Discrete optimization
Discrete optimization is a branch of optimization in applied mathematics and computer science.As opposed to 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 a discrete set of values, such as the integers.[1] Three notable branches of discrete optimization are:[2] These branches are all closely intertwined however, since many combinatorial optimization problems can be modeled as integer programs (e.g. shortest path) or constraint programs, any constraint program can be formulated as an integer program and vice versa, and constraint and integer programs can often be given a combinatorial interpretation.
optimizationapplied mathematicscomputer sciencecontinuous optimizationvariablesdiscrete variablesdiscreteintegerscombinatorial optimizationgraphsmatroidsinteger programmingconstraint programmingDiophantine equationAlgorithmsmethodsheuristicsUnconstrained nonlinearFunctionsGolden-section searchPowell's methodLine searchNelder–Mead methodSuccessive parabolic interpolationGradientsConvergenceTrust regionWolfe conditionsQuasi–NewtonBerndt–Hall–Hall–HausmanBroyden–Fletcher–Goldfarb–ShannoL-BFGSDavidon–Fletcher–PowellSymmetric rank-one (SR1)Other methodsConjugate gradientGauss–NewtonGradientMirrorLevenberg–MarquardtPowell's dog leg methodTruncated NewtonHessiansNewton's methodConstrained nonlinearBarrier methodsPenalty methodsAugmented Lagrangian methodsSequential quadratic programmingSuccessive linear programmingConvex optimizationConvex minimizationCutting-plane methodReduced gradient (Frank–Wolfe)Subgradient methodLinearquadraticAffine scalingEllipsoid algorithm of KhachiyanProjective algorithm of KarmarkarBasis-exchangeSimplex algorithm of DantzigRevised simplex algorithmCriss-cross algorithmPrincipal pivoting algorithm of LemkeActive-set methodCombinatorialApproximation algorithmDynamic programmingGreedy algorithmBranch and boundGraph algorithmsMinimum spanning treeBorůvkaKruskalShortest pathBellman–FordDijkstraFloyd–WarshallNetwork flowsEdmonds–KarpFord–FulkersonPush–relabel maximum flowMetaheuristicsEvolutionary algorithmHill climbingLocal searchParallel metaheuristicsSimulated annealingSpiral optimization algorithmTabu searchSoftware