Package dev.nm.solver
Interface Optimizer<P,S>
- 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:
-
Method Summary
-
Method Details
-
solve
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
-