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some more eigenization

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This commit is contained in:
Thomas Capricelli
2010-01-26 07:36:15 +01:00
parent a3034ee079
commit ba2a9cce03
2 changed files with 39 additions and 131 deletions
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132
unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
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@@ -186,7 +186,6 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveInit(
njev = 0;
/* check the input parameters for errors. */
if (n <= 0 || parameters.xtol < 0. || parameters.maxfev <= 0 || parameters.factor <= 0. )
return ImproperInputParameters;
if (mode == 2)
@@ -196,14 +195,12 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveInit(
/* evaluate the function at the starting point */
/* and calculate its norm. */
nfev = 1;
if ( functor(x, fvec) < 0)
return UserAksed;
fnorm = fvec.stableNorm();
/* initialize iteration counter and monitors. */
iter = 1;
ncsuc = 0;
ncfail = 0;
@@ -224,7 +221,6 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
jeval = true;
/* calculate the jacobian matrix. */
if ( functor.df(x, fjac) < 0)
return UserAksed;
++njev;
@@ -235,11 +231,8 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
/* to the norms of the columns of the initial jacobian. */
if (iter == 1) {
if (mode != 2)
for (j = 0; j < n; ++j) {
diag[j] = wa2[j];
if (wa2[j] == 0.)
diag[j] = 1.;
}
for (j = 0; j < n; ++j)
diag[j] = (wa2[j]==0.) ? 1. : wa2[j];
/* on the first iteration, calculate the norm of the scaled x */
/* and initialize the step bound delta. */
@@ -260,7 +253,6 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
for(int ii=0; ii< fjac.cols(); ii++) fjac.col(ii).segment(ii+1, fjac.rows()-ii-1) *= fjac(ii,ii); // rescale vectors
/* form (q transpose)*fvec and store in qtf. */
qtf = fvec;
for (j = 0; j < n; ++j)
if (fjac(j,j) != 0.) {
@@ -273,76 +265,54 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
}
/* copy the triangular factor of the qr factorization into r. */
R = qrfac.matrixQR();
sing = false;
for (j = 0; j < n; ++j)
if (wa1[j] == 0.) sing = true;
sing = wa1.cwiseAbs().minCoeff()==0.;
/* accumulate the orthogonal factor in fjac. */
ei_qform<Scalar>(n, n, fjac.data(), fjac.rows(), wa1.data());
/* rescale if necessary. */
/* Computing MAX */
if (mode != 2)
diag = diag.cwiseMax(wa2);
/* beginning of the inner loop. */
while (true) {
/* determine the direction p. */
ei_dogleg<Scalar>(R, diag, qtf, delta, wa1);
/* store the direction p and x + p. calculate the norm of p. */
wa1 = -wa1;
wa2 = x + wa1;
wa3 = diag.cwiseProduct(wa1);
pnorm = wa3.stableNorm();
/* on the first iteration, adjust the initial step bound. */
if (iter == 1)
delta = std::min(delta,pnorm);
/* evaluate the function at x + p and calculate its norm. */
if ( functor(wa2, wa4) < 0)
return UserAksed;
++nfev;
fnorm1 = wa4.stableNorm();
/* compute the scaled actual reduction. */
actred = -1.;
if (fnorm1 < fnorm) /* Computing 2nd power */
actred = 1. - ei_abs2(fnorm1 / fnorm);
/* compute the scaled predicted reduction. */
for (i = 0; i < n; ++i) {
sum = 0.;
for (j = i; j < n; ++j)
sum += R(i,j) * wa1[j];
wa3[i] = qtf[i] + sum;
}
wa3 = R.template triangularView<Upper>()*wa1 + qtf;
temp = wa3.stableNorm();
prered = 0.;
if (temp < fnorm) /* Computing 2nd power */
prered = 1. - ei_abs2(temp / fnorm);
/* compute the ratio of the actual to the predicted */
/* reduction. */
/* compute the ratio of the actual to the predicted reduction. */
ratio = 0.;
if (prered > 0.)
ratio = actred / prered;
/* update the step bound. */
if (ratio < Scalar(.1)) {
ncsuc = 0;
++ncfail;
@@ -350,7 +320,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
} else {
ncfail = 0;
++ncsuc;
if (ratio >= Scalar(.5) || ncsuc > 1) /* Computing MAX */
if (ratio >= Scalar(.5) || ncsuc > 1)
delta = std::max(delta, pnorm / Scalar(.5));
if (ei_abs(ratio - 1.) <= Scalar(.1)) {
delta = pnorm / Scalar(.5);
@@ -358,7 +328,6 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
}
/* test for successful iteration. */
if (ratio >= Scalar(1e-4)) {
/* successful iteration. update x, fvec, and their norms. */
x = wa2;
@@ -370,7 +339,6 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
}
/* determine the progress of the iteration. */
++nslow1;
if (actred >= Scalar(.001))
nslow1 = 0;
@@ -380,12 +348,10 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
nslow2 = 0;
/* test for convergence. */
if (delta <= parameters.xtol * xnorm || fnorm == 0.)
return RelativeErrorTooSmall;
/* tests for termination and stringent tolerances. */
if (nfev >= parameters.maxfev)
return TooManyFunctionEvaluation;
if (Scalar(.1) * std::max(Scalar(.1) * delta, pnorm) <= epsilon<Scalar>() * xnorm)
@@ -396,37 +362,27 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
return NotMakingProgressIterations;
/* criterion for recalculating jacobian. */
if (ncfail == 2)
break; // leave inner loop and go for the next outer loop iteration
/* calculate the rank one modification to the jacobian */
/* and update qtf if necessary. */
for (j = 0; j < n; ++j) {
sum = wa4.dot(fjac.col(j));
wa2[j] = (sum - wa3[j]) / pnorm;
wa1[j] = diag[j] * (diag[j] * wa1[j] / pnorm);
if (ratio >= Scalar(1e-4))
qtf[j] = sum;
}
wa1 = diag.cwiseProduct( diag.cwiseProduct(wa1)/pnorm );
wa2 = fjac.transpose() * wa4;
if (ratio >= Scalar(1e-4))
qtf = wa2;
wa2 = (wa2-wa3)/pnorm;
/* compute the qr factorization of the updated jacobian. */
ei_r1updt<Scalar>(n, n, R, wa1.data(), wa2.data(), wa3.data(), &sing);
ei_r1mpyq<Scalar>(n, n, fjac.data(), fjac.rows(), wa2.data(), wa3.data());
ei_r1mpyq<Scalar>(1, n, qtf.data(), 1, wa2.data(), wa3.data());
/* end of the inner loop. */
jeval = false;
}
/* end of the outer loop. */
return Running;
}
template<typename FunctorType, typename Scalar>
typename HybridNonLinearSolver<FunctorType,Scalar>::Status
HybridNonLinearSolver<FunctorType,Scalar>::solve(
@@ -493,7 +449,6 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffInit(
njev = 0;
/* check the input parameters for errors. */
if (n <= 0 || parameters.xtol < 0. || parameters.maxfev <= 0 || parameters.nb_of_subdiagonals< 0 || parameters.nb_of_superdiagonals< 0 || parameters.factor <= 0. )
return ImproperInputParameters;
if (mode == 2)
@@ -503,14 +458,12 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffInit(
/* evaluate the function at the starting point */
/* and calculate its norm. */
nfev = 1;
if ( functor(x, fvec) < 0)
return UserAksed;
fnorm = fvec.stableNorm();
/* initialize iteration counter and monitors. */
iter = 1;
ncsuc = 0;
ncfail = 0;
@@ -544,11 +497,8 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
/* to the norms of the columns of the initial jacobian. */
if (iter == 1) {
if (mode != 2)
for (j = 0; j < n; ++j) {
diag[j] = wa2[j];
if (wa2[j] == 0.)
diag[j] = 1.;
}
for (j = 0; j < n; ++j)
diag[j] = (wa2[j]==0.) ? 1. : wa2[j];
/* on the first iteration, calculate the norm of the scaled x */
/* and initialize the step bound delta. */
@@ -569,7 +519,6 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
for(int ii=0; ii< fjac.cols(); ii++) fjac.col(ii).segment(ii+1, fjac.rows()-ii-1) *= fjac(ii,ii); // rescale vectors
/* form (q transpose)*fvec and store in qtf. */
qtf = fvec;
for (j = 0; j < n; ++j)
if (fjac(j,j) != 0.) {
@@ -583,74 +532,53 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
/* copy the triangular factor of the qr factorization into r. */
R = qrfac.matrixQR();
sing = false;
for (j = 0; j < n; ++j)
if (wa1[j] == 0.) sing = true;
sing = wa1.cwiseAbs().minCoeff()==0.;
/* accumulate the orthogonal factor in fjac. */
ei_qform<Scalar>(n, n, fjac.data(), fjac.rows(), wa1.data());
/* rescale if necessary. */
/* Computing MAX */
if (mode != 2)
diag = diag.cwiseMax(wa2);
/* beginning of the inner loop. */
while (true) {
/* determine the direction p. */
ei_dogleg<Scalar>(R, diag, qtf, delta, wa1);
/* store the direction p and x + p. calculate the norm of p. */
wa1 = -wa1;
wa2 = x + wa1;
wa3 = diag.cwiseProduct(wa1);
pnorm = wa3.stableNorm();
/* on the first iteration, adjust the initial step bound. */
if (iter == 1)
delta = std::min(delta,pnorm);
/* evaluate the function at x + p and calculate its norm. */
if ( functor(wa2, wa4) < 0)
return UserAksed;
++nfev;
fnorm1 = wa4.stableNorm();
/* compute the scaled actual reduction. */
actred = -1.;
if (fnorm1 < fnorm) /* Computing 2nd power */
actred = 1. - ei_abs2(fnorm1 / fnorm);
/* compute the scaled predicted reduction. */
for (i = 0; i < n; ++i) {
sum = 0.;
for (j = i; j < n; ++j)
sum += R(i,j) * wa1[j];
wa3[i] = qtf[i] + sum;
}
wa3 = R.template triangularView<Upper>()*wa1 + qtf;
temp = wa3.stableNorm();
prered = 0.;
if (temp < fnorm) /* Computing 2nd power */
prered = 1. - ei_abs2(temp / fnorm);
/* compute the ratio of the actual to the predicted */
/* reduction. */
/* compute the ratio of the actual to the predicted reduction. */
ratio = 0.;
if (prered > 0.)
ratio = actred / prered;
/* update the step bound. */
if (ratio < Scalar(.1)) {
ncsuc = 0;
++ncfail;
@@ -658,7 +586,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
} else {
ncfail = 0;
++ncsuc;
if (ratio >= Scalar(.5) || ncsuc > 1) /* Computing MAX */
if (ratio >= Scalar(.5) || ncsuc > 1)
delta = std::max(delta, pnorm / Scalar(.5));
if (ei_abs(ratio - 1.) <= Scalar(.1)) {
delta = pnorm / Scalar(.5);
@@ -666,7 +594,6 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
}
/* test for successful iteration. */
if (ratio >= Scalar(1e-4)) {
/* successful iteration. update x, fvec, and their norms. */
x = wa2;
@@ -678,7 +605,6 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
}
/* determine the progress of the iteration. */
++nslow1;
if (actred >= Scalar(.001))
nslow1 = 0;
@@ -688,12 +614,10 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
nslow2 = 0;
/* test for convergence. */
if (delta <= parameters.xtol * xnorm || fnorm == 0.)
return RelativeErrorTooSmall;
/* tests for termination and stringent tolerances. */
if (nfev >= parameters.maxfev)
return TooManyFunctionEvaluation;
if (Scalar(.1) * std::max(Scalar(.1) * delta, pnorm) <= epsilon<Scalar>() * xnorm)
@@ -703,35 +627,25 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
if (nslow1 == 10)
return NotMakingProgressIterations;
/* criterion for recalculating jacobian approximation */
/* by forward differences. */
/* criterion for recalculating jacobian. */
if (ncfail == 2)
break; // leave inner loop and go for the next outer loop iteration
/* calculate the rank one modification to the jacobian */
/* and update qtf if necessary. */
for (j = 0; j < n; ++j) {
sum = wa4.dot(fjac.col(j));
wa2[j] = (sum - wa3[j]) / pnorm;
wa1[j] = diag[j] * (diag[j] * wa1[j] / pnorm);
if (ratio >= Scalar(1e-4))
qtf[j] = sum;
}
wa1 = diag.cwiseProduct( diag.cwiseProduct(wa1)/pnorm );
wa2 = fjac.transpose() * wa4;
if (ratio >= Scalar(1e-4))
qtf = wa2;
wa2 = (wa2-wa3)/pnorm;
/* compute the qr factorization of the updated jacobian. */
ei_r1updt<Scalar>(n, n, R, wa1.data(), wa2.data(), wa3.data(), &sing);
ei_r1mpyq<Scalar>(n, n, fjac.data(), fjac.rows(), wa2.data(), wa3.data());
ei_r1mpyq<Scalar>(1, n, qtf.data(), 1, wa2.data(), wa3.data());
/* end of the inner loop. */
jeval = false;
}
/* end of the outer loop. */
return Running;
}