Package dev.nm.solver
Interface Optimizer<P,S>
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- Type Parameters:
P- the optimization problem typeS- the optimization solution type
- All Known Subinterfaces:
BoxMinimizer<P,S>,CetaMaximizer,ConstrainedMinimizer<P,S>,IPMinimizer<T,S>,IterativeC2Minimizer,IterativeMinimizer<P>,LineSearch,LPSimplexSolver<P>,LPSolver<P,S>,Maxmizer<P,S>,Minimizer<P,S>,MinMaxMinimizer<T>,MultivariateMinimizer<P,S>,QPMinimizer,UnivariateMinimizer
- All Known Implementing Classes:
BFGSMinimizer,BoxGeneralizedSimulatedAnnealingMinimizer,BracketSearchMinimizer,BrentCetaMaximizer,BrentMinimizer,BruteForceIPMinimizer,BruteForceMinimizer,CombinedCetaMaximizer,ConjugateGradientMinimizer,CSDPMinimizer,DEOptim,DFPMinimizer,DoubleBruteForceMinimizer,FerrisMangasarianWrightPhase2,FibonaccMinimizer,FirstOrderMinimizer,FletcherLineSearch,FletcherReevesMinimizer,GaussNewtonMinimizer.MySteepestDescent,GeneralizedSimulatedAnnealingMinimizer,GlobalSearchByLocalMinimizer,GoldenMinimizer,GomoryMixedCutMinimizer,GomoryPureCutMinimizer,GridSearchCetaMaximizer,GridSearchMinimizer,HomogeneousPathFollowingMinimizer,HuangMinimizer,ILPBranchAndBoundMinimizer,IterativeC2Maximizer,LeastPth,LPCanonicalSolver,LPRevisedSimplexSolver,LPTwoPhaseSolver,McCormickMinimizer,NelderMeadMinimizer,NewtonRaphsonMinimizer,PearsonMinimizer,PenaltyMethodMinimizer,PowellMinimizer,PrimalDualInteriorPointMinimizer,PrimalDualInteriorPointMinimizer1,PrimalDualPathFollowingMinimizer,QPbySOCPMinimizer,QPbySOCPMinimizer1,QPDualActiveSetMinimizer,QPPrimalActiveSetMinimizer,QuasiNewtonMinimizer,RankOneMinimizer,SimpleGridMinimizer,SimplexCuttingPlaneMinimizer,SimulatedAnnealingMinimizer,SQPActiveSetMinimizer,SQPActiveSetOnlyEqualityConstraint1Minimizer,SQPActiveSetOnlyEqualityConstraint2Minimizer,SQPActiveSetOnlyInequalityConstraintMinimizer,SteepestDescentMinimizer,SubProblemMinimizer,ZangwillMinimizer
public interface Optimizer<P,S>Optimization, or mathematical programming, refers to choosing the best element from some set of available alternatives. In the simplest case, this means solving problems in which one seeks to minimize (or maximize) a real function by systematically choosing the values of real or integer variables from within an allowed set. The generalization of optimization theory and techniques to other formulations comprises a large area of applied mathematics. More generally, it means finding "best available" values of some objective function given a defined domain, including a variety of different types of objective functions and different types of domains. This interface defines the input (the optimization problem) and output (the optimization solution) of an optimization algorithm.- See Also:
- Wikipedia: Mathematical optimization
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Method Summary
All Methods Instance Methods Abstract Methods Modifier and Type Method Description Ssolve(P problem)Solve an optimization problem, e.g.,OptimProblem.
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Method Detail
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solve
S solve(P problem) throws Exception
Solve an optimization problem, e.g.,OptimProblem.- Parameters:
problem- an optimization problem- Returns:
- a solution to the optimization problem
- Throws:
Exception- when there is an error solving the problem
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