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Thomas Capricelli
parent
113995355b
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d791b51112
@ -429,7 +429,6 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffInit(
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/* Function Body */
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nfev = 0;
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njev = 0;
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@ -207,7 +207,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeInit(
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njev = 0;
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/* check the input parameters for errors. */
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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 ImproperInputParameters;
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@ -218,14 +217,12 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeInit(
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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 UserAsked;
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fnorm = fvec.stableNorm();
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/* initialize levenberg-marquardt parameter and iteration counter. */
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par = 0.;
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iter = 1;
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@ -242,7 +239,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
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int i, j, l;
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/* calculate the jacobian matrix. */
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int df_ret = functor.df(x, fjac);
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if (df_ret<0)
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return UserAsked;
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@ -252,7 +248,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
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else njev++;
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/* compute the qr factorization of the jacobian. */
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wa2 = fjac.colwise().blueNorm();
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ColPivHouseholderQR<JacobianType> qrfac(fjac);
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fjac = qrfac.matrixQR();
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@ -264,7 +259,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
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/* on the first iteration and if mode is 1, scale according */
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/* to the norms of the columns of the initial jacobian. */
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if (iter == 1) {
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if (mode != 2)
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for (j = 0; j < n; ++j) {
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@ -275,7 +269,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
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/* on the first iteration, calculate the norm of the scaled x */
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/* and initialize the step bound delta. */
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wa3 = diag.cwiseProduct(x);
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xnorm = wa3.stableNorm();
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delta = parameters.factor * xnorm;
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@ -285,7 +278,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
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/* form (q transpose)*fvec and store the first n components in */
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/* qtf. */
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#if 0
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// find a way to only compute the first n items, we have m>>n here.
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wa4 = fvec;
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@ -309,7 +301,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
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#endif
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/* compute the norm of the scaled gradient. */
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gnorm = 0.;
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if (fnorm != 0.)
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for (j = 0; j < n; ++j) {
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@ -324,12 +315,10 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
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}
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/* test for convergence of the gradient norm. */
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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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if (mode != 2) /* Computing MAX */
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diag = diag.cwiseMax(wa2);
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@ -337,37 +326,31 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
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do {
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/* determine the levenberg-marquardt parameter. */
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ei_lmpar2<Scalar>(qrfac, diag, qtf, delta, par, wa1);
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/* store the direction p and x + p. calculate the norm of p. */
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wa1 = -wa1;
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wa2 = x + wa1;
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wa3 = diag.cwiseProduct(wa1);
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pnorm = wa3.stableNorm();
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/* on the first iteration, adjust the initial step bound. */
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if (iter == 1)
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delta = std::min(delta,pnorm);
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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 UserAsked;
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++nfev;
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fnorm1 = wa4.stableNorm();
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/* compute the scaled actual reduction. */
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actred = -1.;
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if (Scalar(.1) * fnorm1 < fnorm) /* Computing 2nd power */
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actred = 1. - ei_abs2(fnorm1 / fnorm);
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/* compute the scaled predicted reduction and */
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/* the scaled directional derivative. */
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wa3.fill(0.);
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for (j = 0; j < n; ++j) {
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l = ipvt[j];
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@ -383,13 +366,11 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
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/* compute the ratio of the actual to the predicted */
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/* reduction. */
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ratio = 0.;
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if (prered != 0.)
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ratio = actred / prered;
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/* update the step bound. */
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if (ratio <= Scalar(.25)) {
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if (actred >= 0.)
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temp = Scalar(.5);
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@ -406,7 +387,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
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}
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/* test for successful iteration. */
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if (ratio >= Scalar(1e-4)) {
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/* successful iteration. update x, fvec, and their norms. */
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x = wa2;
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@ -418,7 +398,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
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}
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/* tests for convergence. */
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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) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.)
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@ -427,7 +406,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
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return 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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if (ei_abs(actred) <= epsilon<Scalar>() && prered <= epsilon<Scalar>() && Scalar(.5) * ratio <= 1.)
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@ -489,7 +467,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageInit(
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njev = 0;
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/* check the input parameters for errors. */
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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 ImproperInputParameters;
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@ -500,14 +477,12 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageInit(
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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 UserAsked;
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fnorm = fvec.stableNorm();
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/* initialize levenberg-marquardt parameter and iteration counter. */
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par = 0.;
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iter = 1;
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@ -529,7 +504,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
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/* calculated one row at a time, while simultaneously */
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/* forming (q transpose)*fvec and storing the first */
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/* n components in qtf. */
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qtf.fill(0.);
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fjac.fill(0.);
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int rownb = 2;
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@ -543,7 +517,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
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/* if the jacobian is rank deficient, call qrfac to */
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/* reorder its columns and update the components of qtf. */
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sing = false;
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for (j = 0; j < n; ++j) {
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if (fjac(j,j) == 0.) {
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@ -578,7 +551,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
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/* on the first iteration and if mode is 1, scale according */
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/* to the norms of the columns of the initial jacobian. */
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if (iter == 1) {
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if (mode != 2)
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for (j = 0; j < n; ++j) {
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@ -589,7 +561,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
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/* on the first iteration, calculate the norm of the scaled x */
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/* and initialize the step bound delta. */
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wa3 = diag.cwiseProduct(x);
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xnorm = wa3.stableNorm();
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delta = parameters.factor * xnorm;
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@ -598,7 +569,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
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}
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/* compute the norm of the scaled gradient. */
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gnorm = 0.;
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if (fnorm != 0.)
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for (j = 0; j < n; ++j) {
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@ -613,12 +583,10 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
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}
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/* test for convergence of the gradient norm. */
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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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if (mode != 2) /* Computing MAX */
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diag = diag.cwiseMax(wa2);
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@ -626,37 +594,31 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
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do {
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/* determine the levenberg-marquardt parameter. */
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ei_lmpar<Scalar>(fjac, ipvt, diag, qtf, delta, par, wa1);
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/* store the direction p and x + p. calculate the norm of p. */
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wa1 = -wa1;
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wa2 = x + wa1;
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wa3 = diag.cwiseProduct(wa1);
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pnorm = wa3.stableNorm();
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/* on the first iteration, adjust the initial step bound. */
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if (iter == 1)
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delta = std::min(delta,pnorm);
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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 UserAsked;
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++nfev;
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fnorm1 = wa4.stableNorm();
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/* compute the scaled actual reduction. */
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actred = -1.;
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if (Scalar(.1) * fnorm1 < fnorm) /* Computing 2nd power */
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actred = 1. - ei_abs2(fnorm1 / fnorm);
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/* compute the scaled predicted reduction and */
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/* the scaled directional derivative. */
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wa3.fill(0.);
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for (j = 0; j < n; ++j) {
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l = ipvt[j];
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@ -672,13 +634,11 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
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/* compute the ratio of the actual to the predicted */
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/* reduction. */
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ratio = 0.;
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if (prered != 0.)
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ratio = actred / prered;
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/* update the step bound. */
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if (ratio <= Scalar(.25)) {
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if (actred >= 0.)
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temp = Scalar(.5);
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@ -695,7 +655,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
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}
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/* test for successful iteration. */
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if (ratio >= Scalar(1e-4)) {
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/* successful iteration. update x, fvec, and their norms. */
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x = wa2;
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@ -707,7 +666,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
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}
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/* tests for convergence. */
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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) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.)
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@ -716,7 +674,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
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return 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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if (ei_abs(actred) <= epsilon<Scalar>() && prered <= epsilon<Scalar>() && Scalar(.5) * ratio <= 1.)
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@ -30,7 +30,6 @@ void ei_lmpar(
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/* compute and store in x the gauss-newton direction. if the */
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/* jacobian is rank-deficient, obtain a least squares solution. */
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int nsing = n-1;
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wa1 = qtb;
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for (j = 0; j < n; ++j) {
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@ -52,7 +51,6 @@ void ei_lmpar(
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/* initialize the iteration counter. */
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/* evaluate the function at the origin, and test */
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/* for acceptance of the gauss-newton direction. */
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iter = 0;
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wa2 = diag.cwiseProduct(x);
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dxnorm = wa2.blueNorm();
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@ -65,7 +63,6 @@ void ei_lmpar(
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/* if the jacobian is not rank deficient, the newton */
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/* step provides a lower bound, parl, for the zero of */
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/* the function. otherwise set this bound to zero. */
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parl = 0.;
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if (nsing >= n-1) {
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for (j = 0; j < n; ++j) {
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@ -85,7 +82,6 @@ void ei_lmpar(
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}
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/* calculate an upper bound, paru, for the zero of the function. */
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for (j = 0; j < n; ++j)
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wa1[j] = r.col(j).head(j+1).dot(qtb.head(j+1)) / diag[ipvt[j]];
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@ -96,22 +92,18 @@ void ei_lmpar(
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/* if the input par lies outside of the interval (parl,paru), */
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/* set par to the closer endpoint. */
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par = std::max(par,parl);
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par = std::min(par,paru);
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if (par == 0.)
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par = gnorm / dxnorm;
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/* beginning of an iteration. */
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while (true) {
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++iter;
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/* evaluate the function at the current value of par. */
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if (par == 0.)
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par = std::max(dwarf,Scalar(.001) * paru); /* Computing MAX */
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wa1 = ei_sqrt(par)* diag;
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Matrix< Scalar, Dynamic, 1 > sdiag(n);
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@ -125,12 +117,10 @@ void ei_lmpar(
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/* if the function is small enough, accept the current value */
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/* of par. also test for the exceptional cases where parl */
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/* is zero or the number of iterations has reached 10. */
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if (ei_abs(fp) <= Scalar(0.1) * delta || (parl == 0. && fp <= temp && temp < 0.) || iter == 10)
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break;
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/* compute the newton correction. */
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for (j = 0; j < n; ++j) {
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l = ipvt[j];
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wa1[j] = diag[l] * (wa2[l] / dxnorm);
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@ -145,23 +135,19 @@ void ei_lmpar(
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parc = fp / delta / temp / temp;
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/* depending on the sign of the function, update parl or paru. */
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if (fp > 0.)
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parl = std::max(parl,par);
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if (fp < 0.)
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paru = std::min(paru,par);
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/* compute an improved estimate for par. */
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/* Computing MAX */
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par = std::max(parl,par+parc);
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/* end of an iteration. */
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}
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/* termination. */
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if (iter == 0)
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par = 0.;
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return;
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@ -198,7 +184,6 @@ void ei_lmpar2(
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/* compute and store in x the gauss-newton direction. if the */
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/* jacobian is rank-deficient, obtain a least squares solution. */
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// const int rank = qr.nonzeroPivots(); // exactly double(0.)
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const int rank = qr.rank(); // use a threshold
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wa1 = qtb; wa1.segment(rank,n-rank).setZero();
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@ -209,7 +194,6 @@ void ei_lmpar2(
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/* initialize the iteration counter. */
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/* evaluate the function at the origin, and test */
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/* for acceptance of the gauss-newton direction. */
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iter = 0;
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wa2 = diag.cwiseProduct(x);
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dxnorm = wa2.blueNorm();
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@ -222,7 +206,6 @@ void ei_lmpar2(
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/* if the jacobian is not rank deficient, the newton */
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/* step provides a lower bound, parl, for the zero of */
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/* the function. otherwise set this bound to zero. */
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parl = 0.;
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if (rank==n) {
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wa1 = qr.colsPermutation().inverse() * diag.cwiseProduct(wa2)/dxnorm;
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@ -232,7 +215,6 @@ void ei_lmpar2(
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}
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/* calculate an upper bound, paru, for the zero of the function. */
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for (j = 0; j < n; ++j)
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wa1[j] = qr.matrixQR().col(j).head(j+1).dot(qtb.head(j+1)) / diag[qr.colsPermutation().indices()(j)];
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@ -243,23 +225,19 @@ void ei_lmpar2(
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/* if the input par lies outside of the interval (parl,paru), */
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/* set par to the closer endpoint. */
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par = std::max(par,parl);
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par = std::min(par,paru);
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if (par == 0.)
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par = gnorm / dxnorm;
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/* beginning of an iteration. */
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Matrix< Scalar, Dynamic, Dynamic > s = qr.matrixQR();
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while (true) {
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++iter;
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/* evaluate the function at the current value of par. */
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if (par == 0.)
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par = std::max(dwarf,Scalar(.001) * paru); /* Computing MAX */
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wa1 = ei_sqrt(par)* diag;
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Matrix< Scalar, Dynamic, 1 > sdiag(n);
|
||||
@ -273,12 +251,10 @@ void ei_lmpar2(
|
||||
/* if the function is small enough, accept the current value */
|
||||
/* of par. also test for the exceptional cases where parl */
|
||||
/* is zero or the number of iterations has reached 10. */
|
||||
|
||||
if (ei_abs(fp) <= Scalar(0.1) * delta || (parl == 0. && fp <= temp && temp < 0.) || iter == 10)
|
||||
break;
|
||||
|
||||
/* compute the newton correction. */
|
||||
|
||||
wa1 = qr.colsPermutation().inverse() * diag.cwiseProduct(wa2/dxnorm);
|
||||
for (j = 0; j < n; ++j) {
|
||||
wa1[j] /= sdiag[j];
|
||||
@ -290,7 +266,6 @@ void ei_lmpar2(
|
||||
parc = fp / delta / temp / temp;
|
||||
|
||||
/* depending on the sign of the function, update parl or paru. */
|
||||
|
||||
if (fp > 0.)
|
||||
parl = std::max(parl,par);
|
||||
if (fp < 0.)
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user