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https://gitlab.com/libeigen/eigen.git
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Put the Status outside of the class, it really does not depend on the
FunctorType or Scalar template parameters.
This commit is contained in:
Thomas Capricelli
parent
27cf1b3a97
commit
98a584ceb8
@ -28,6 +28,19 @@
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#ifndef EIGEN_HYBRIDNONLINEARSOLVER_H
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#define EIGEN_HYBRIDNONLINEARSOLVER_H
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namespace HybridNonLinearSolverSpace {
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enum Status {
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Running = -1,
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ImproperInputParameters = 0,
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RelativeErrorTooSmall = 1,
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TooManyFunctionEvaluation = 2,
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TolTooSmall = 3,
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NotMakingProgressJacobian = 4,
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NotMakingProgressIterations = 5,
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UserAksed = 6
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};
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}
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/**
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* \ingroup NonLinearOptimization_Module
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* \brief Finds a zero of a system of n
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@ -46,17 +59,6 @@ public:
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HybridNonLinearSolver(FunctorType &_functor)
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: functor(_functor) { nfev=njev=iter = 0; fnorm= 0.; }
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enum Status {
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Running = -1,
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ImproperInputParameters = 0,
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RelativeErrorTooSmall = 1,
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TooManyFunctionEvaluation = 2,
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TolTooSmall = 3,
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NotMakingProgressJacobian = 4,
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NotMakingProgressIterations = 5,
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UserAksed = 6
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};
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struct Parameters {
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Parameters()
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: factor(Scalar(100.))
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@ -77,38 +79,38 @@ public:
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/* TODO: if eigen provides a triangular storage, use it here */
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typedef Matrix< Scalar, Dynamic, Dynamic > UpperTriangularType;
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Status hybrj1(
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HybridNonLinearSolverSpace::Status hybrj1(
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FVectorType &x,
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const Scalar tol = ei_sqrt(epsilon<Scalar>())
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);
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Status solveInit(
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HybridNonLinearSolverSpace::Status solveInit(
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FVectorType &x,
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const int mode=1
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);
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Status solveOneStep(
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HybridNonLinearSolverSpace::Status solveOneStep(
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FVectorType &x,
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const int mode=1
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);
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Status solve(
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HybridNonLinearSolverSpace::Status solve(
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FVectorType &x,
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const int mode=1
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);
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Status hybrd1(
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HybridNonLinearSolverSpace::Status hybrd1(
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FVectorType &x,
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const Scalar tol = ei_sqrt(epsilon<Scalar>())
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);
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Status solveNumericalDiffInit(
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HybridNonLinearSolverSpace::Status solveNumericalDiffInit(
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FVectorType &x,
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const int mode=1
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);
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Status solveNumericalDiffOneStep(
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HybridNonLinearSolverSpace::Status solveNumericalDiffOneStep(
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FVectorType &x,
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const int mode=1
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);
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Status solveNumericalDiff(
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HybridNonLinearSolverSpace::Status solveNumericalDiff(
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FVectorType &x,
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const int mode=1
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);
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@ -142,7 +144,7 @@ private:
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template<typename FunctorType, typename Scalar>
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typename HybridNonLinearSolver<FunctorType,Scalar>::Status
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HybridNonLinearSolverSpace::Status
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HybridNonLinearSolver<FunctorType,Scalar>::hybrj1(
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FVectorType &x,
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const Scalar tol
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@ -152,7 +154,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::hybrj1(
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/* check the input parameters for errors. */
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if (n <= 0 || tol < 0.)
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return ImproperInputParameters;
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return HybridNonLinearSolverSpace::ImproperInputParameters;
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resetParameters();
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parameters.maxfev = 100*(n+1);
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@ -165,7 +167,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::hybrj1(
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}
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template<typename FunctorType, typename Scalar>
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typename HybridNonLinearSolver<FunctorType,Scalar>::Status
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HybridNonLinearSolverSpace::Status
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HybridNonLinearSolver<FunctorType,Scalar>::solveInit(
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FVectorType &x,
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const int mode
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@ -187,17 +189,17 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveInit(
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/* check the input parameters for errors. */
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if (n <= 0 || parameters.xtol < 0. || parameters.maxfev <= 0 || parameters.factor <= 0. )
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return ImproperInputParameters;
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return HybridNonLinearSolverSpace::ImproperInputParameters;
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if (mode == 2)
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for (int j = 0; j < n; ++j)
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if (diag[j] <= 0.)
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return ImproperInputParameters;
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return HybridNonLinearSolverSpace::ImproperInputParameters;
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/* evaluate the function at the starting point */
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/* and calculate its norm. */
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nfev = 1;
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if ( functor(x, fvec) < 0)
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return UserAksed;
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return HybridNonLinearSolverSpace::UserAksed;
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fnorm = fvec.stableNorm();
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/* initialize iteration counter and monitors. */
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@ -207,11 +209,11 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveInit(
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nslow1 = 0;
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nslow2 = 0;
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return Running;
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return HybridNonLinearSolverSpace::Running;
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}
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template<typename FunctorType, typename Scalar>
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typename HybridNonLinearSolver<FunctorType,Scalar>::Status
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HybridNonLinearSolverSpace::Status
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HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
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FVectorType &x,
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const int mode
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@ -224,7 +226,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
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/* calculate the jacobian matrix. */
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if ( functor.df(x, fjac) < 0)
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return UserAksed;
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return HybridNonLinearSolverSpace::UserAksed;
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++njev;
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wa2 = fjac.colwise().blueNorm();
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@ -276,7 +278,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
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/* evaluate the function at x + p and calculate its norm. */
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if ( functor(wa2, wa4) < 0)
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return UserAksed;
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return HybridNonLinearSolverSpace::UserAksed;
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++nfev;
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fnorm1 = wa4.stableNorm();
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@ -334,17 +336,17 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
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/* test for convergence. */
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if (delta <= parameters.xtol * xnorm || fnorm == 0.)
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return RelativeErrorTooSmall;
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return HybridNonLinearSolverSpace::RelativeErrorTooSmall;
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/* tests for termination and stringent tolerances. */
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if (nfev >= parameters.maxfev)
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return TooManyFunctionEvaluation;
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return HybridNonLinearSolverSpace::TooManyFunctionEvaluation;
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if (Scalar(.1) * std::max(Scalar(.1) * delta, pnorm) <= epsilon<Scalar>() * xnorm)
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return TolTooSmall;
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return HybridNonLinearSolverSpace::TolTooSmall;
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if (nslow2 == 5)
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return NotMakingProgressJacobian;
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return HybridNonLinearSolverSpace::NotMakingProgressJacobian;
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if (nslow1 == 10)
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return NotMakingProgressIterations;
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return HybridNonLinearSolverSpace::NotMakingProgressIterations;
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/* criterion for recalculating jacobian. */
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if (ncfail == 2)
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@ -365,18 +367,18 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
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jeval = false;
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}
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return Running;
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return HybridNonLinearSolverSpace::Running;
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}
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template<typename FunctorType, typename Scalar>
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typename HybridNonLinearSolver<FunctorType,Scalar>::Status
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HybridNonLinearSolverSpace::Status
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HybridNonLinearSolver<FunctorType,Scalar>::solve(
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FVectorType &x,
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const int mode
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)
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{
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Status status = solveInit(x, mode);
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while (status==Running)
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HybridNonLinearSolverSpace::Status status = solveInit(x, mode);
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while (status==HybridNonLinearSolverSpace::Running)
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status = solveOneStep(x, mode);
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return status;
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}
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@ -384,7 +386,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solve(
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template<typename FunctorType, typename Scalar>
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typename HybridNonLinearSolver<FunctorType,Scalar>::Status
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HybridNonLinearSolverSpace::Status
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HybridNonLinearSolver<FunctorType,Scalar>::hybrd1(
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FVectorType &x,
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const Scalar tol
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@ -394,7 +396,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::hybrd1(
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/* check the input parameters for errors. */
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if (n <= 0 || tol < 0.)
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return ImproperInputParameters;
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return HybridNonLinearSolverSpace::ImproperInputParameters;
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resetParameters();
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parameters.maxfev = 200*(n+1);
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@ -408,7 +410,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::hybrd1(
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}
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template<typename FunctorType, typename Scalar>
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typename HybridNonLinearSolver<FunctorType,Scalar>::Status
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HybridNonLinearSolverSpace::Status
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HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffInit(
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FVectorType &x,
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const int mode
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@ -434,17 +436,17 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffInit(
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/* check the input parameters for errors. */
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if (n <= 0 || parameters.xtol < 0. || parameters.maxfev <= 0 || parameters.nb_of_subdiagonals< 0 || parameters.nb_of_superdiagonals< 0 || parameters.factor <= 0. )
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return ImproperInputParameters;
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return HybridNonLinearSolverSpace::ImproperInputParameters;
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if (mode == 2)
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for (int j = 0; j < n; ++j)
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if (diag[j] <= 0.)
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return ImproperInputParameters;
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return HybridNonLinearSolverSpace::ImproperInputParameters;
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/* evaluate the function at the starting point */
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/* and calculate its norm. */
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nfev = 1;
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if ( functor(x, fvec) < 0)
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return UserAksed;
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return HybridNonLinearSolverSpace::UserAksed;
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fnorm = fvec.stableNorm();
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/* initialize iteration counter and monitors. */
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@ -454,11 +456,11 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffInit(
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nslow1 = 0;
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nslow2 = 0;
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return Running;
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return HybridNonLinearSolverSpace::Running;
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}
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template<typename FunctorType, typename Scalar>
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typename HybridNonLinearSolver<FunctorType,Scalar>::Status
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HybridNonLinearSolverSpace::Status
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HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
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FVectorType &x,
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const int mode
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@ -473,7 +475,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
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/* calculate the jacobian matrix. */
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if (ei_fdjac1(functor, x, fvec, fjac, parameters.nb_of_subdiagonals, parameters.nb_of_superdiagonals, parameters.epsfcn) <0)
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return UserAksed;
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return HybridNonLinearSolverSpace::UserAksed;
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nfev += std::min(parameters.nb_of_subdiagonals+parameters.nb_of_superdiagonals+ 1, n);
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wa2 = fjac.colwise().blueNorm();
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@ -525,7 +527,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
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/* evaluate the function at x + p and calculate its norm. */
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if ( functor(wa2, wa4) < 0)
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return UserAksed;
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return HybridNonLinearSolverSpace::UserAksed;
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++nfev;
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fnorm1 = wa4.stableNorm();
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@ -583,17 +585,17 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
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/* test for convergence. */
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if (delta <= parameters.xtol * xnorm || fnorm == 0.)
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return RelativeErrorTooSmall;
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return HybridNonLinearSolverSpace::RelativeErrorTooSmall;
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/* tests for termination and stringent tolerances. */
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if (nfev >= parameters.maxfev)
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return TooManyFunctionEvaluation;
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return HybridNonLinearSolverSpace::TooManyFunctionEvaluation;
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if (Scalar(.1) * std::max(Scalar(.1) * delta, pnorm) <= epsilon<Scalar>() * xnorm)
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return TolTooSmall;
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return HybridNonLinearSolverSpace::TolTooSmall;
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if (nslow2 == 5)
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return NotMakingProgressJacobian;
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return HybridNonLinearSolverSpace::NotMakingProgressJacobian;
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if (nslow1 == 10)
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return NotMakingProgressIterations;
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return HybridNonLinearSolverSpace::NotMakingProgressIterations;
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/* criterion for recalculating jacobian. */
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if (ncfail == 2)
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@ -614,18 +616,18 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
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jeval = false;
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}
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return Running;
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return HybridNonLinearSolverSpace::Running;
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}
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template<typename FunctorType, typename Scalar>
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typename HybridNonLinearSolver<FunctorType,Scalar>::Status
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HybridNonLinearSolverSpace::Status
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HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(
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FVectorType &x,
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const int mode
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)
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{
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Status status = solveNumericalDiffInit(x, mode);
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while (status==Running)
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HybridNonLinearSolverSpace::Status status = solveNumericalDiffInit(x, mode);
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while (status==HybridNonLinearSolverSpace::Running)
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status = solveNumericalDiffOneStep(x, mode);
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return status;
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}
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@ -28,21 +28,8 @@
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#ifndef EIGEN_LEVENBERGMARQUARDT__H
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#define EIGEN_LEVENBERGMARQUARDT__H
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/**
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* \ingroup NonLinearOptimization_Module
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* \brief Performs non linear optimization over a non-linear function,
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* using a variant of the Levenberg Marquardt algorithm.
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*
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* Check wikipedia for more information.
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* http://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm
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*/
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template<typename FunctorType, typename Scalar=double>
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class LevenbergMarquardt
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{
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public:
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LevenbergMarquardt(FunctorType &_functor)
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: functor(_functor) { nfev = njev = iter = 0; fnorm=gnorm = 0.; }
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namespace LevenbergMarquardtSpace {
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enum Status {
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NotStarted = -2,
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Running = -1,
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@ -57,6 +44,24 @@ public:
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GtolTooSmall = 8,
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UserAsked = 9
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};
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}
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/**
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* \ingroup NonLinearOptimization_Module
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* \brief Performs non linear optimization over a non-linear function,
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* using a variant of the Levenberg Marquardt algorithm.
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*
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* Check wikipedia for more information.
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* http://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm
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*/
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template<typename FunctorType, typename Scalar=double>
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class LevenbergMarquardt
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{
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public:
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LevenbergMarquardt(FunctorType &_functor)
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: functor(_functor) { nfev = njev = iter = 0; fnorm=gnorm = 0.; }
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struct Parameters {
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Parameters()
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@ -77,45 +82,45 @@ public:
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typedef Matrix< Scalar, Dynamic, 1 > FVectorType;
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typedef Matrix< Scalar, Dynamic, Dynamic > JacobianType;
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Status lmder1(
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LevenbergMarquardtSpace::Status lmder1(
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FVectorType &x,
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const Scalar tol = ei_sqrt(epsilon<Scalar>())
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);
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Status minimize(
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LevenbergMarquardtSpace::Status minimize(
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FVectorType &x,
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const int mode=1
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);
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Status minimizeInit(
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LevenbergMarquardtSpace::Status minimizeInit(
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FVectorType &x,
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const int mode=1
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);
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Status minimizeOneStep(
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LevenbergMarquardtSpace::Status minimizeOneStep(
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FVectorType &x,
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const int mode=1
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);
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static Status lmdif1(
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static LevenbergMarquardtSpace::Status lmdif1(
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FunctorType &functor,
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FVectorType &x,
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int *nfev,
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const Scalar tol = ei_sqrt(epsilon<Scalar>())
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);
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Status lmstr1(
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LevenbergMarquardtSpace::Status lmstr1(
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FVectorType &x,
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const Scalar tol = ei_sqrt(epsilon<Scalar>())
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);
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Status minimizeOptimumStorage(
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LevenbergMarquardtSpace::Status minimizeOptimumStorage(
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FVectorType &x,
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const int mode=1
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);
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Status minimizeOptimumStorageInit(
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LevenbergMarquardtSpace::Status minimizeOptimumStorageInit(
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FVectorType &x,
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const int mode=1
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);
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Status minimizeOptimumStorageOneStep(
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LevenbergMarquardtSpace::Status minimizeOptimumStorageOneStep(
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FVectorType &x,
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const int mode=1
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);
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@ -146,7 +151,7 @@ private:
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};
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template<typename FunctorType, typename Scalar>
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typename LevenbergMarquardt<FunctorType,Scalar>::Status
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LevenbergMarquardtSpace::Status
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LevenbergMarquardt<FunctorType,Scalar>::lmder1(
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FVectorType &x,
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const Scalar tol
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@ -157,7 +162,7 @@ LevenbergMarquardt<FunctorType,Scalar>::lmder1(
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/* check the input parameters for errors. */
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if (n <= 0 || m < n || tol < 0.)
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return ImproperInputParameters;
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return LevenbergMarquardtSpace::ImproperInputParameters;
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resetParameters();
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parameters.ftol = tol;
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@ -169,21 +174,21 @@ LevenbergMarquardt<FunctorType,Scalar>::lmder1(
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template<typename FunctorType, typename Scalar>
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typename LevenbergMarquardt<FunctorType,Scalar>::Status
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LevenbergMarquardtSpace::Status
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LevenbergMarquardt<FunctorType,Scalar>::minimize(
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FVectorType &x,
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const int mode
|
||||
)
|
||||
{
|
||||
Status status = minimizeInit(x, mode);
|
||||
LevenbergMarquardtSpace::Status status = minimizeInit(x, mode);
|
||||
do {
|
||||
status = minimizeOneStep(x, mode);
|
||||
} while (status==Running);
|
||||
} while (status==LevenbergMarquardtSpace::Running);
|
||||
return status;
|
||||
}
|
||||
|
||||
template<typename FunctorType, typename Scalar>
|
||||
typename LevenbergMarquardt<FunctorType,Scalar>::Status
|
||||
LevenbergMarquardtSpace::Status
|
||||
LevenbergMarquardt<FunctorType,Scalar>::minimizeInit(
|
||||
FVectorType &x,
|
||||
const int mode
|
||||
@ -207,29 +212,29 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeInit(
|
||||
|
||||
/* check the input parameters for errors. */
|
||||
if (n <= 0 || m < n || parameters.ftol < 0. || parameters.xtol < 0. || parameters.gtol < 0. || parameters.maxfev <= 0 || parameters.factor <= 0.)
|
||||
return ImproperInputParameters;
|
||||
return LevenbergMarquardtSpace::ImproperInputParameters;
|
||||
|
||||
if (mode == 2)
|
||||
for (int j = 0; j < n; ++j)
|
||||
if (diag[j] <= 0.)
|
||||
return ImproperInputParameters;
|
||||
return LevenbergMarquardtSpace::ImproperInputParameters;
|
||||
|
||||
/* evaluate the function at the starting point */
|
||||
/* and calculate its norm. */
|
||||
nfev = 1;
|
||||
if ( functor(x, fvec) < 0)
|
||||
return UserAsked;
|
||||
return LevenbergMarquardtSpace::UserAsked;
|
||||
fnorm = fvec.stableNorm();
|
||||
|
||||
/* initialize levenberg-marquardt parameter and iteration counter. */
|
||||
par = 0.;
|
||||
iter = 1;
|
||||
|
||||
return NotStarted;
|
||||
return LevenbergMarquardtSpace::NotStarted;
|
||||
}
|
||||
|
||||
template<typename FunctorType, typename Scalar>
|
||||
typename LevenbergMarquardt<FunctorType,Scalar>::Status
|
||||
LevenbergMarquardtSpace::Status
|
||||
LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
|
||||
FVectorType &x,
|
||||
const int mode
|
||||
@ -240,7 +245,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
|
||||
/* calculate the jacobian matrix. */
|
||||
int df_ret = functor.df(x, fjac);
|
||||
if (df_ret<0)
|
||||
return UserAsked;
|
||||
return LevenbergMarquardtSpace::UserAsked;
|
||||
if (df_ret>0)
|
||||
// numerical diff, we evaluated the function df_ret times
|
||||
nfev += df_ret;
|
||||
@ -282,7 +287,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
|
||||
|
||||
/* test for convergence of the gradient norm. */
|
||||
if (gnorm <= parameters.gtol)
|
||||
return CosinusTooSmall;
|
||||
return LevenbergMarquardtSpace::CosinusTooSmall;
|
||||
|
||||
/* rescale if necessary. */
|
||||
if (mode != 2)
|
||||
@ -304,7 +309,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
|
||||
|
||||
/* evaluate the function at x + p and calculate its norm. */
|
||||
if ( functor(wa2, wa4) < 0)
|
||||
return UserAsked;
|
||||
return LevenbergMarquardtSpace::UserAsked;
|
||||
++nfev;
|
||||
fnorm1 = wa4.stableNorm();
|
||||
|
||||
@ -356,29 +361,29 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
|
||||
|
||||
/* tests for convergence. */
|
||||
if (ei_abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. && delta <= parameters.xtol * xnorm)
|
||||
return RelativeErrorAndReductionTooSmall;
|
||||
return LevenbergMarquardtSpace::RelativeErrorAndReductionTooSmall;
|
||||
if (ei_abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.)
|
||||
return RelativeReductionTooSmall;
|
||||
return LevenbergMarquardtSpace::RelativeReductionTooSmall;
|
||||
if (delta <= parameters.xtol * xnorm)
|
||||
return RelativeErrorTooSmall;
|
||||
return LevenbergMarquardtSpace::RelativeErrorTooSmall;
|
||||
|
||||
/* tests for termination and stringent tolerances. */
|
||||
if (nfev >= parameters.maxfev)
|
||||
return TooManyFunctionEvaluation;
|
||||
return LevenbergMarquardtSpace::TooManyFunctionEvaluation;
|
||||
if (ei_abs(actred) <= epsilon<Scalar>() && prered <= epsilon<Scalar>() && Scalar(.5) * ratio <= 1.)
|
||||
return FtolTooSmall;
|
||||
return LevenbergMarquardtSpace::FtolTooSmall;
|
||||
if (delta <= epsilon<Scalar>() * xnorm)
|
||||
return XtolTooSmall;
|
||||
return LevenbergMarquardtSpace::XtolTooSmall;
|
||||
if (gnorm <= epsilon<Scalar>())
|
||||
return GtolTooSmall;
|
||||
return LevenbergMarquardtSpace::GtolTooSmall;
|
||||
|
||||
} while (ratio < Scalar(1e-4));
|
||||
|
||||
return Running;
|
||||
return LevenbergMarquardtSpace::Running;
|
||||
}
|
||||
|
||||
template<typename FunctorType, typename Scalar>
|
||||
typename LevenbergMarquardt<FunctorType,Scalar>::Status
|
||||
LevenbergMarquardtSpace::Status
|
||||
LevenbergMarquardt<FunctorType,Scalar>::lmstr1(
|
||||
FVectorType &x,
|
||||
const Scalar tol
|
||||
@ -389,7 +394,7 @@ LevenbergMarquardt<FunctorType,Scalar>::lmstr1(
|
||||
|
||||
/* check the input parameters for errors. */
|
||||
if (n <= 0 || m < n || tol < 0.)
|
||||
return ImproperInputParameters;
|
||||
return LevenbergMarquardtSpace::ImproperInputParameters;
|
||||
|
||||
resetParameters();
|
||||
parameters.ftol = tol;
|
||||
@ -400,7 +405,7 @@ LevenbergMarquardt<FunctorType,Scalar>::lmstr1(
|
||||
}
|
||||
|
||||
template<typename FunctorType, typename Scalar>
|
||||
typename LevenbergMarquardt<FunctorType,Scalar>::Status
|
||||
LevenbergMarquardtSpace::Status
|
||||
LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageInit(
|
||||
FVectorType &x,
|
||||
const int mode
|
||||
@ -424,30 +429,30 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageInit(
|
||||
|
||||
/* check the input parameters for errors. */
|
||||
if (n <= 0 || m < n || parameters.ftol < 0. || parameters.xtol < 0. || parameters.gtol < 0. || parameters.maxfev <= 0 || parameters.factor <= 0.)
|
||||
return ImproperInputParameters;
|
||||
return LevenbergMarquardtSpace::ImproperInputParameters;
|
||||
|
||||
if (mode == 2)
|
||||
for (int j = 0; j < n; ++j)
|
||||
if (diag[j] <= 0.)
|
||||
return ImproperInputParameters;
|
||||
return LevenbergMarquardtSpace::ImproperInputParameters;
|
||||
|
||||
/* evaluate the function at the starting point */
|
||||
/* and calculate its norm. */
|
||||
nfev = 1;
|
||||
if ( functor(x, fvec) < 0)
|
||||
return UserAsked;
|
||||
return LevenbergMarquardtSpace::UserAsked;
|
||||
fnorm = fvec.stableNorm();
|
||||
|
||||
/* initialize levenberg-marquardt parameter and iteration counter. */
|
||||
par = 0.;
|
||||
iter = 1;
|
||||
|
||||
return NotStarted;
|
||||
return LevenbergMarquardtSpace::NotStarted;
|
||||
}
|
||||
|
||||
|
||||
template<typename FunctorType, typename Scalar>
|
||||
typename LevenbergMarquardt<FunctorType,Scalar>::Status
|
||||
LevenbergMarquardtSpace::Status
|
||||
LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
|
||||
FVectorType &x,
|
||||
const int mode
|
||||
@ -464,7 +469,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
|
||||
fjac.fill(0.);
|
||||
int rownb = 2;
|
||||
for (i = 0; i < m; ++i) {
|
||||
if (functor.df(x, wa3, rownb) < 0) return UserAsked;
|
||||
if (functor.df(x, wa3, rownb) < 0) return LevenbergMarquardtSpace::UserAsked;
|
||||
ei_rwupdt<Scalar>(fjac, wa3, qtf, fvec[i]);
|
||||
++rownb;
|
||||
}
|
||||
@ -528,7 +533,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
|
||||
|
||||
/* test for convergence of the gradient norm. */
|
||||
if (gnorm <= parameters.gtol)
|
||||
return CosinusTooSmall;
|
||||
return LevenbergMarquardtSpace::CosinusTooSmall;
|
||||
|
||||
/* rescale if necessary. */
|
||||
if (mode != 2)
|
||||
@ -550,7 +555,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
|
||||
|
||||
/* evaluate the function at x + p and calculate its norm. */
|
||||
if ( functor(wa2, wa4) < 0)
|
||||
return UserAsked;
|
||||
return LevenbergMarquardtSpace::UserAsked;
|
||||
++nfev;
|
||||
fnorm1 = wa4.stableNorm();
|
||||
|
||||
@ -602,43 +607,43 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
|
||||
|
||||
/* tests for convergence. */
|
||||
if (ei_abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. && delta <= parameters.xtol * xnorm)
|
||||
return RelativeErrorAndReductionTooSmall;
|
||||
return LevenbergMarquardtSpace::RelativeErrorAndReductionTooSmall;
|
||||
if (ei_abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.)
|
||||
return RelativeReductionTooSmall;
|
||||
return LevenbergMarquardtSpace::RelativeReductionTooSmall;
|
||||
if (delta <= parameters.xtol * xnorm)
|
||||
return RelativeErrorTooSmall;
|
||||
return LevenbergMarquardtSpace::RelativeErrorTooSmall;
|
||||
|
||||
/* tests for termination and stringent tolerances. */
|
||||
if (nfev >= parameters.maxfev)
|
||||
return TooManyFunctionEvaluation;
|
||||
return LevenbergMarquardtSpace::TooManyFunctionEvaluation;
|
||||
if (ei_abs(actred) <= epsilon<Scalar>() && prered <= epsilon<Scalar>() && Scalar(.5) * ratio <= 1.)
|
||||
return FtolTooSmall;
|
||||
return LevenbergMarquardtSpace::FtolTooSmall;
|
||||
if (delta <= epsilon<Scalar>() * xnorm)
|
||||
return XtolTooSmall;
|
||||
return LevenbergMarquardtSpace::XtolTooSmall;
|
||||
if (gnorm <= epsilon<Scalar>())
|
||||
return GtolTooSmall;
|
||||
return LevenbergMarquardtSpace::GtolTooSmall;
|
||||
|
||||
} while (ratio < Scalar(1e-4));
|
||||
|
||||
return Running;
|
||||
return LevenbergMarquardtSpace::Running;
|
||||
}
|
||||
|
||||
template<typename FunctorType, typename Scalar>
|
||||
typename LevenbergMarquardt<FunctorType,Scalar>::Status
|
||||
LevenbergMarquardtSpace::Status
|
||||
LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
|
||||
FVectorType &x,
|
||||
const int mode
|
||||
)
|
||||
{
|
||||
Status status = minimizeOptimumStorageInit(x, mode);
|
||||
LevenbergMarquardtSpace::Status status = minimizeOptimumStorageInit(x, mode);
|
||||
do {
|
||||
status = minimizeOptimumStorageOneStep(x, mode);
|
||||
} while (status==Running);
|
||||
} while (status==LevenbergMarquardtSpace::Running);
|
||||
return status;
|
||||
}
|
||||
|
||||
template<typename FunctorType, typename Scalar>
|
||||
typename LevenbergMarquardt<FunctorType,Scalar>::Status
|
||||
LevenbergMarquardtSpace::Status
|
||||
LevenbergMarquardt<FunctorType,Scalar>::lmdif1(
|
||||
FunctorType &functor,
|
||||
FVectorType &x,
|
||||
@ -651,7 +656,7 @@ LevenbergMarquardt<FunctorType,Scalar>::lmdif1(
|
||||
|
||||
/* check the input parameters for errors. */
|
||||
if (n <= 0 || m < n || tol < 0.)
|
||||
return ImproperInputParameters;
|
||||
return LevenbergMarquardtSpace::ImproperInputParameters;
|
||||
|
||||
NumericalDiff<FunctorType> numDiff(functor);
|
||||
// embedded LevenbergMarquardt
|
||||
@ -660,7 +665,7 @@ LevenbergMarquardt<FunctorType,Scalar>::lmdif1(
|
||||
lm.parameters.xtol = tol;
|
||||
lm.parameters.maxfev = 200*(n+1);
|
||||
|
||||
Status info = Status(lm.minimize(x));
|
||||
LevenbergMarquardtSpace::Status info = LevenbergMarquardtSpace::Status(lm.minimize(x));
|
||||
if (nfev)
|
||||
* nfev = lm.nfev;
|
||||
return info;
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user