler equations to somePricing science (2,235 words) [view diff] exact match in snippet view article find links to article
intended to affect purchase events over some future time horizon. The optimization problem reflects the mathematical complexity required to reach feasible andDeep image prior (975 words) [view diff] exact match in snippet view article find links to article
into the equation for θ ∗ {\displaystyle \theta ^{*}} yields the optimization problem m i n θ | | f θ ( z ) − x 0 | | 2 {\displaystyle min_{\theta }||f_{\thetaDifferential game (1,038 words) [view diff] exact match in snippet view article find links to article
expectancy of the cost function. It was shown that the modified optimization problem can be reformulated as a discounted differential game over an infiniteEvoSuite (483 words) [view diff] case mismatch in snippet view article find links to article
Paolo (2015). "Reformulating Branch Coverage as a Many-Objective Optimization Problem". 2015 IEEE 8th International Conference on Software Testing, VerificationAIMMS (1,265 words) [view diff] exact match in snippet view article find links to article
user model libraries. AIMMS supports a wide range of mathematical optimization problem types: Linear programming Quadratic programming Nonlinear programmingProto-Uto-Aztecan language (1,577 words) [view diff] exact match in snippet view article find links to article
; Whiteley, P. M. (2014). "Historical linguistics as a sequence optimization problem: the evolution and biogeography of Uto-Aztecan languages" (PDF).Greedy algorithm (1,964 words) [view diff] exact match in snippet view article find links to article
linear independence from vector spaces to arbitrary sets. If an optimization problem has the structure of a matroid, then the appropriate greedy algorithmGradient boosting (4,259 words) [view diff] exact match in snippet view article find links to article
for an arbitrary loss function L is a computationally infeasible optimization problem in general. Therefore, we restrict our approach to a simplified versionContract curve (1,657 words) [view diff] exact match in snippet view article find links to article
2 {\displaystyle u^{2}(x_{1}^{2},x_{2}^{2})\geq u_{0}^{2}} This optimization problem states that the goods are to be allocated between the two peopleHigher-order singular value decomposition (4,394 words) [view diff] exact match in snippet view article find links to article
{R}}_{M})} is a nonlinear non-convex ℓ 2 {\displaystyle \ell _{2}} -optimization problem min A ¯ ∈ C I 1 × I 2 × ⋯ × I M 1 2 ‖ A − A ¯ ‖ F 2 s.t. r a n kJack Edmonds (1,543 words) [view diff] exact match in snippet view article find links to article
characterization of the polyhedron associated with a combinatorial optimization problem could lead, via the duality theory of linear programming, to theReduction (complexity) (1,661 words) [view diff] exact match in snippet view article
are often used to prove hardness of approximation results: if some optimization problem A is hard to approximate (under some complexity assumption) withinPseudo-polynomial time (877 words) [view diff] exact match in snippet view article find links to article
knapsack W {\displaystyle W} . The goal is to solve the following optimization problem; informally, what's the best way to fit the items into the knapsackSupervised learning (3,005 words) [view diff] exact match in snippet view article find links to article
denoted by C ( g ) {\displaystyle C(g)} . The supervised learning optimization problem is to find the function g {\displaystyle g} that minimizes J ( gInterpolation (3,039 words) [view diff] exact match in snippet view article find links to article
identify the subspace of functions where the solution to a constrained optimization problem resides. Consequently, TFC transforms constrained optimization problemsEnvy-freeness (1,689 words) [view diff] exact match in snippet view article find links to article
protocol. See Symmetric fair cake-cutting. Envy minimization is an optimization problem in which the objective is to minimize the amount of envy (which canNews style (2,302 words) [view diff] exact match in snippet view article find links to article
the unreadability of a long sentence. This makes writing a lead an optimization problem, in which the goal is to articulate the most encompassing and interestingCaterpillar tree (1,205 words) [view diff] exact match in snippet view article find links to article
Finding a spanning caterpillar in a graph is NP-complete. A related optimization problem is the Minimum Spanning Caterpillar Problem (MSCP), where a graphModern portfolio theory (7,879 words) [view diff] exact match in snippet view article find links to article
the standard deviation of the return, we are to solve a quadratic optimization problem: { E [ w T R ] = μ min σ 2 = V a r [ w T R ] ∑ i w i = 1 {\displaystyleGenetic algorithm (8,221 words) [view diff] exact match in snippet view article find links to article
(called individuals, creatures, organisms, or phenotypes) to an optimization problem is evolved toward better solutions. Each candidate solution has aEuclidean distance (3,288 words) [view diff] exact match in snippet view article find links to article
distance is equivalent to minimizing the Euclidean distance, so the optimization problem is equivalent in terms of either, but easier to solve using squaredGeorge Pólya Award (120 words) [view diff] case mismatch in snippet view article find links to article
Recipient Article 2023 William Q. Erickson Haste Makes Waste: An Optimization Problem 2023 Johnner Barrett Unlawful Calculations: A Look into Lie’s NotebookQuadratic assignment problem (773 words) [view diff] exact match in snippet view article find links to article
Combinatorial optimization problemAnt colony optimization algorithms (9,484 words) [view diff] exact match in snippet view article find links to article
searches for good solutions to a given optimization problem. To apply an ant colony algorithm, the optimization problem needs to be converted into the problemPoisson regression (2,750 words) [view diff] exact match in snippet view article find links to article
{\displaystyle e^{\theta 'x_{i}}} . Regularization can be added to this optimization problem by instead maximizing ∑ i = 1 m log ( p ( y i ; e θ ′ x i ) ) −Dynamic programming (9,166 words) [view diff] exact match in snippet view article find links to article
problems. Optimal substructure means that the solution to a given optimization problem can be obtained by the combination of optimal solutions to its sub-problemsStigler (209 words) [view diff] exact match in snippet view article find links to article
the measurement of inflation in the United States Stigler diet, an optimization problem regarding recommended dietary allowances (RDAs) Stigler's law ofAlgorithmic composition (2,120 words) [view diff] exact match in snippet view article find links to article
generating well defined styles, music can be seen as a combinatorial optimization problem, whereby the aim is to find the right combination of notes such thatFundamental theorem of linear programming (599 words) [view diff] exact match in snippet view article find links to article
occur everywhere on the line segment between them. Consider the optimization problem min c T x subject to x ∈ P {\displaystyle \min c^{T}x{\text{ subjectRestriction (mathematics) (1,906 words) [view diff] exact match in snippet view article
{\displaystyle R} ⩥ B . {\displaystyle B.} Constraint – Condition of an optimization problem which the solution must satisfy Deformation retract – ContinuousChance constrained programming (678 words) [view diff] exact match in snippet view article find links to article
simultaneously with a certain probability. A general chance constrained optimization problem can be formulated as follows: min f ( x , u , ξ ) s.t. g ( x , uRectangle packing (949 words) [view diff] exact match in snippet view article find links to article
Optimization problem in mathematicsSupply chain optimization (1,251 words) [view diff] exact match in snippet view article find links to article
other vendors have started to apply stochastic techniques to the optimization problem. They calculate the most desirable inventory level per article forManifold alignment (1,331 words) [view diff] exact match in snippet view article find links to article
_{X}\left(X_{i}\right)-\phi _{Y}\left(Y_{j}\right)\Vert ^{2}W_{i,j}} Solving this optimization problem is equivalent to solving a generalized eigenvalue problem using theDelone set (1,241 words) [view diff] exact match in snippet view article find links to article
optimal solution value (using a technique specific to the particular optimization problem being solved) If it is bigger, remove from the input the points whoseRotating calipers (1,296 words) [view diff] exact match in snippet view article find links to article
Quadrangulation Nice triangulation Art gallery problem Wedge placement optimization problem Union of two convex polygons Common tangents to two convex polygonsUC Irvine Institute of Transportation Studies (2,132 words) [view diff] exact match in snippet view article find links to article
formulated the vehicle reidentification problem as a lexicographic optimization problem and demonstrated robust performance 2000 – David Brownstone exploredFred W. Glover (1,287 words) [view diff] case mismatch in snippet view article find links to article
for Solving the QUBO Model", The Quadratic Unconstrained Binary Optimization Problem: Theory, Algorithms, and Applications, Cham: Springer InternationalCrystal structure prediction (1,454 words) [view diff] exact match in snippet view article find links to article
principles random search. The latter are capable of solving the global optimization problem with up to around a hundred degrees of freedom, while the approachEgalitarian rule (932 words) [view diff] exact match in snippet view article find links to article
maximizes the minimum utility, that is, it solves the following optimization problem: max x ∈ X min i ∈ I u i ( x ) . {\displaystyle \max _{x\in X}\minNonlinearity (disambiguation) (503 words) [view diff] exact match in snippet view article
the crystal. Nonlinear programming is the process of solving an optimization problem, where some of the parameters are nonlinear. Nonlinear regressionInverse kinematics (2,400 words) [view diff] exact match in snippet view article find links to article
instead optimize a solution given additional preferences (costs in an optimization problem). An analytic solution to an inverse kinematics problem is a closed-formMerit order (2,602 words) [view diff] exact match in snippet view article find links to article
These equations can now be combined to build the Lagrangian of the optimization problem: L = ∑ k = 1 n C k ( I k ) + π [ L ( I 1 , I 2 , … , I n − 1 ) −Routing (3,766 words) [view diff] exact match in snippet view article find links to article
the later over private WAN discusses modeling routing as a graph optimization problem by pushing all the queuing to the end-points. The authors also proposeBRST algorithm (739 words) [view diff] exact match in snippet view article find links to article
class of functions to include multimodal functions makes the global optimization problem unsolvable in general. In order to be solvable some smoothness conditionMatched filter (5,509 words) [view diff] exact match in snippet view article find links to article
optimization. After reversing the sign, we obtain the equivalent optimization problem j ∗ , μ ∗ = arg max j , μ [ 2 μ ∑ k s k h j − k − μ 2 ∑ k h jMinimum-cost flow problem (1,399 words) [view diff] exact match in snippet view article find links to article
Mathematical optimization problemPrecoding (3,895 words) [view diff] exact match in snippet view article find links to article
performance for all users. This can be viewed as a multi-objective optimization problem where each objective corresponds to maximization of the capacityDiscontinuity layout optimization (909 words) [view diff] exact match in snippet view article find links to article
formulation). In the latter case the objective of the mathematical optimization problem is to minimize the internal energy dissipated along discontinuitiesEvolutionary computation (2,970 words) [view diff] exact match in snippet view article find links to article
recombination and natural selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the cost functionPolyhedral combinatorics (2,304 words) [view diff] exact match in snippet view article find links to article
all perfect matchings in a complete bipartite graph, and a linear optimization problem on this polytope can be interpreted as a bipartite minimum weightGeneralized assignment problem (1,054 words) [view diff] exact match in snippet view article find links to article
Combinatorial optimization problemDynamic mode decomposition (3,792 words) [view diff] exact match in snippet view article find links to article
of the time series. Optimized DMD recasts the DMD procedure as an optimization problem where the identified linear operator has a fixed rank. FurthermoreList of circle topics (2,411 words) [view diff] exact match in snippet view article find links to article
Regiomontanus' angle maximization problem – Famous mathematical optimization problem Ring lemma Seven circles theorem – A chain of six circles tangentShephard's lemma (721 words) [view diff] exact match in snippet view article find links to article
{\displaystyle e(p_{1},p_{2},u)} is the value function of the constrained optimization problem characterized by the following Lagrangian: L = p 1 x 1 + p 2 x 2List of numerical analysis topics (8,327 words) [view diff] exact match in snippet view article find links to article
constraint set can be biconvex Nonlinear programming — the most general optimization problem in the usual framework Special cases of nonlinear programming: SeeHicksian demand function (1,050 words) [view diff] exact match in snippet view article find links to article
linear in the x i {\displaystyle x_{i}} , which gives a simpler optimization problem. However, Marshallian demand functions of the form x ( p , w ) {\displaystyleHyperparameter optimization (2,528 words) [view diff] exact match in snippet view article find links to article
performance of the machine learning algorithm. In this case, the optimization problem is said to have a low intrinsic dimensionality. Random Search isXGBoost (1,323 words) [view diff] exact match in snippet view article find links to article
{h}}_{m}(x_{i})}}\right\}_{i=1}^{N}} [clarification needed] by solving the optimization problem below: ϕ ^ m = arg min ϕ ∈ Φ ∑ i = 1 N 1 2 h ^ m ( x i ) [ ϕ (Mathematical model (4,768 words) [view diff] exact match in snippet view article find links to article
problem of rational behavior in this model then becomes a mathematical optimization problem, that is: max U ( x 1 , x 2 , … , x n ) {\displaystyle \max \,U(x_{1}Bipartite graph (4,086 words) [view diff] exact match in snippet view article find links to article
bipartite graphs appear naturally is in the (NP-complete) railway optimization problem, in which the input is a schedule of trains and their stops, andPSeven (667 words) [view diff] exact match in snippet view article find links to article
SmartSelection adaptively selects the optimization algorithm for a given optimization problem. pSeven provides tools to build and automatically run the workflowArtificial bee colony algorithm (1,293 words) [view diff] exact match in snippet view article find links to article
position of a food source represents a possible solution to the optimization problem and the nectar amount of a food source corresponds to the qualityVehicle rescheduling problem (439 words) [view diff] exact match in snippet view article find links to article
Transportation and logistics optimization problemSpanning tree (3,280 words) [view diff] exact match in snippet view article find links to article
it is not necessary to construct this graph in order to solve the optimization problem; the Euclidean minimum spanning tree problem, for instance, can beEarly stopping (1,836 words) [view diff] exact match in snippet view article find links to article
optimization methods. Each iteration updates an approximate solution to the optimization problem by taking a step in the direction of the negative of the gradientRanking (information retrieval) (2,129 words) [view diff] exact match in snippet view article
constructing the final item ranking results in a time-intensive optimization problem and substantial research effort has focused on speeding up the optimizationExpected shortfall (6,445 words) [view diff] exact match in snippet view article find links to article
in its standard form, is known to lead to a generally non-convex optimization problem. However, it is possible to transform the problem into a linear programCascading classifiers (1,263 words) [view diff] exact match in snippet view article find links to article
rate (sensitivity) below the desired rate, so this is a constrained optimization problem. To be precise, the total sensitivity will be the product of stageSystem on a chip (4,745 words) [view diff] exact match in snippet view article find links to article
optimizing any of these quantities may be a hard combinatorial optimization problem, and can indeed be NP-hard fairly easily. Therefore, sophisticatedAdversarial machine learning (7,938 words) [view diff] exact match in snippet view article find links to article
example x ^ {\textstyle {\hat {x}}} as solving the below constrained optimization problem: min x ^ ∈ [ 0 , 1 ] d L ( f ( x ^ ) , y ) , s.t. | | x ^ − x |Partial derivative (4,182 words) [view diff] exact match in snippet view article find links to article
r^{2}\right).} Partial derivatives appear in any calculus-based optimization problem with more than one choice variable. For example, in economics a firmGrey box model (1,500 words) [view diff] exact match in snippet view article find links to article
non-zero and assigning their values. The model completion becomes an optimization problem to determine the non-zero values in A that minimizes the error termsSelf-organized criticality (3,042 words) [view diff] exact match in snippet view article find links to article
random search for optimal solutions on graphs. An example of such an optimization problem is graph coloring. The SOC process apparently helps the optimizationCarl-Erik Särndal (500 words) [view diff] exact match in snippet view article find links to article
Deville and Särndal's calibration: revisiting a 25-year-old successful optimization problem, TEST, 28(4), 1033–1091, with discussions and rejoinder. "Carl-ErikUto-Aztecan languages (3,362 words) [view diff] case mismatch in snippet view article find links to article
Whiteley, Peter M. (2014). "Historical Linguistics as a Sequence Optimization Problem: the Evolution and Biogeography of Uto-Aztecan Languages". Cladistics:Multicriteria classification (1,812 words) [view diff] exact match in snippet view article find links to article
a decision model f {\displaystyle f} based on the solution of an optimization problem of the following general form: β ∗ = argmin β ∈ B L [ D ( X )Types of artificial neural networks (10,769 words) [view diff] exact match in snippet view article find links to article
introduced in 2011 by Deng and Yu. It formulates the learning as a convex optimization problem with a closed-form solution, emphasizing the mechanism's similaritySocial golfer problem (1,043 words) [view diff] exact match in snippet view article find links to article
constraints). Second is the choice of variables. The SGP can be seen as an optimization problem to maximize the number of weeks in the schedule. Hence, incorrectlyNeutral network (evolution) (2,832 words) [view diff] exact match in snippet view article
the folding problem over a two letter alphabet as a planar graph optimization problem, where the quantity to be maximized is the number of matchings inSmoothing spline (2,314 words) [view diff] exact match in snippet view article find links to article
approximating manifold and uses the coarse discretization of the optimization problem. Source code for spline smoothing can be found in the examples fromOverhead power line (4,784 words) [view diff] exact match in snippet view article find links to article
the line construction cost due to the size of the conductors. The optimization problem is made more complex by additional factors such as varying annualMCACEA (819 words) [view diff] exact match in snippet view article find links to article
and cooperation constraints and objective indexes. Each EA is an optimization problem that runs in parallel and that exchanges some information with theDifferential calculus (4,452 words) [view diff] exact match in snippet view article find links to article
then the test is considered to be inconclusive. One example of an optimization problem is: Find the shortest curve between two points on a surface, assumingMicroeconomics (5,949 words) [view diff] exact match in snippet view article find links to article
choices as well. The utility maximization problem is a constrained optimization problem in which an individual seeks to maximize utility subject to a budgetJames Tobin (2,339 words) [view diff] exact match in snippet view article find links to article
from these assumptions. Instead of worrying about the investor's optimization problem in potentially millions of possible states of the world, one needSociété française de Recherche Opérationnelle et Aide à la Décision (698 words) [view diff] exact match in snippet view article find links to article
International research teams compete to solve at best a real-world optimization problem provided by a business company. Latest problems were proposed byFeedback vertex set (1,805 words) [view diff] exact match in snippet view article find links to article
algorithm based on the matroid parity problem. The corresponding NP optimization problem of finding the size of a minimum feedback vertex set can be solvedRobust principal component analysis (1,765 words) [view diff] exact match in snippet view article find links to article
the rank constraint r a n k ( L ) {\displaystyle rank(L)} in the optimization problem to the nuclear norm ‖ L ‖ ∗ {\displaystyle \|L\|_{*}} and the sparsityComputational sustainability (4,400 words) [view diff] exact match in snippet view article find links to article
connect core conservation areas through corridors results in an optimization problem. This is where technology can help, not only in optimizing corridorsBio-inspired computing (2,979 words) [view diff] case mismatch in snippet view article find links to article
Pintea, 2014, Advances in Bio-inspired Computing for Combinatorial Optimization Problem, Springer ISBN 978-3-642-40178-7 "PSA: A novel optimization algorithmInteger programming (4,226 words) [view diff] exact match in snippet view article find links to article
Mathematical optimization problem restricted to integersCoefficient of determination (6,215 words) [view diff] exact match in snippet view article find links to article
the predicted values and the R2 unchanged. The only way that the optimization problem will give a non-zero coefficient is if doing so improves the R2.Proof of work (4,181 words) [view diff] exact match in snippet view article find links to article
achieves consensus while simultaneously providing a decentralized optimization problem solver. The protocol is built around Doubly Parallel Local SearchCoreset (562 words) [view diff] exact match in snippet view article find links to article
>0} is some user defined approximation parameter. Formally, for an optimization problem with some cost function COST ( P ) {\displaystyle \left(P\right)}One-class classification (2,323 words) [view diff] exact match in snippet view article find links to article
{\displaystyle \alpha _{i}} 's are the solution to the following optimization problem: max α ∑ i = 1 n α i κ ( x i , x i ) − ∑ i , j = 1 n α i α j κ (Adjoint functors (10,260 words) [view diff] exact match in snippet view article find links to article
meaningful in that finding a universal morphism is like solving an optimization problem. Using hom-sets, an adjunction between two categories C {\displaystyleMultidimensional scaling (3,244 words) [view diff] exact match in snippet view article find links to article
vectors x i {\displaystyle x_{i}} . Usually, MDS is formulated as an optimization problem, where ( x 1 , … , x M ) {\displaystyle (x_{1},\ldots ,x_{M})} isPrinciple of maximum entropy (4,332 words) [view diff] exact match in snippet view article find links to article
subject to the constraints of the information. This constrained optimization problem is typically solved using the method of Lagrange multipliers. EntropyDocking (molecular) (4,015 words) [view diff] exact match in snippet view article
be thought of as a "key". Molecular docking may be defined as an optimization problem, which would describe the "best-fit" orientation of a ligand thatLeast squares (5,243 words) [view diff] exact match in snippet view article find links to article
zero-mean Laplace prior distribution on the parameter vector. The optimization problem may be solved using quadratic programming or more general convexRoy's identity (812 words) [view diff] exact match in snippet view article find links to article
{\displaystyle v(p_{1},p_{2},w)} is the value function of the constrained optimization problem characterized by the following Lagrangian: L = u ( x 1 , x 2 ) +Coal blending (562 words) [view diff] exact match in snippet view article find links to article
and profitability. Blend optimization is a nonlinear combinatorial optimization problem where the objective is typically to maximize revenue, Net PresentBiogeography-based optimization (3,458 words) [view diff] exact match in snippet view article find links to article
function, and each island represents a candidate solution to a function optimization problem. Islands with a high HSI not only have a high emigration rate, butTone mapping (3,444 words) [view diff] exact match in snippet view article find links to article
the application, one possible solution is to treat the issue as an optimization problem[11]. For this method, models for the Human Visual System (HVS) andBregman method (1,592 words) [view diff] exact match in snippet view article find links to article
u|_{1}+|Ku-f|^{2}} . We start by rewriting it as the constrained optimization problem min u : d = Φ ( u ) | d | 1 + H ( u ) {\displaystyle \min _{u:d=\PhiColumn generation (1,360 words) [view diff] exact match in snippet view article find links to article
variable having the minimum reduced cost. This can be done using an optimization problem called the pricing subproblem which strongly depends on the structureMonotone comparative statics (8,477 words) [view diff] exact match in snippet view article find links to article
endogenous variable. This guarantees that the set of solutions to the optimization problem is increasing with respect to the exogenous parameter. Let X ⊆ RNancy Lou Schwartz (811 words) [view diff] exact match in snippet view article find links to article
developed a linear discrete programming problem of this dynamic optimization problem, and was able to solve it for moderate problems." Her later researchUniversal property (4,031 words) [view diff] exact match in snippet view article find links to article
than adjoint functor pairs: a universal construction is like an optimization problem; it gives rise to an adjoint pair if and only if this problem hasGraph partition (2,979 words) [view diff] exact match in snippet view article find links to article
communities. Some methods express graph partitioning as a multi-criteria optimization problem which can be solved using local methods expressed in a game theoreticStack (abstract data type) (4,727 words) [view diff] exact match in snippet view article
searching through spaces that represent potential solutions to an optimization problem. Branch and bound is a technique for performing such backtrackingRandom sample consensus (4,146 words) [view diff] exact match in snippet view article find links to article
re-estimation of inliers and the multi-model fitting being formulated as an optimization problem with a global energy function describing the quality of the overallEgalitarian cake-cutting (928 words) [view diff] exact match in snippet view article find links to article
JSTOR 2311357. Dall'Aglio, Marco (2001-05-01). "The Dubins–Spanier optimization problem in fair division theory". Journal of Computational and Applied MathematicsOptimal radix choice (1,812 words) [view diff] exact match in snippet view article find links to article
\over \ln(10)}\approx 4.34294\,.} The closely related continuous optimization problem of finding the maximum of the function f ( x ) = x 1 / x , {\displaystyleJohn Hopfield (3,180 words) [view diff] exact match in snippet view article find links to article
using a Hopfield network with continuous activation function. The optimization problem was encoded in the interaction parameters (weights) of the networkData envelopment analysis (2,483 words) [view diff] exact match in snippet view article find links to article
analysed and quantified for every evaluated unit using the dual of the optimization problem identifies which DMU is evaluating itself against which other DMUsMultiple kernel learning (2,856 words) [view diff] exact match in snippet view article find links to article
combination of the norms (i.e. elastic net regularization). This optimization problem can then be solved by standard optimization methods. AdaptationsLotka–Volterra equations (4,494 words) [view diff] exact match in snippet view article find links to article
The largest value of the constant K is obtained by solving the optimization problem y α e − β y x γ e − δ x = y α x γ e δ x + β y ⟶ max x , y > 0 . {\displaystyleSocial cognitive optimization (1,007 words) [view diff] exact match in snippet view article find links to article
Apache OpenOffice. Let f ( x ) {\displaystyle f(x)} be a global optimization problem, where x {\displaystyle x} is a state in the problem space S {\displaystyleBernhard Schölkopf (2,007 words) [view diff] exact match in snippet view article find links to article
expansions on the training data, thus reducing an infinite dimensional optimization problem to a finite dimensional one. He co-developed kernel embeddings ofStep detection (1,943 words) [view diff] exact match in snippet view article find links to article
step detection is the Potts model. It is given by the non-convex optimization problem u ∗ = arg min u ∈ R N γ ‖ ∇ u ‖ 0 + ‖ u − x ‖ p p {\displaystyleBayesian network (6,630 words) [view diff] exact match in snippet view article find links to article
particularly fast method for exact BN learning is to cast the problem as an optimization problem, and solve it using integer programming. Acyclicity constraints areKriging (6,063 words) [view diff] exact match in snippet view article find links to article
_{x_{i}x_{0}}+\operatorname {Var} _{x_{0}}\right).} Solving this optimization problem (see Lagrange multipliers) results in the kriging system: [ W ^ μPierre-Louis Lions (2,101 words) [view diff] exact match in snippet view article find links to article
solutions of the equation as rescalings of minima of a constrained optimization problem, based upon a modified Dirichlet energy. Making use of the SchwarzMarket equilibrium computation (4,358 words) [view diff] exact match in snippet view article find links to article
goods, when the resources are bads the CE does not solve any convex optimization problem even with linear utilities. CE allocations correspond to local minimaBehavioral operations management (1,926 words) [view diff] exact match in snippet view article find links to article
and suffers loss of goodwill. The assignment problem is a complex optimization problem. The problem involves number of agents and a number of tasks. TheMittag-Leffler function (1,790 words) [view diff] exact match in snippet view article find links to article
relaxation behavior to sufficient accuracy. This results in a difficult optimization problem in order to identify the large number of material parameters requiredEfstratios N. Pistikopoulos (860 words) [view diff] exact match in snippet view article find links to article
control, which allows for the explicit solution of the underlying optimization problem, hence removing the necessity of online optimization The use andLinear–quadratic regulator (2,284 words) [view diff] exact match in snippet view article find links to article
\leq \mathbf {e} .} The finite horizon version of this is a convex optimization problem, and so the problem is often solved repeatedly with a receding horizonPerceptron (6,297 words) [view diff] exact match in snippet view article find links to article
1989)). AdaTron uses the fact that the corresponding quadratic optimization problem is convex. The perceptron of optimal stability, together with theQuantization (signal processing) (6,284 words) [view diff] exact match in snippet view article
coding that is better than an FLC in the rate–distortion sense), the optimization problem reduces to minimization of distortion D {\displaystyle D} alone.Backward induction (3,617 words) [view diff] exact match in snippet view article find links to article
a job for a long time, it is worth picking carefully. A dynamic optimization problem of this kind is called an optimal stopping problem because the issuePeg solitaire (3,069 words) [view diff] exact match in snippet view article find links to article
1996 paper formulated a peg solitaire problem as a combinatorial optimization problem and discussed the properties of the feasible region called 'a solitaireK-means clustering (7,770 words) [view diff] exact match in snippet view article find links to article
531 features. As expected, due to the NP-hardness of the subjacent optimization problem, the computational time of optimal algorithms for k-means quicklySpace mapping (2,488 words) [view diff] exact match in snippet view article find links to article
Scharrer and S. Volkwein, "Space mapping techniques for a structural optimization problem governed by the p-Laplace equation" Archived 2022-01-30 at the WaybackMechanism design (5,063 words) [view diff] exact match in snippet view article find links to article
derived from the first- and second-order conditions of the agent's optimization problem assuming truth-telling. Its meaning can be understood in two piecesSolow–Swan model (4,945 words) [view diff] exact match in snippet view article find links to article
decision to consume and save. This is done by adding a household optimization problem to the Solow model. see also Giri R (undated, before 2022): LectureAlgorithmic probability (2,734 words) [view diff] exact match in snippet view article find links to article
rewards. This approach transforms sequential decision-making into an optimization problem. However, the general formulation of AIXI is incomputable, makingVehicle routing problem (3,114 words) [view diff] exact match in snippet view article find links to article
Optimization problem3 Quarks Daily (1,132 words) [view diff] case mismatch in snippet view article find links to article
and it messed with our brains 2 Cosma Shalizi In Soviet Union, Optimization Problem Solves You 3 Holly Dunsworth Forget bipedalism. What about babyismJModelica.org (781 words) [view diff] case mismatch in snippet view article find links to article
JModelica.org—Languages and Tools for Solving Large-Scale Dynamic Optimization Problem" Archived 2018-10-17 at the Wayback Machine. Computers and ChemicalAutoencoder (6,540 words) [view diff] exact match in snippet view article find links to article
{\displaystyle (\mu _{\text{ref}},d)} , the problem of training a DAE is the optimization problem: min θ , ϕ L ( θ , ϕ ) = E x ∼ μ X , T ∼ μ T [ d ( x , ( D θ ∘ ELearning to rank (4,442 words) [view diff] exact match in snippet view article find links to article
algorithms. Often a learning-to-rank problem is reformulated as an optimization problem with respect to one of these metrics. Examples of ranking qualityPROPT (1,743 words) [view diff] exact match in snippet view article find links to article
the polynomials used to approximate the solution to the Trajectory optimization problem. Source transformation to turn user-supplied expressions into MATLABMinimum mean square error (9,310 words) [view diff] exact match in snippet view article find links to article
among all estimators of such form. That is, it solves the following optimization problem: min W , b MSE s.t. x ^ = W y + b . {\displaystyle \min _{W,b}\operatornameLinear programming (6,690 words) [view diff] exact match in snippet view article find links to article
calls an appropriate solver such as CPLEX or similar, to solve the optimization problem at hand. Academic licenses are free of charge. ALGLIB A commercialNonholonomic system (4,039 words) [view diff] exact match in snippet view article find links to article
Type of optimization problemSuperiorization (1,027 words) [view diff] exact match in snippet view article find links to article
This projection onto the constraints set is in itself a non-trivial optimization problem and the need to solve it in every iteration hinders projected gradientRobot calibration (1,230 words) [view diff] exact match in snippet view article find links to article
Objective function and optimization problemWeak ordering (4,360 words) [view diff] exact match in snippet view article find links to article
utility theory. In linear programming and other types of combinatorial optimization problem, the prioritization of solutions or of bases is often given by aMonte Carlo method (10,691 words) [view diff] exact match in snippet view article find links to article
The traveling salesman problem is what is called a conventional optimization problem. That is, all the facts (distances between each destination point)Probabilistic soft logic (2,120 words) [view diff] exact match in snippet view article find links to article
allows for the underlying inference to be solved quickly as a convex optimization problem. This is useful in problems such as collective classification, linkOrganizational theory (6,422 words) [view diff] exact match in snippet view article find links to article
The contingency theory views organization design as "a constrained optimization problem," meaning that an organization must try to maximize performance byRepresenter theorem (2,800 words) [view diff] exact match in snippet view article find links to article
allows us to transform a complicated (possibly infinite dimensional) optimization problem into a simple linear system that can be solved numerically. AssumeCompressed sensing (5,874 words) [view diff] exact match in snippet view article find links to article
process known as forward–backward splitting algorithm is used. The optimization problem is split into two sub-problems which are then solved with the conjugatePhysics-informed neural networks (4,835 words) [view diff] exact match in snippet view article find links to article
Dirichlet and Neumann boundary conditions which pose a multi-objective optimization problem which requires manually weighing the loss terms to be able to optimizeSearch theory (2,668 words) [view diff] exact match in snippet view article find links to article
sample size can be calculated using a straightforward one-variable optimization problem and expressed in closed form. It is assumed that a non-degenerateRandom coordinate descent (1,048 words) [view diff] exact match in snippet view article find links to article
{\displaystyle \|\cdot \|_{2}} is the standard Euclidean norm. Consider the optimization problem min x ∈ R n f ( x ) , {\displaystyle \min _{x\in R^{n}}f(x),} whereFacility location (competitive game) (766 words) [view diff] exact match in snippet view article
general class of games, called utility games. Facility location (optimization problem) Facility location (cooperative game) Vetta, A. (2002). "Nash equilibriaYottaa (955 words) [view diff] exact match in snippet view article find links to article
security. Yottaa deals with the problem of website speed as a software optimization problem, instead of dealing with it as a problem of bits' movement betweenEdward William Barankin (417 words) [view diff] exact match in snippet view article find links to article
Computing the Barankin bound, by solving an unconstrained quadratic optimization problem. In Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997Game theory (15,389 words) [view diff] exact match in snippet view article find links to article
expectation of the cost function. It was shown that the modified optimization problem can be reformulated as a discounted differential game over an infiniteViktor Maslov (mathematician) (1,615 words) [view diff] exact match in snippet view article
tropical mathematics, in which the operations of the conditional optimization problem were considered. In the early 1970s, he met Lê Vũ Anh, the daughterLexicographic max-min optimization (4,059 words) [view diff] exact match in snippet view article find links to article
f_{[1]}(x)\leq f_{[2]}(x)\leq \cdots \leq f_{[n]}(x)} . Then, the lexmaxmin optimization problem can be written as the following lexicographic maximization problem:Markowitz model (2,097 words) [view diff] exact match in snippet view article find links to article
expected returns are uncertain, and when we make this assumption, the optimization problem yields solutions different from those of the Markowitz Model. RustagiRemez algorithm (2,697 words) [view diff] exact match in snippet view article find links to article
theory of polynomial interpolation. For the initialization of the optimization problem for function f by the Lagrange interpolant Ln(f), it can be shownRegret (decision theory) (2,987 words) [view diff] exact match in snippet view article
achieved in the latter case. In this case, the solution of a convex optimization problem gives the optimal, minimax regret-minimizing linear estimator, whichCost curve (3,548 words) [view diff] exact match in snippet view article find links to article
cost of producing a particular output level is the result of an optimization problem: The sum of expenditures on labor (the wage rate times the chosenContent similarity detection (4,752 words) [view diff] exact match in snippet view article find links to article
hashing – Algorithmic technique using hashing Nearest neighbor search – Optimization problem in computer science Paraphrase detection – Automatic generation orLattice problem (3,660 words) [view diff] exact match in snippet view article find links to article
Optimization problem in computer scienceLadyzhenskaya–Babuška–Brezzi condition (1,382 words) [view diff] exact match in snippet view article find links to article
conditions necessary to guarantee that a minimizer of the constrained optimization problem also satisfies the first-order necessary conditions represented byPeter Capak (1,475 words) [view diff] case mismatch in snippet view article find links to article
Steinhardt, Charles L. (2016). "Exploring Photometric Redshifts as an Optimization Problem: An Ensemble MCMC and Simulated Annealing-Driven Template-FittingMembrane bioreactor (5,527 words) [view diff] exact match in snippet view article find links to article
the aeration system less effective; the classical solution to this optimization problem is to ensure a concentration of mixed liquor suspended solids whichBasis set (chemistry) (4,970 words) [view diff] exact match in snippet view article
the dimension of the search space or even avoiding the exponent optimization problem. In order to properly describe electronic delocalized states, a previouslyVickrey–Clarke–Groves mechanism (2,484 words) [view diff] exact match in snippet view article find links to article
: 270–273, chap.11 Sometimes there are approximation algorithms to the optimization problem, but, using such an approximation might make the mechanism non-truthfulRecurrent neural network (10,416 words) [view diff] exact match in snippet view article find links to article
weights in a neural network can be modeled as a non-linear global optimization problem. A target function can be formed to evaluate the fitness or errorEnergy minimization (3,131 words) [view diff] exact match in snippet view article find links to article
the positions, E(r). Geometry optimization is then a mathematical optimization problem, in which it is desired to find the value of r for which E(r) isDubins–Spanier theorems (2,047 words) [view diff] exact match in snippet view article find links to article
2140/pjm.1951.1.59. Dall'Aglio, Marco (2001). "The Dubins–Spanier optimization problem in fair division theory". Journal of Computational and Applied MathematicsTransportation theory (mathematics) (4,442 words) [view diff] exact match in snippet view article
_{j=1}^{J}\psi _{j}\nu _{j}\right\}} which is a finite-dimensional convex optimization problem that can be solved by standard techniques, such as gradient descentPositive-definite kernel (4,346 words) [view diff] exact match in snippet view article find links to article
minimization problem from an infinite dimensional to a finite dimensional optimization problem. There are several different ways in which kernels arise in probabilityAuto-WEKA (541 words) [view diff] exact match in snippet view article find links to article
extends both the Algorithm selection problem and the Hyperparameter optimization problem, by searching for the best algorithm and also its hyperparametersYield (Circuit) (4,696 words) [view diff] exact match in snippet view article
candidates. Formally, yield optimization can be posed as the following optimization problem: x ∗ = arg max x ∈ X g ( x ) {\displaystyle \mathbf {x} ^{*}=\argKelly criterion (5,663 words) [view diff] exact match in snippet view article find links to article
{(r_{k}-r)(r_{j}-r)}{(1+r)^{2}}}\right].} Thus we reduce the optimization problem to quadratic programming and the unconstrained solution is u ⋆ →Facility location (cooperative game) (464 words) [view diff] exact match in snippet view article
conditions for a game to have nonempty core. Facility location (optimization problem) Facility location (competitive game) Cost-sharing mechanism KamalKernel embedding of distributions (9,770 words) [view diff] exact match in snippet view article find links to article
{\displaystyle P_{X}^{*}} . This can be done by solving the following optimization problem max P X H ( P X ) {\displaystyle \max _{P_{X}}H(P_{X})} subject toNonlinear dimensionality reduction (6,119 words) [view diff] exact match in snippet view article find links to article
Usually, the principal manifold is defined as a solution to an optimization problem. The objective function includes a quality of data approximationLow-rank approximation (3,884 words) [view diff] exact match in snippet view article find links to article
{\displaystyle P} and L {\displaystyle L} is a difficult biconvex optimization problem, minimization over one of the variables alone is a linear least squaresGraver basis (2,144 words) [view diff] exact match in snippet view article find links to article
quadratic and higher degree polynomial functions. Consider the following optimization problem over three-dimensional tables with prescribed line sums, min { wCenter-of-gravity method (576 words) [view diff] exact match in snippet view article find links to article
is very computationally expensive. Our goal is to solve a convex optimization problem of the form: minimize f(x) s.t. x in G, where f is a convex functionAlpha Profiling (1,148 words) [view diff] exact match in snippet view article find links to article
Chriss provided closed-form solutions of the basic risk-adjusted cost optimization problem with a linear impact model and trivial alpha profile. More recentMatrix completion (6,402 words) [view diff] exact match in snippet view article find links to article
To recover the incomplete matrix, we try to solve the following optimization problem: min X ‖ X ‖ ∗ subject to ‖ P Ω ( X − Y ) ‖ F ≤ δ {\displaystyleGittins index (2,910 words) [view diff] exact match in snippet view article find links to article
it corresponds to the class of linear-fractional markov reward optimization problem. However, a detrimental aspect of such ratio optimizations is thatAutomatic summarization (6,821 words) [view diff] exact match in snippet view article find links to article
naturally models coverage and diversity. Another example of a submodular optimization problem is using a determinantal point process to model diversity. SimilarlyMind the gap (3,980 words) [view diff] exact match in snippet view article find links to article
September 2012. The phrase has been used to name a combinatorial optimization problem. The original Oswald Laurence "Mind the gap" announcement and theMulticlass classification (4,571 words) [view diff] exact match in snippet view article find links to article
extensions, additional parameters and constraints are added to the optimization problem to handle the separation of the different classes. Multi expressionPower system simulation (2,855 words) [view diff] exact match in snippet view article find links to article
plan for generation, transmission, and distribution facilities. The optimization problem will typically consider the long term investment cash flow and aEfficient envy-free division (2,406 words) [view diff] exact match in snippet view article find links to article
logarithms of utilities. Finding such an allocation is a convex optimization problem: maximize ∑ i = 1 n log ( u i ( X i ) ) such that (Smart order routing (1,731 words) [view diff] exact match in snippet view article find links to article
20% by 2007". Smart order routing may be formulated in terms of an optimization problem which achieves a tradeoff between speed and cost of execution. SOROversampling and undersampling in data analysis (2,718 words) [view diff] exact match in snippet view article find links to article
between precision and recall is, however, inherently a multi-objective optimization problem. It is well known that these problems typically have multiple incomparableExact diagonalization (1,281 words) [view diff] exact match in snippet view article find links to article
because numerical analytic continuation is an ill-posed and difficult optimization problem. Can be used as an impurity solver for Dynamical mean-field theory