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https://gitlab.com/libeigen/eigen.git
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16061a46db
used by those algorithms (aka "second level"). This is a row import : we copy/paste the files from cminpack and make very few changes : * template<Scalar> them (replace double) * dpmpar() replaced by c++ standard equivalent * abs/fabs/sqrt/min/max replaced by ei_* or std::* * use eigen norms instead of enorm() Important Notes: * The use of stableNorm() was not enough in some cases, but using blueNorm() instead fixed the problems (some tests gave bad results, either in number of iterations or precision of the results) * As a whole, the only test that changed is testNistMGH17() : it now takes some few steps less to get the same result. So this is a small improvement. After this commit, the only remaining dependency from the cminpack static library is 'covar', only used from the tests.
59 lines
1.1 KiB
C++
59 lines
1.1 KiB
C++
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template <typename Scalar>
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int ei_fdjac2(minpack_func_mn fcn, void *p, int m, int n, Scalar *x,
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const Scalar *fvec, Scalar *fjac, int ldfjac,
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Scalar epsfcn, Scalar *wa)
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{
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/* System generated locals */
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int fjac_dim1, fjac_offset, i__1, i__2;
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/* Local variables */
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Scalar h__;
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int i__, j;
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Scalar eps, temp, epsmch;
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int iflag;
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/* Parameter adjustments */
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--wa;
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--fvec;
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--x;
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fjac_dim1 = ldfjac;
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fjac_offset = 1 + fjac_dim1 * 1;
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fjac -= fjac_offset;
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/* Function Body */
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/* epsmch is the machine precision. */
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epsmch = epsilon<Scalar>();
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eps = ei_sqrt((std::max(epsfcn,epsmch)));
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i__1 = n;
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for (j = 1; j <= i__1; ++j) {
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temp = x[j];
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h__ = eps * ei_abs(temp);
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if (h__ == 0.) {
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h__ = eps;
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}
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x[j] = temp + h__;
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iflag = (*fcn)(p, m, n, &x[1], &wa[1], 1);
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if (iflag < 0) {
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/* goto L30; */
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return iflag;
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}
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x[j] = temp;
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i__2 = m;
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for (i__ = 1; i__ <= i__2; ++i__) {
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fjac[i__ + j * fjac_dim1] = (wa[i__] - fvec[i__]) / h__;
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/* L10: */
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}
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/* L20: */
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}
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/* L30: */
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return iflag;
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/* last card of subroutine fdjac2. */
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} /* fdjac2_ */
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