mirror of
https://gitlab.com/libeigen/eigen.git
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define and use struct Parameters
This commit is contained in:
Thomas Capricelli
parent
d13bcdc891
commit
e465ea82e1
@ -17,6 +17,16 @@ public:
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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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, maxfev(1000)
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, xtol(ei_sqrt(epsilon<Scalar>())) {}
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Scalar factor;
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int maxfev; // maximum number of function evaluation
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Scalar xtol;
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};
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Status solve(
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Matrix< Scalar, Dynamic, 1 > &x,
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const Scalar tol = ei_sqrt(epsilon<Scalar>())
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@ -25,10 +35,8 @@ public:
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Matrix< Scalar, Dynamic, 1 > &x,
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int &nfev, int &njev,
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Matrix< Scalar, Dynamic, 1 > &diag,
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const int mode=1,
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const int maxfev = 1000,
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const Scalar factor = Scalar(100.),
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const Scalar xtol = ei_sqrt(epsilon<Scalar>())
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const Parameters ¶meters,
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const int mode=1
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);
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Status solveNumericalDiff(
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@ -39,12 +47,10 @@ public:
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Matrix< Scalar, Dynamic, 1 > &x,
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int &nfev,
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Matrix< Scalar, Dynamic, 1 > &diag,
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const Parameters ¶meters,
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const int mode=1,
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int nb_of_subdiagonals = -1,
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int nb_of_superdiagonals = -1,
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const int maxfev = 2000,
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const Scalar factor = Scalar(100.),
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const Scalar xtol = ei_sqrt(epsilon<Scalar>()),
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const Scalar epsfcn = Scalar(0.)
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);
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@ -68,6 +74,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solve(
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const int n = x.size();
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int nfev=0, njev=0;
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Matrix< Scalar, Dynamic, 1> diag;
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Parameters parameters;
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/* check the input parameters for errors. */
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if (n <= 0 || tol < 0.) {
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@ -75,15 +82,15 @@ HybridNonLinearSolver<FunctorType,Scalar>::solve(
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return ImproperInputParameters;
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}
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parameters.maxfev = 100*(n+1);
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parameters.xtol = tol;
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diag.setConstant(n, 1.);
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return solve(
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x,
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nfev, njev,
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diag,
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2,
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(n+1)*100,
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100.,
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tol
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parameters,
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2
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);
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}
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@ -96,10 +103,8 @@ HybridNonLinearSolver<FunctorType,Scalar>::solve(
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int &nfev,
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int &njev,
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Matrix< Scalar, Dynamic, 1 > &diag,
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const int mode,
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const int maxfev,
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const Scalar factor,
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const Scalar xtol
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const Parameters ¶meters,
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const int mode
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)
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{
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const int n = x.size();
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@ -133,7 +138,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solve(
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/* check the input parameters for errors. */
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if (n <= 0 || xtol < 0. || maxfev <= 0 || factor <= 0. )
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if (n <= 0 || parameters.xtol < 0. || parameters.maxfev <= 0 || parameters.factor <= 0. )
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return RelativeErrorTooSmall;
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if (mode == 2)
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for (j = 0; j < n; ++j)
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@ -187,9 +192,9 @@ HybridNonLinearSolver<FunctorType,Scalar>::solve(
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wa3 = diag.cwise() * x;
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xnorm = wa3.stableNorm();
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delta = factor * xnorm;
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delta = parameters.factor * xnorm;
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if (delta == 0.)
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delta = factor;
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delta = parameters.factor;
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}
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/* form (q transpose)*fvec and store in qtf. */
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@ -326,12 +331,12 @@ HybridNonLinearSolver<FunctorType,Scalar>::solve(
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/* test for convergence. */
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if (delta <= xtol * xnorm || fnorm == 0.)
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if (delta <= parameters.xtol * xnorm || fnorm == 0.)
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return RelativeErrorTooSmall;
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/* tests for termination and stringent tolerances. */
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if (nfev >= maxfev)
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if (nfev >= parameters.maxfev)
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return TooManyFunctionEvaluation;
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/* Computing MAX */
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if (Scalar(.1) * std::max(Scalar(.1) * delta, pnorm) <= epsilon<Scalar>() * xnorm)
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@ -384,6 +389,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(
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const int n = x.size();
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int nfev=0;
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Matrix< Scalar, Dynamic, 1> diag;
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Parameters parameters;
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/* check the input parameters for errors. */
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if (n <= 0 || tol < 0.) {
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@ -391,16 +397,18 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(
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return ImproperInputParameters;
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}
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parameters.maxfev = 200*(n+1);
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parameters.xtol = tol;
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diag.setConstant(n, 1.);
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return solveNumericalDiff(
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x,
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nfev,
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diag,
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parameters,
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2,
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-1, -1,
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(n+1)*200,
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100.,
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tol, Scalar(0.)
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Scalar(0.)
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);
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}
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@ -411,12 +419,10 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(
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Matrix< Scalar, Dynamic, 1 > &x,
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int &nfev,
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Matrix< Scalar, Dynamic, 1 > &diag,
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const Parameters ¶meters,
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const int mode,
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int nb_of_subdiagonals,
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int nb_of_superdiagonals,
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const int maxfev,
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const Scalar factor,
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const Scalar xtol,
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const Scalar epsfcn
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)
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{
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@ -454,7 +460,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(
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/* check the input parameters for errors. */
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if (n <= 0 || xtol < 0. || maxfev <= 0 || nb_of_subdiagonals < 0 || nb_of_superdiagonals < 0 || factor <= 0. )
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if (n <= 0 || parameters.xtol < 0. || parameters.maxfev <= 0 || nb_of_subdiagonals < 0 || nb_of_superdiagonals < 0 || parameters.factor <= 0. )
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return RelativeErrorTooSmall;
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if (mode == 2)
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for (j = 0; j < n; ++j)
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@ -514,9 +520,9 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(
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wa3 = diag.cwise() * x;
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xnorm = wa3.stableNorm();
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delta = factor * xnorm;
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delta = parameters.factor * xnorm;
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if (delta == 0.)
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delta = factor;
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delta = parameters.factor;
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}
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/* form (q transpose)*fvec and store in qtf. */
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@ -653,12 +659,12 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(
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/* test for convergence. */
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if (delta <= xtol * xnorm || fnorm == 0.)
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if (delta <= parameters.xtol * xnorm || fnorm == 0.)
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return RelativeErrorTooSmall;
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/* tests for termination and stringent tolerances. */
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if (nfev >= maxfev)
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if (nfev >= parameters.maxfev)
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return TooManyFunctionEvaluation;
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/* Computing MAX */
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if (Scalar(.1) * std::max(Scalar(.1) * delta, pnorm) <= epsilon<Scalar>() * xnorm)
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@ -1,5 +1,4 @@
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template<typename FunctorType, typename Scalar=double>
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class LevenbergMarquardt
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{
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@ -21,6 +20,20 @@ public:
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UserAsked = 9
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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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, maxfev(400)
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, ftol(ei_sqrt(epsilon<Scalar>()))
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, xtol(ei_sqrt(epsilon<Scalar>()))
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, gtol(Scalar(0.)) { }
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Scalar factor;
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int maxfev; // maximum number of function evaluation
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Scalar ftol;
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Scalar xtol;
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Scalar gtol;
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};
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Status minimize(
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Matrix< Scalar, Dynamic, 1 > &x,
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const Scalar tol = ei_sqrt(epsilon<Scalar>())
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@ -31,12 +44,8 @@ public:
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int &nfev,
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int &njev,
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Matrix< Scalar, Dynamic, 1 > &diag,
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const int mode=1,
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const Scalar factor = Scalar(100.),
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const int maxfev = 400,
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const Scalar ftol = ei_sqrt(epsilon<Scalar>()),
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const Scalar xtol = ei_sqrt(epsilon<Scalar>()),
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const Scalar gtol = Scalar(0.)
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const Parameters ¶meters,
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const int mode=1
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);
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Status minimizeNumericalDiff(
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@ -48,12 +57,8 @@ public:
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Matrix< Scalar, Dynamic, 1 > &x,
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int &nfev,
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Matrix< Scalar, Dynamic, 1 > &diag,
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const Parameters ¶meters,
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const int mode=1,
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const Scalar factor = Scalar(100.),
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const int maxfev = 400,
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const Scalar ftol = ei_sqrt(epsilon<Scalar>()),
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const Scalar xtol = ei_sqrt(epsilon<Scalar>()),
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const Scalar gtol = Scalar(0.),
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const Scalar epsfcn = Scalar(0.)
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);
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@ -67,12 +72,8 @@ public:
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int &nfev,
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int &njev,
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Matrix< Scalar, Dynamic, 1 > &diag,
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const int mode=1,
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const Scalar factor = Scalar(100.),
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const int maxfev = 400,
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const Scalar ftol = ei_sqrt(epsilon<Scalar>()),
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const Scalar xtol = ei_sqrt(epsilon<Scalar>()),
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const Scalar gtol = Scalar(0.)
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const Parameters ¶meters,
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const int mode=1
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);
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Matrix< Scalar, Dynamic, 1 > fvec;
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@ -96,6 +97,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimize(
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Matrix< Scalar, Dynamic, Dynamic > fjac(m, n);
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Matrix< Scalar, Dynamic, 1> diag, qtf;
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VectorXi ipvt;
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Parameters parameters;
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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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@ -103,14 +105,16 @@ LevenbergMarquardt<FunctorType,Scalar>::minimize(
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return ImproperInputParameters;
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}
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parameters.ftol = tol;
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parameters.xtol = tol;
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parameters.maxfev = 100*(n+1);
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return minimize(
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x,
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nfev, njev,
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diag,
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1,
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100.,
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(n+1)*100,
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tol, tol, Scalar(0.)
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parameters,
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1
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);
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}
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@ -122,12 +126,8 @@ LevenbergMarquardt<FunctorType,Scalar>::minimize(
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int &nfev,
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int &njev,
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Matrix< Scalar, Dynamic, 1 > &diag,
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const int mode,
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const Scalar factor,
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const int maxfev,
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const Scalar ftol,
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const Scalar xtol,
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const Scalar gtol
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const Parameters ¶meters,
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const int mode
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)
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{
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const int n = x.size();
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@ -156,7 +156,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimize(
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/* check the input parameters for errors. */
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if (n <= 0 || m < n || ftol < 0. || xtol < 0. || gtol < 0. || maxfev <= 0 || factor <= 0.)
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if (n <= 0 || m < n || parameters.ftol < 0. || parameters.xtol < 0. || parameters.gtol < 0. || parameters.maxfev <= 0 || parameters.factor <= 0.)
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return RelativeErrorTooSmall;
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if (mode == 2)
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@ -208,9 +208,9 @@ LevenbergMarquardt<FunctorType,Scalar>::minimize(
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wa3 = diag.cwise() * x;
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xnorm = wa3.stableNorm();
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delta = factor * xnorm;
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delta = parameters.factor * xnorm;
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if (delta == 0.)
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delta = factor;
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delta = parameters.factor;
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}
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/* form (q transpose)*fvec and store the first n components in */
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@ -247,7 +247,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimize(
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/* test for convergence of the gradient norm. */
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if (gnorm <= gtol)
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if (gnorm <= parameters.gtol)
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return CosinusTooSmall;
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/* rescale if necessary. */
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@ -341,16 +341,16 @@ LevenbergMarquardt<FunctorType,Scalar>::minimize(
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/* tests for convergence. */
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if (ei_abs(actred) <= ftol && prered <= ftol && Scalar(.5) * ratio <= 1. && delta <= xtol * xnorm)
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if (ei_abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. && delta <= parameters.xtol * xnorm)
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return RelativeErrorAndReductionTooSmall;
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if (ei_abs(actred) <= ftol && prered <= ftol && Scalar(.5) * ratio <= 1.)
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if (ei_abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.)
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return RelativeReductionTooSmall;
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if (delta <= xtol * xnorm)
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if (delta <= parameters.xtol * xnorm)
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return RelativeErrorTooSmall;
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/* tests for termination and stringent tolerances. */
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if (nfev >= maxfev)
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if (nfev >= parameters.maxfev)
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return TooManyFunctionEvaluation;
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if (ei_abs(actred) <= epsilon<Scalar>() && prered <= epsilon<Scalar>() && Scalar(.5) * ratio <= 1.)
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return FtolTooSmall;
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@ -379,6 +379,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiff(
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Matrix< Scalar, Dynamic, Dynamic > fjac(m, n);
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Matrix< Scalar, Dynamic, 1> diag, qtf;
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VectorXi ipvt;
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Parameters parameters;
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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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@ -386,14 +387,17 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiff(
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return ImproperInputParameters;
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}
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parameters.ftol = tol;
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parameters.xtol = tol;
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parameters.maxfev = 200*(n+1);
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return minimizeNumericalDiff(
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x,
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nfev,
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diag,
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parameters,
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1,
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100.,
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(n+1)*200,
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tol, tol, Scalar(0.), Scalar(0.)
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Scalar(0.)
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);
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}
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@ -403,12 +407,8 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiff(
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Matrix< Scalar, Dynamic, 1 > &x,
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int &nfev,
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Matrix< Scalar, Dynamic, 1 > &diag,
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const Parameters ¶meters,
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const int mode,
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const Scalar factor,
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const int maxfev,
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const Scalar ftol,
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const Scalar xtol,
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const Scalar gtol,
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const Scalar epsfcn
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)
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{
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@ -437,7 +437,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiff(
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/* check the input parameters for errors. */
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if (n <= 0 || m < n || ftol < 0. || xtol < 0. || gtol < 0. || maxfev <= 0 || factor <= 0.)
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if (n <= 0 || m < n || parameters.ftol < 0. || parameters.xtol < 0. || parameters.gtol < 0. || parameters.maxfev <= 0 || parameters.factor <= 0.)
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return RelativeErrorTooSmall;
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if (mode == 2)
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for (j = 0; j < n; ++j)
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@ -488,9 +488,9 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiff(
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wa3 = diag.cwise() * x;
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xnorm = wa3.stableNorm();
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delta = factor * xnorm;
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delta = parameters.factor * xnorm;
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if (delta == 0.)
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delta = factor;
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delta = parameters.factor;
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}
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/* form (q transpose)*fvec and store the first n components in */
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@ -527,7 +527,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiff(
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/* test for convergence of the gradient norm. */
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if (gnorm <= gtol)
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if (gnorm <= parameters.gtol)
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return CosinusTooSmall;
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/* rescale if necessary. */
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@ -621,16 +621,16 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiff(
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/* tests for convergence. */
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if (ei_abs(actred) <= ftol && prered <= ftol && Scalar(.5) * ratio <= 1. && delta <= xtol * xnorm)
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if (ei_abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. && delta <= parameters.xtol * xnorm)
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return RelativeErrorAndReductionTooSmall;
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if (ei_abs(actred) <= ftol && prered <= ftol && Scalar(.5) * ratio <= 1.)
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if (ei_abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.)
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return RelativeReductionTooSmall;
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if (delta <= xtol * xnorm)
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if (delta <= parameters.xtol * xnorm)
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return RelativeErrorTooSmall;
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/* tests for termination and stringent tolerances. */
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if (nfev >= maxfev)
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if (nfev >= parameters.maxfev)
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return TooManyFunctionEvaluation;
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if (ei_abs(actred) <= epsilon<Scalar>() && prered <= epsilon<Scalar>() && Scalar(.5) * ratio <= 1.)
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return FtolTooSmall;
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@ -660,6 +660,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
|
||||
Matrix< Scalar, Dynamic, Dynamic > fjac(m, n);
|
||||
Matrix< Scalar, Dynamic, 1> diag, qtf;
|
||||
VectorXi ipvt;
|
||||
Parameters parameters;
|
||||
|
||||
/* check the input parameters for errors. */
|
||||
if (n <= 0 || m < n || tol < 0.) {
|
||||
@ -667,14 +668,16 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
|
||||
return ImproperInputParameters;
|
||||
}
|
||||
|
||||
parameters.ftol = tol;
|
||||
parameters.xtol = tol;
|
||||
parameters.maxfev = 100*(n+1);
|
||||
|
||||
return minimizeOptimumStorage(
|
||||
x,
|
||||
nfev, njev,
|
||||
diag,
|
||||
1,
|
||||
100.,
|
||||
(n+1)*100,
|
||||
tol, tol, Scalar(0.)
|
||||
parameters,
|
||||
1
|
||||
);
|
||||
}
|
||||
|
||||
@ -685,12 +688,8 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
|
||||
int &nfev,
|
||||
int &njev,
|
||||
Matrix< Scalar, Dynamic, 1 > &diag,
|
||||
const int mode,
|
||||
const Scalar factor,
|
||||
const int maxfev,
|
||||
const Scalar ftol,
|
||||
const Scalar xtol,
|
||||
const Scalar gtol
|
||||
const Parameters ¶meters,
|
||||
const int mode
|
||||
)
|
||||
{
|
||||
const int n = x.size();
|
||||
@ -720,7 +719,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
|
||||
|
||||
/* check the input parameters for errors. */
|
||||
|
||||
if (n <= 0 || m < n || ftol < 0. || xtol < 0. || gtol < 0. || maxfev <= 0 || factor <= 0.)
|
||||
if (n <= 0 || m < n || parameters.ftol < 0. || parameters.xtol < 0. || parameters.gtol < 0. || parameters.maxfev <= 0 || parameters.factor <= 0.)
|
||||
return RelativeErrorTooSmall;
|
||||
|
||||
if (mode == 2)
|
||||
@ -805,9 +804,9 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
|
||||
|
||||
wa3 = diag.cwise() * x;
|
||||
xnorm = wa3.stableNorm();
|
||||
delta = factor * xnorm;
|
||||
delta = parameters.factor * xnorm;
|
||||
if (delta == 0.)
|
||||
delta = factor;
|
||||
delta = parameters.factor;
|
||||
}
|
||||
|
||||
/* compute the norm of the scaled gradient. */
|
||||
@ -827,7 +826,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
|
||||
|
||||
/* test for convergence of the gradient norm. */
|
||||
|
||||
if (gnorm <= gtol)
|
||||
if (gnorm <= parameters.gtol)
|
||||
return CosinusTooSmall;
|
||||
|
||||
/* rescale if necessary. */
|
||||
@ -921,16 +920,16 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
|
||||
|
||||
/* tests for convergence. */
|
||||
|
||||
if (ei_abs(actred) <= ftol && prered <= ftol && Scalar(.5) * ratio <= 1. && delta <= xtol * xnorm)
|
||||
if (ei_abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. && delta <= parameters.xtol * xnorm)
|
||||
return RelativeErrorAndReductionTooSmall;
|
||||
if (ei_abs(actred) <= ftol && prered <= ftol && Scalar(.5) * ratio <= 1.)
|
||||
if (ei_abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.)
|
||||
return RelativeReductionTooSmall;
|
||||
if (delta <= xtol * xnorm)
|
||||
if (delta <= parameters.xtol * xnorm)
|
||||
return RelativeErrorTooSmall;
|
||||
|
||||
/* tests for termination and stringent tolerances. */
|
||||
|
||||
if (nfev >= maxfev)
|
||||
if (nfev >= parameters.maxfev)
|
||||
return TooManyFunctionEvaluation;
|
||||
if (ei_abs(actred) <= epsilon<Scalar>() && prered <= epsilon<Scalar>() && Scalar(.5) * ratio <= 1.)
|
||||
return FtolTooSmall;
|
||||
|
||||
@ -181,7 +181,8 @@ void testLmder()
|
||||
// do the computation
|
||||
lmder_functor functor;
|
||||
LevenbergMarquardt<lmder_functor> lm(functor);
|
||||
info = lm.minimize(x, nfev, njev, diag);
|
||||
LevenbergMarquardt<lmder_functor>::Parameters parameters;
|
||||
info = lm.minimize(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return values
|
||||
VERIFY( 1 == info);
|
||||
@ -290,19 +291,19 @@ void testHybrj1()
|
||||
void testHybrj()
|
||||
{
|
||||
const int n=9;
|
||||
int info, nfev=0, njev=0, mode;
|
||||
int info, nfev=0, njev=0;
|
||||
VectorXd x(n), diag(n);
|
||||
|
||||
/* the following starting values provide a rough fit. */
|
||||
x.setConstant(n, -1.);
|
||||
|
||||
mode = 2;
|
||||
diag.setConstant(n, 1.);
|
||||
|
||||
// do the computation
|
||||
hybrj_functor functor;
|
||||
HybridNonLinearSolver<hybrj_functor> solver(functor);
|
||||
info = solver.solve(x, nfev, njev, diag, mode);
|
||||
HybridNonLinearSolver<hybrj_functor>::Parameters parameters;
|
||||
info = solver.solve(x, nfev, njev, diag, parameters, 2);
|
||||
|
||||
// check return value
|
||||
VERIFY( 1 == info);
|
||||
@ -372,7 +373,7 @@ void testHybrd1()
|
||||
void testHybrd()
|
||||
{
|
||||
const int n=9;
|
||||
int info, nfev=0, ml, mu, mode;
|
||||
int info, nfev=0, ml, mu;
|
||||
VectorXd x, diag(n);
|
||||
|
||||
/* the following starting values provide a rough fit. */
|
||||
@ -380,13 +381,13 @@ void testHybrd()
|
||||
|
||||
ml = 1;
|
||||
mu = 1;
|
||||
mode = 2;
|
||||
diag.setConstant(n, 1.);
|
||||
|
||||
// do the computation
|
||||
hybrd_functor functor;
|
||||
HybridNonLinearSolver<hybrd_functor> solver(functor);
|
||||
info = solver.solveNumericalDiff(x, nfev, diag, mode, ml, mu);
|
||||
HybridNonLinearSolver<hybrd_functor>::Parameters parameters;
|
||||
info = solver.solveNumericalDiff(x, nfev, diag, parameters, 2, ml, mu);
|
||||
|
||||
// check return value
|
||||
VERIFY( 1 == info);
|
||||
@ -484,7 +485,8 @@ void testLmstr()
|
||||
// do the computation
|
||||
lmstr_functor functor;
|
||||
LevenbergMarquardt<lmstr_functor> lm(functor);
|
||||
info = lm.minimizeOptimumStorage(x, nfev, njev, diag);
|
||||
LevenbergMarquardt<lmstr_functor>::Parameters parameters;
|
||||
info = lm.minimizeOptimumStorage(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return values
|
||||
VERIFY( 1 == info);
|
||||
@ -570,7 +572,8 @@ void testLmdif()
|
||||
// do the computation
|
||||
lmdif_functor functor;
|
||||
LevenbergMarquardt<lmdif_functor> lm(functor);
|
||||
info = lm.minimizeNumericalDiff(x, nfev, diag);
|
||||
LevenbergMarquardt<lmdif_functor>::Parameters parameters;
|
||||
info = lm.minimizeNumericalDiff(x, nfev, diag, parameters);
|
||||
|
||||
// check return values
|
||||
VERIFY( 1 == info);
|
||||
@ -655,7 +658,8 @@ void testNistChwirut2(void)
|
||||
// do the computation
|
||||
chwirut2_functor functor;
|
||||
LevenbergMarquardt<chwirut2_functor> lm(functor);
|
||||
info = lm.minimize(x, nfev, njev, diag);
|
||||
LevenbergMarquardt<chwirut2_functor>::Parameters parameters;
|
||||
info = lm.minimize(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return value
|
||||
VERIFY( 1 == info);
|
||||
@ -673,8 +677,10 @@ void testNistChwirut2(void)
|
||||
*/
|
||||
x<< 0.15, 0.008, 0.010;
|
||||
// do the computation
|
||||
info = lm.minimize(x, nfev, njev, diag,
|
||||
1, 100., 400, 1.E6*epsilon<double>(), 1.E6*epsilon<double>());
|
||||
parameters = LevenbergMarquardt<chwirut2_functor>::Parameters(); // get default back
|
||||
parameters.ftol = 1.E6*epsilon<double>();
|
||||
parameters.xtol = 1.E6*epsilon<double>();
|
||||
info = lm.minimize(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return value
|
||||
VERIFY( 1 == info);
|
||||
@ -733,7 +739,8 @@ void testNistMisra1a(void)
|
||||
// do the computation
|
||||
misra1a_functor functor;
|
||||
LevenbergMarquardt<misra1a_functor> lm(functor);
|
||||
info = lm.minimize(x, nfev, njev, diag);
|
||||
LevenbergMarquardt<misra1a_functor>::Parameters parameters;
|
||||
info = lm.minimize(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return value
|
||||
VERIFY( 1 == info);
|
||||
@ -750,7 +757,7 @@ void testNistMisra1a(void)
|
||||
*/
|
||||
x<< 250., 0.0005;
|
||||
// do the computation
|
||||
info = lm.minimize(x, nfev, njev, diag);
|
||||
info = lm.minimize(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return value
|
||||
VERIFY( 1 == info);
|
||||
@ -819,7 +826,8 @@ void testNistHahn1(void)
|
||||
// do the computation
|
||||
hahn1_functor functor;
|
||||
LevenbergMarquardt<hahn1_functor> lm(functor);
|
||||
info = lm.minimize(x, nfev, njev, diag);
|
||||
LevenbergMarquardt<hahn1_functor>::Parameters parameters;
|
||||
info = lm.minimize(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return value
|
||||
VERIFY( 1 == info);
|
||||
@ -841,7 +849,7 @@ void testNistHahn1(void)
|
||||
*/
|
||||
x<< .1, -.1, .005, -.000001, -.005, .0001, -.0000001;
|
||||
// do the computation
|
||||
info = lm.minimize(x, nfev, njev, diag);
|
||||
info = lm.minimize(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return value
|
||||
VERIFY( 1 == info);
|
||||
@ -905,7 +913,8 @@ void testNistMisra1d(void)
|
||||
// do the computation
|
||||
misra1d_functor functor;
|
||||
LevenbergMarquardt<misra1d_functor> lm(functor);
|
||||
info = lm.minimize(x, nfev, njev, diag);
|
||||
LevenbergMarquardt<misra1d_functor>::Parameters parameters;
|
||||
info = lm.minimize(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return value
|
||||
VERIFY( 3 == info);
|
||||
@ -922,7 +931,7 @@ void testNistMisra1d(void)
|
||||
*/
|
||||
x<< 450., 0.0003;
|
||||
// do the computation
|
||||
info = lm.minimize(x, nfev, njev, diag);
|
||||
info = lm.minimize(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return value
|
||||
VERIFY( 1 == info);
|
||||
@ -983,7 +992,8 @@ void testNistLanczos1(void)
|
||||
// do the computation
|
||||
lanczos1_functor functor;
|
||||
LevenbergMarquardt<lanczos1_functor> lm(functor);
|
||||
info = lm.minimize(x, nfev, njev, diag);
|
||||
LevenbergMarquardt<lanczos1_functor>::Parameters parameters;
|
||||
info = lm.minimize(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return value
|
||||
VERIFY( 2 == info);
|
||||
@ -1004,7 +1014,7 @@ void testNistLanczos1(void)
|
||||
*/
|
||||
x<< 0.5, 0.7, 3.6, 4.2, 4., 6.3;
|
||||
// do the computation
|
||||
info = lm.minimize(x, nfev, njev, diag);
|
||||
info = lm.minimize(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return value
|
||||
VERIFY( 2 == info);
|
||||
@ -1069,7 +1079,8 @@ void testNistRat42(void)
|
||||
// do the computation
|
||||
rat42_functor functor;
|
||||
LevenbergMarquardt<rat42_functor> lm(functor);
|
||||
info = lm.minimize(x, nfev, njev, diag);
|
||||
LevenbergMarquardt<rat42_functor>::Parameters parameters;
|
||||
info = lm.minimize(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return value
|
||||
VERIFY( 1 == info);
|
||||
@ -1087,7 +1098,7 @@ void testNistRat42(void)
|
||||
*/
|
||||
x<< 75., 2.5, 0.07;
|
||||
// do the computation
|
||||
info = lm.minimize(x, nfev, njev, diag);
|
||||
info = lm.minimize(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return value
|
||||
VERIFY( 1 == info);
|
||||
@ -1147,7 +1158,8 @@ void testNistMGH10(void)
|
||||
// do the computation
|
||||
MGH10_functor functor;
|
||||
LevenbergMarquardt<MGH10_functor> lm(functor);
|
||||
info = lm.minimize(x, nfev, njev, diag);
|
||||
LevenbergMarquardt<MGH10_functor>::Parameters parameters;
|
||||
info = lm.minimize(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return value
|
||||
VERIFY( 2 == info);
|
||||
@ -1165,7 +1177,7 @@ void testNistMGH10(void)
|
||||
*/
|
||||
x<< 0.02, 4000., 250.;
|
||||
// do the computation
|
||||
info = lm.minimize(x, nfev, njev, diag);
|
||||
info = lm.minimize(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return value
|
||||
VERIFY( 2 == info);
|
||||
@ -1223,8 +1235,11 @@ void testNistBoxBOD(void)
|
||||
// do the computation
|
||||
BoxBOD_functor functor;
|
||||
LevenbergMarquardt<BoxBOD_functor> lm(functor);
|
||||
info = lm.minimize(x, nfev, njev, diag,
|
||||
1, 10., 400, 1E6*epsilon<double>(), 1E6*epsilon<double>());
|
||||
LevenbergMarquardt<BoxBOD_functor>::Parameters parameters;
|
||||
parameters.ftol = 1.E6*epsilon<double>();
|
||||
parameters.xtol = 1.E6*epsilon<double>();
|
||||
parameters.factor = 10.;
|
||||
info = lm.minimize(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return value
|
||||
VERIFY( 1 == info);
|
||||
@ -1241,8 +1256,10 @@ void testNistBoxBOD(void)
|
||||
*/
|
||||
x<< 100., 0.75;
|
||||
// do the computation
|
||||
info = lm.minimize(x, nfev, njev, diag,
|
||||
1, 100., 14000, epsilon<double>(), epsilon<double>());
|
||||
parameters = LevenbergMarquardt<BoxBOD_functor>::Parameters(); // get default back
|
||||
parameters.ftol = epsilon<double>();
|
||||
parameters.xtol = epsilon<double>();
|
||||
info = lm.minimize(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return value
|
||||
VERIFY( 1 == info);
|
||||
@ -1301,8 +1318,11 @@ void testNistMGH17(void)
|
||||
// do the computation
|
||||
MGH17_functor functor;
|
||||
LevenbergMarquardt<MGH17_functor> lm(functor);
|
||||
info = lm.minimize(x, nfev, njev, diag,
|
||||
1, 100., 5000, epsilon<double>(), epsilon<double>());
|
||||
LevenbergMarquardt<MGH17_functor>::Parameters parameters;
|
||||
parameters.ftol = epsilon<double>();
|
||||
parameters.xtol = epsilon<double>();
|
||||
parameters.maxfev = 1000;
|
||||
info = lm.minimize(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return value
|
||||
VERIFY( 1 == info);
|
||||
@ -1322,7 +1342,8 @@ void testNistMGH17(void)
|
||||
*/
|
||||
x<< 0.5 ,1.5 ,-1 ,0.01 ,0.02;
|
||||
// do the computation
|
||||
info = lm.minimize(x, nfev, njev, diag);
|
||||
parameters = LevenbergMarquardt<MGH17_functor>::Parameters(); // get default back
|
||||
info = lm.minimize(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return value
|
||||
VERIFY( 1 == info);
|
||||
@ -1387,8 +1408,9 @@ void testNistMGH09(void)
|
||||
// do the computation
|
||||
MGH09_functor functor;
|
||||
LevenbergMarquardt<MGH09_functor> lm(functor);
|
||||
info = lm.minimize(x, nfev, njev, diag,
|
||||
1, 100., 5000);
|
||||
LevenbergMarquardt<MGH09_functor>::Parameters parameters;
|
||||
parameters.maxfev = 1000;
|
||||
info = lm.minimize(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return value
|
||||
VERIFY( 1 == info);
|
||||
@ -1407,7 +1429,8 @@ void testNistMGH09(void)
|
||||
*/
|
||||
x<< 0.25, 0.39, 0.415, 0.39;
|
||||
// do the computation
|
||||
info = lm.minimize(x, nfev, njev, diag);
|
||||
parameters = LevenbergMarquardt<MGH09_functor>::Parameters();
|
||||
info = lm.minimize(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return value
|
||||
VERIFY( 1 == info);
|
||||
@ -1469,7 +1492,9 @@ void testNistBennett5(void)
|
||||
// do the computation
|
||||
Bennett5_functor functor;
|
||||
LevenbergMarquardt<Bennett5_functor> lm(functor);
|
||||
info = lm.minimize(x, nfev, njev, diag, 1, 100., 5000);
|
||||
LevenbergMarquardt<Bennett5_functor>::Parameters parameters;
|
||||
parameters.maxfev = 1000;
|
||||
info = lm.minimize(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return value
|
||||
VERIFY( 1 == info);
|
||||
@ -1486,7 +1511,8 @@ void testNistBennett5(void)
|
||||
*/
|
||||
x<< -1500., 45., 0.85;
|
||||
// do the computation
|
||||
info = lm.minimize(x, nfev, njev, diag);
|
||||
parameters = LevenbergMarquardt<Bennett5_functor>::Parameters();
|
||||
info = lm.minimize(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return value
|
||||
VERIFY( 1 == info);
|
||||
@ -1554,8 +1580,10 @@ void testNistThurber(void)
|
||||
// do the computation
|
||||
thurber_functor functor;
|
||||
LevenbergMarquardt<thurber_functor> lm(functor);
|
||||
info = lm.minimize(x, nfev, njev, diag,
|
||||
1, 100., 400, 1.E4*epsilon<double>(), 1.E4*epsilon<double>());
|
||||
LevenbergMarquardt<thurber_functor>::Parameters parameters;
|
||||
parameters.ftol = 1.E4*epsilon<double>();
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parameters.xtol = 1.E4*epsilon<double>();
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info = lm.minimize(x, nfev, njev, diag, parameters);
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||||
|
||||
// check return value
|
||||
VERIFY( 1 == info);
|
||||
@ -1577,8 +1605,10 @@ void testNistThurber(void)
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||||
*/
|
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x<< 1300 ,1500 ,500 ,75 ,1 ,0.4 ,0.05 ;
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// do the computation
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||||
info = lm.minimize(x, nfev, njev, diag,
|
||||
1, 100., 400, 1.E4*epsilon<double>(), 1.E4*epsilon<double>());
|
||||
parameters = LevenbergMarquardt<thurber_functor>::Parameters();
|
||||
parameters.ftol = 1.E4*epsilon<double>();
|
||||
parameters.xtol = 1.E4*epsilon<double>();
|
||||
info = lm.minimize(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return value
|
||||
VERIFY( 1 == info);
|
||||
@ -1643,8 +1673,10 @@ void testNistRat43(void)
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||||
// do the computation
|
||||
rat43_functor functor;
|
||||
LevenbergMarquardt<rat43_functor> lm(functor);
|
||||
info = lm.minimize(x, nfev, njev, diag,
|
||||
1, 100., 400, 1.E6*epsilon<double>(), 1.E6*epsilon<double>());
|
||||
LevenbergMarquardt<rat43_functor>::Parameters parameters;
|
||||
parameters.ftol = 1.E6*epsilon<double>();
|
||||
parameters.xtol = 1.E6*epsilon<double>();
|
||||
info = lm.minimize(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return value
|
||||
VERIFY( 1 == info);
|
||||
@ -1663,8 +1695,10 @@ void testNistRat43(void)
|
||||
*/
|
||||
x<< 700., 5., 0.75, 1.3;
|
||||
// do the computation
|
||||
info = lm.minimize(x, nfev, njev, diag,
|
||||
1, 100., 400, 1.E5*epsilon<double>(), 1.E5*epsilon<double>());
|
||||
parameters = LevenbergMarquardt<rat43_functor>::Parameters(); // get default back
|
||||
parameters.ftol = 1.E5*epsilon<double>();
|
||||
parameters.xtol = 1.E5*epsilon<double>();
|
||||
info = lm.minimize(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return value
|
||||
VERIFY( 1 == info);
|
||||
@ -1727,7 +1761,8 @@ void testNistEckerle4(void)
|
||||
// do the computation
|
||||
eckerle4_functor functor;
|
||||
LevenbergMarquardt<eckerle4_functor> lm(functor);
|
||||
info = lm.minimize(x, nfev, njev, diag);
|
||||
LevenbergMarquardt<eckerle4_functor>::Parameters parameters;
|
||||
info = lm.minimize(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return value
|
||||
VERIFY( 1 == info);
|
||||
@ -1745,7 +1780,7 @@ void testNistEckerle4(void)
|
||||
*/
|
||||
x<< 1.5, 5., 450.;
|
||||
// do the computation
|
||||
info = lm.minimize(x, nfev, njev, diag);
|
||||
info = lm.minimize(x, nfev, njev, diag, parameters);
|
||||
|
||||
// check return value
|
||||
VERIFY( 1 == info);
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user