Package dev.nm.solver

Interface Optimizer<P,S>

Type Parameters:
P - the optimization problem type
S - 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:

  • Method Summary

    Modifier and Type
    Method
    Description
    S
    solve(P problem)
    Solve an optimization problem, e.g., OptimProblem.

  • Method Details

    • 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