Add Basin Hopping Global Optimization Algorithm with New Least Squares Interface #1122
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- Introduce a unified interface for nonlinear least squares minimization. - Define two overloads of the FindMinimum method (one accepting a Vector<double> and one accepting a double[]). - Update LevenbergMarquardtMinimizer and TrustRegionMinimizerBase classes to implement ILeastSquaresMinimizer.
diluculo
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This was referenced Apr 3, 2025
- Implements the basin-hopping algorithm for global optimization based on the approach in SciPy. - Basin-hopping combines Monte Carlo random displacement with local minimization to efficiently find global minima in complex energy landscapes. - Supports both ILeastSquaresMinimizer and IUnconstrainedMinimizer interfaces, allowing for flexibility in choosing local minimization algorithms. - Depends on PR mathnet#1119 (ILeastSquaresMinimizer interface) which must be merged first.
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Fixed unintended text changes in Builder.cs file. |
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This PR adds both a new unified interface for least squares minimization and a Basin Hopping implementation for global optimization.
ILeastSquaresMinimizer Interface
First, this PR introduces a standardized interface for nonlinear least squares minimization algorithms:
ILeastSquaresMinimizerinterfaceFindMinimummethod:Vector<double>parameterdouble[]parameterLevenbergMarquardtMinimizerandTrustRegionMinimizerBaseto implement this interfaceBasin Hopping Implementation
Building on this interface, the PR adds a Basin Hopping algorithm for global optimization. Basin hopping is a stochastic algorithm that efficiently finds global minima in complex energy landscapes by combining random Monte Carlo steps with local optimization (Wikipedia).
Key features:
ILeastSquaresMinimizerandIUnconstrainedMinimizerinterfacesLevenbergMarquardtMinimizer,TrustRegionDogLegMinimizer,TrustRegionNewtonCGMinimizerBfgsMinimizer,LimitedMemoryBfgsMinimizer,ConjugateGradientMinimizer,NewtonMinimizerNote: This PR supersedes PR #1119 which only contained the interface portion of these changes.