Actual source code: pounders.c
1: #include <../src/tao/leastsquares/impls/pounders/pounders.h>
3: static PetscErrorCode pounders_h(Tao subtao, Vec v, Mat H, Mat Hpre, void *ctx)
4: {
5: PetscFunctionBegin;
6: PetscFunctionReturn(PETSC_SUCCESS);
7: }
9: static PetscErrorCode pounders_fg(Tao subtao, Vec x, PetscReal *f, Vec g, void *ctx)
10: {
11: TAO_POUNDERS *mfqP = (TAO_POUNDERS *)ctx;
12: PetscReal d1, d2;
14: PetscFunctionBegin;
15: /* g = A*x (add b later)*/
16: PetscCall(MatMult(mfqP->subH, x, g));
18: /* f = 1/2 * x'*(Ax) + b'*x */
19: PetscCall(VecDot(x, g, &d1));
20: PetscCall(VecDot(mfqP->subb, x, &d2));
21: *f = 0.5 * d1 + d2;
23: /* now g = g + b */
24: PetscCall(VecAXPY(g, 1.0, mfqP->subb));
25: PetscFunctionReturn(PETSC_SUCCESS);
26: }
28: static PetscErrorCode pounders_feval(Tao tao, Vec x, Vec F, PetscReal *fsum)
29: {
30: TAO_POUNDERS *mfqP = (TAO_POUNDERS *)tao->data;
31: PetscInt i, row, col;
32: PetscReal fr, fc;
34: PetscFunctionBegin;
35: PetscCall(TaoComputeResidual(tao, x, F));
36: if (tao->res_weights_v) {
37: PetscCall(VecPointwiseMult(mfqP->workfvec, tao->res_weights_v, F));
38: PetscCall(VecDot(mfqP->workfvec, mfqP->workfvec, fsum));
39: } else if (tao->res_weights_w) {
40: *fsum = 0;
41: for (i = 0; i < tao->res_weights_n; i++) {
42: row = tao->res_weights_rows[i];
43: col = tao->res_weights_cols[i];
44: PetscCall(VecGetValues(F, 1, &row, &fr));
45: PetscCall(VecGetValues(F, 1, &col, &fc));
46: *fsum += tao->res_weights_w[i] * fc * fr;
47: }
48: } else {
49: PetscCall(VecDot(F, F, fsum));
50: }
51: PetscCall(PetscInfo(tao, "Least-squares residual norm: %20.19e\n", (double)*fsum));
52: PetscCheck(!PetscIsInfOrNanReal(*fsum), PETSC_COMM_SELF, PETSC_ERR_USER, "User provided compute function generated Inf or NaN");
53: PetscFunctionReturn(PETSC_SUCCESS);
54: }
56: static PetscErrorCode gqtwrap(Tao tao, PetscReal *gnorm, PetscReal *qmin)
57: {
58: #if defined(PETSC_USE_REAL_SINGLE)
59: PetscReal atol = 1.0e-5;
60: #else
61: PetscReal atol = 1.0e-10;
62: #endif
63: PetscInt info, its;
64: TAO_POUNDERS *mfqP = (TAO_POUNDERS *)tao->data;
66: PetscFunctionBegin;
67: if (!mfqP->usegqt) {
68: PetscReal maxval;
69: PetscInt i, j;
71: PetscCall(VecSetValues(mfqP->subb, mfqP->n, mfqP->indices, mfqP->Gres, INSERT_VALUES));
72: PetscCall(VecAssemblyBegin(mfqP->subb));
73: PetscCall(VecAssemblyEnd(mfqP->subb));
75: PetscCall(VecSet(mfqP->subx, 0.0));
77: PetscCall(VecSet(mfqP->subndel, -1.0));
78: PetscCall(VecSet(mfqP->subpdel, +1.0));
80: /* Complete the lower triangle of the Hessian matrix */
81: for (i = 0; i < mfqP->n; i++) {
82: for (j = i + 1; j < mfqP->n; j++) mfqP->Hres[j + mfqP->n * i] = mfqP->Hres[mfqP->n * j + i];
83: }
84: PetscCall(MatSetValues(mfqP->subH, mfqP->n, mfqP->indices, mfqP->n, mfqP->indices, mfqP->Hres, INSERT_VALUES));
85: PetscCall(MatAssemblyBegin(mfqP->subH, MAT_FINAL_ASSEMBLY));
86: PetscCall(MatAssemblyEnd(mfqP->subH, MAT_FINAL_ASSEMBLY));
88: PetscCall(TaoResetStatistics(mfqP->subtao));
89: /* PetscCall(TaoSetTolerances(mfqP->subtao,*gnorm,*gnorm,PETSC_CURRENT)); */
90: /* enforce bound constraints -- experimental */
91: if (tao->XU && tao->XL) {
92: PetscCall(VecCopy(tao->XU, mfqP->subxu));
93: PetscCall(VecAXPY(mfqP->subxu, -1.0, tao->solution));
94: PetscCall(VecScale(mfqP->subxu, 1.0 / mfqP->delta));
95: PetscCall(VecCopy(tao->XL, mfqP->subxl));
96: PetscCall(VecAXPY(mfqP->subxl, -1.0, tao->solution));
97: PetscCall(VecScale(mfqP->subxl, 1.0 / mfqP->delta));
99: PetscCall(VecPointwiseMin(mfqP->subxu, mfqP->subxu, mfqP->subpdel));
100: PetscCall(VecPointwiseMax(mfqP->subxl, mfqP->subxl, mfqP->subndel));
101: } else {
102: PetscCall(VecCopy(mfqP->subpdel, mfqP->subxu));
103: PetscCall(VecCopy(mfqP->subndel, mfqP->subxl));
104: }
105: /* Make sure xu > xl */
106: PetscCall(VecCopy(mfqP->subxl, mfqP->subpdel));
107: PetscCall(VecAXPY(mfqP->subpdel, -1.0, mfqP->subxu));
108: PetscCall(VecMax(mfqP->subpdel, NULL, &maxval));
109: PetscCheck(maxval <= 1e-10, PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_OUTOFRANGE, "upper bound < lower bound in subproblem");
110: /* Make sure xu > tao->solution > xl */
111: PetscCall(VecCopy(mfqP->subxl, mfqP->subpdel));
112: PetscCall(VecAXPY(mfqP->subpdel, -1.0, mfqP->subx));
113: PetscCall(VecMax(mfqP->subpdel, NULL, &maxval));
114: PetscCheck(maxval <= 1e-10, PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_OUTOFRANGE, "initial guess < lower bound in subproblem");
116: PetscCall(VecCopy(mfqP->subx, mfqP->subpdel));
117: PetscCall(VecAXPY(mfqP->subpdel, -1.0, mfqP->subxu));
118: PetscCall(VecMax(mfqP->subpdel, NULL, &maxval));
119: PetscCheck(maxval <= 1e-10, PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_OUTOFRANGE, "initial guess > upper bound in subproblem");
121: PetscCall(TaoSolve(mfqP->subtao));
122: PetscCall(TaoGetSolutionStatus(mfqP->subtao, NULL, qmin, NULL, NULL, NULL, NULL));
124: /* test bounds post-solution*/
125: PetscCall(VecCopy(mfqP->subxl, mfqP->subpdel));
126: PetscCall(VecAXPY(mfqP->subpdel, -1.0, mfqP->subx));
127: PetscCall(VecMax(mfqP->subpdel, NULL, &maxval));
128: if (maxval > 1e-5) {
129: PetscCall(PetscInfo(tao, "subproblem solution < lower bound\n"));
130: tao->reason = TAO_DIVERGED_TR_REDUCTION;
131: }
133: PetscCall(VecCopy(mfqP->subx, mfqP->subpdel));
134: PetscCall(VecAXPY(mfqP->subpdel, -1.0, mfqP->subxu));
135: PetscCall(VecMax(mfqP->subpdel, NULL, &maxval));
136: if (maxval > 1e-5) {
137: PetscCall(PetscInfo(tao, "subproblem solution > upper bound\n"));
138: tao->reason = TAO_DIVERGED_TR_REDUCTION;
139: }
140: } else {
141: PetscCall(gqt(mfqP->n, mfqP->Hres, mfqP->n, mfqP->Gres, 1.0, mfqP->gqt_rtol, atol, mfqP->gqt_maxits, gnorm, qmin, mfqP->Xsubproblem, &info, &its, mfqP->work, mfqP->work2, mfqP->work3));
142: }
143: *qmin *= -1;
144: PetscFunctionReturn(PETSC_SUCCESS);
145: }
147: static PetscErrorCode pounders_update_res(Tao tao)
148: {
149: TAO_POUNDERS *mfqP = (TAO_POUNDERS *)tao->data;
150: PetscInt i, row, col;
151: PetscBLASInt blasn, blasn2, blasm, ione = 1;
152: PetscReal zero = 0.0, one = 1.0, wii, factor;
154: PetscFunctionBegin;
155: PetscCall(PetscBLASIntCast(mfqP->n, &blasn));
156: PetscCall(PetscBLASIntCast(mfqP->m, &blasm));
157: PetscCall(PetscBLASIntCast(mfqP->n * mfqP->n, &blasn2));
158: for (i = 0; i < mfqP->n; i++) mfqP->Gres[i] = 0;
159: for (i = 0; i < mfqP->n * mfqP->n; i++) mfqP->Hres[i] = 0;
161: /* Compute Gres= sum_ij[wij * (cjgi + cigj)] */
162: if (tao->res_weights_v) {
163: /* Vector(diagonal) weights: gres = sum_i(wii*ci*gi) */
164: for (i = 0; i < mfqP->m; i++) {
165: PetscCall(VecGetValues(tao->res_weights_v, 1, &i, &factor));
166: factor = factor * mfqP->C[i];
167: PetscCallBLAS("BLASaxpy", BLASaxpy_(&blasn, &factor, &mfqP->Fdiff[blasn * i], &ione, mfqP->Gres, &ione));
168: }
170: /* compute Hres = sum_ij [wij * (*ci*Hj + cj*Hi + gi gj' + gj gi') ] */
171: /* vector(diagonal weights) Hres = sum_i(wii*(ci*Hi + gi * gi')*/
172: for (i = 0; i < mfqP->m; i++) {
173: PetscCall(VecGetValues(tao->res_weights_v, 1, &i, &wii));
174: if (tao->niter > 1) {
175: factor = wii * mfqP->C[i];
176: /* add wii * ci * Hi */
177: PetscCallBLAS("BLASaxpy", BLASaxpy_(&blasn2, &factor, &mfqP->H[i], &blasm, mfqP->Hres, &ione));
178: }
179: /* add wii * gi * gi' */
180: PetscCallBLAS("BLASgemm", BLASgemm_("N", "T", &blasn, &blasn, &ione, &wii, &mfqP->Fdiff[blasn * i], &blasn, &mfqP->Fdiff[blasn * i], &blasn, &one, mfqP->Hres, &blasn));
181: }
182: } else if (tao->res_weights_w) {
183: /* General case: .5 * Gres= sum_ij[wij * (cjgi + cigj)] */
184: for (i = 0; i < tao->res_weights_n; i++) {
185: row = tao->res_weights_rows[i];
186: col = tao->res_weights_cols[i];
188: factor = tao->res_weights_w[i] * mfqP->C[col] / 2.0;
189: PetscCallBLAS("BLASaxpy", BLASaxpy_(&blasn, &factor, &mfqP->Fdiff[blasn * row], &ione, mfqP->Gres, &ione));
190: factor = tao->res_weights_w[i] * mfqP->C[row] / 2.0;
191: PetscCallBLAS("BLASaxpy", BLASaxpy_(&blasn, &factor, &mfqP->Fdiff[blasn * col], &ione, mfqP->Gres, &ione));
192: }
194: /* compute Hres = sum_ij [wij * (*ci*Hj + cj*Hi + gi gj' + gj gi') ] */
195: /* .5 * sum_ij [wij * (*ci*Hj + cj*Hi + gi gj' + gj gi') ] */
196: for (i = 0; i < tao->res_weights_n; i++) {
197: row = tao->res_weights_rows[i];
198: col = tao->res_weights_cols[i];
199: factor = tao->res_weights_w[i] / 2.0;
200: /* add wij * gi gj' + wij * gj gi' */
201: PetscCallBLAS("BLASgemm", BLASgemm_("N", "T", &blasn, &blasn, &ione, &factor, &mfqP->Fdiff[blasn * row], &blasn, &mfqP->Fdiff[blasn * col], &blasn, &one, mfqP->Hres, &blasn));
202: PetscCallBLAS("BLASgemm", BLASgemm_("N", "T", &blasn, &blasn, &ione, &factor, &mfqP->Fdiff[blasn * col], &blasn, &mfqP->Fdiff[blasn * row], &blasn, &one, mfqP->Hres, &blasn));
203: }
204: if (tao->niter > 1) {
205: for (i = 0; i < tao->res_weights_n; i++) {
206: row = tao->res_weights_rows[i];
207: col = tao->res_weights_cols[i];
209: /* add wij*cj*Hi */
210: factor = tao->res_weights_w[i] * mfqP->C[col] / 2.0;
211: PetscCallBLAS("BLASaxpy", BLASaxpy_(&blasn2, &factor, &mfqP->H[row], &blasm, mfqP->Hres, &ione));
213: /* add wij*ci*Hj */
214: factor = tao->res_weights_w[i] * mfqP->C[row] / 2.0;
215: PetscCallBLAS("BLASaxpy", BLASaxpy_(&blasn2, &factor, &mfqP->H[col], &blasm, mfqP->Hres, &ione));
216: }
217: }
218: } else {
219: /* Default: Gres= sum_i[cigi] = G*c' */
220: PetscCall(PetscInfo(tao, "Identity weights\n"));
221: PetscCallBLAS("BLASgemv", BLASgemv_("N", &blasn, &blasm, &one, mfqP->Fdiff, &blasn, mfqP->C, &ione, &zero, mfqP->Gres, &ione));
223: /* compute Hres = sum_ij [wij * (*ci*Hj + cj*Hi + gi gj' + gj gi') ] */
224: /* Hres = G*G' + 0.5 sum {F(xkin,i)*H(:,:,i)} */
225: PetscCallBLAS("BLASgemm", BLASgemm_("N", "T", &blasn, &blasn, &blasm, &one, mfqP->Fdiff, &blasn, mfqP->Fdiff, &blasn, &zero, mfqP->Hres, &blasn));
227: /* sum(F(xkin,i)*H(:,:,i)) */
228: if (tao->niter > 1) {
229: for (i = 0; i < mfqP->m; i++) {
230: factor = mfqP->C[i];
231: PetscCallBLAS("BLASaxpy", BLASaxpy_(&blasn2, &factor, &mfqP->H[i], &blasm, mfqP->Hres, &ione));
232: }
233: }
234: }
235: PetscFunctionReturn(PETSC_SUCCESS);
236: }
238: static PetscErrorCode phi2eval(PetscReal *x, PetscInt n, PetscReal *phi)
239: {
240: /* Phi = .5*[x(1)^2 sqrt(2)*x(1)*x(2) ... sqrt(2)*x(1)*x(n) ... x(2)^2 sqrt(2)*x(2)*x(3) .. x(n)^2] */
241: PetscInt i, j, k;
242: PetscReal sqrt2 = PetscSqrtReal(2.0);
244: PetscFunctionBegin;
245: j = 0;
246: for (i = 0; i < n; i++) {
247: phi[j] = 0.5 * x[i] * x[i];
248: j++;
249: for (k = i + 1; k < n; k++) {
250: phi[j] = x[i] * x[k] / sqrt2;
251: j++;
252: }
253: }
254: PetscFunctionReturn(PETSC_SUCCESS);
255: }
257: static PetscErrorCode getquadpounders(TAO_POUNDERS *mfqP)
258: {
259: /* Computes the parameters of the quadratic Q(x) = c + g'*x + 0.5*x*G*x'
260: that satisfies the interpolation conditions Q(X[:,j]) = f(j)
261: for j=1,...,m and with a Hessian matrix of least Frobenius norm */
263: /* NB --we are ignoring c */
264: PetscInt i, j, k, num, np = mfqP->nmodelpoints;
265: PetscReal one = 1.0, zero = 0.0, negone = -1.0;
266: PetscBLASInt blasnpmax, blasnplus1, blasnp, blasint, blasint2;
267: PetscBLASInt info, ione = 1;
268: PetscReal sqrt2 = PetscSqrtReal(2.0);
270: PetscFunctionBegin;
271: PetscCall(PetscBLASIntCast(mfqP->npmax, &blasnpmax));
272: PetscCall(PetscBLASIntCast(mfqP->n + 1, &blasnplus1));
273: PetscCall(PetscBLASIntCast(np, &blasnp));
274: PetscCall(PetscBLASIntCast(mfqP->n * (mfqP->n + 1) / 2, &blasint));
275: PetscCall(PetscBLASIntCast(np - mfqP->n - 1, &blasint2));
276: for (i = 0; i < mfqP->n * mfqP->m; i++) mfqP->Gdel[i] = 0;
277: for (i = 0; i < mfqP->n * mfqP->n * mfqP->m; i++) mfqP->Hdel[i] = 0;
279: /* factor M */
280: PetscCallBLAS("LAPACKgetrf", LAPACKgetrf_(&blasnplus1, &blasnp, mfqP->M, &blasnplus1, mfqP->npmaxiwork, &info));
281: PetscCheck(info == 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "LAPACK routine getrf returned with value %" PetscBLASInt_FMT, info);
283: if (np == mfqP->n + 1) {
284: for (i = 0; i < mfqP->npmax - mfqP->n - 1; i++) mfqP->omega[i] = 0.0;
285: for (i = 0; i < mfqP->n * (mfqP->n + 1) / 2; i++) mfqP->beta[i] = 0.0;
286: } else {
287: /* Let Ltmp = (L'*L) */
288: PetscCallBLAS("BLASgemm", BLASgemm_("T", "N", &blasint2, &blasint2, &blasint, &one, &mfqP->L[(mfqP->n + 1) * blasint], &blasint, &mfqP->L[(mfqP->n + 1) * blasint], &blasint, &zero, mfqP->L_tmp, &blasint));
290: /* factor Ltmp */
291: PetscCallBLAS("LAPACKpotrf", LAPACKpotrf_("L", &blasint2, mfqP->L_tmp, &blasint, &info));
292: PetscCheck(info == 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "LAPACK routine potrf returned with value %" PetscBLASInt_FMT, info);
293: }
295: for (k = 0; k < mfqP->m; k++) {
296: if (np != mfqP->n + 1) {
297: /* Solve L'*L*Omega = Z' * RESk*/
298: PetscCallBLAS("BLASgemv", BLASgemv_("T", &blasnp, &blasint2, &one, mfqP->Z, &blasnpmax, &mfqP->RES[mfqP->npmax * k], &ione, &zero, mfqP->omega, &ione));
299: PetscCallBLAS("LAPACKpotrs", LAPACKpotrs_("L", &blasint2, &ione, mfqP->L_tmp, &blasint, mfqP->omega, &blasint2, &info));
300: PetscCheck(info == 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "LAPACK routine potrs returned with value %" PetscBLASInt_FMT, info);
302: /* Beta = L*Omega */
303: PetscCallBLAS("BLASgemv", BLASgemv_("N", &blasint, &blasint2, &one, &mfqP->L[(mfqP->n + 1) * blasint], &blasint, mfqP->omega, &ione, &zero, mfqP->beta, &ione));
304: }
306: /* solve M'*Alpha = RESk - N'*Beta */
307: PetscCallBLAS("BLASgemv", BLASgemv_("T", &blasint, &blasnp, &negone, mfqP->N, &blasint, mfqP->beta, &ione, &one, &mfqP->RES[mfqP->npmax * k], &ione));
308: PetscCallBLAS("LAPACKgetrs", LAPACKgetrs_("T", &blasnplus1, &ione, mfqP->M, &blasnplus1, mfqP->npmaxiwork, &mfqP->RES[mfqP->npmax * k], &blasnplus1, &info));
309: PetscCheck(info == 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "LAPACK routine getrs returned with value %" PetscBLASInt_FMT, info);
311: /* Gdel(:,k) = Alpha(2:n+1) */
312: for (i = 0; i < mfqP->n; i++) mfqP->Gdel[i + mfqP->n * k] = mfqP->RES[mfqP->npmax * k + i + 1];
314: /* Set Hdels */
315: num = 0;
316: for (i = 0; i < mfqP->n; i++) {
317: /* H[i,i,k] = Beta(num) */
318: mfqP->Hdel[(i * mfqP->n + i) * mfqP->m + k] = mfqP->beta[num];
319: num++;
320: for (j = i + 1; j < mfqP->n; j++) {
321: /* H[i,j,k] = H[j,i,k] = Beta(num)/sqrt(2) */
322: mfqP->Hdel[(j * mfqP->n + i) * mfqP->m + k] = mfqP->beta[num] / sqrt2;
323: mfqP->Hdel[(i * mfqP->n + j) * mfqP->m + k] = mfqP->beta[num] / sqrt2;
324: num++;
325: }
326: }
327: }
328: PetscFunctionReturn(PETSC_SUCCESS);
329: }
331: static PetscErrorCode morepoints(TAO_POUNDERS *mfqP)
332: {
333: /* Assumes mfqP->model_indices[0] is minimum index
334: Finishes adding points to mfqP->model_indices (up to npmax)
335: Computes L,Z,M,N
336: np is actual number of points in model (should equal npmax?) */
337: PetscInt point, i, j, offset;
338: PetscInt reject;
339: PetscBLASInt blasn, blasnpmax, blasnplus1, info, blasnmax, blasint, blasint2, blasnp, blasmaxmn;
340: const PetscReal *x;
341: PetscReal normd;
343: PetscFunctionBegin;
344: PetscCall(PetscBLASIntCast(mfqP->npmax, &blasnpmax));
345: PetscCall(PetscBLASIntCast(mfqP->n, &blasn));
346: PetscCall(PetscBLASIntCast(mfqP->nmax, &blasnmax));
347: PetscCall(PetscBLASIntCast(mfqP->n + 1, &blasnplus1));
348: PetscCall(PetscBLASIntCast(mfqP->n, &blasnp));
349: /* Initialize M,N */
350: for (i = 0; i < mfqP->n + 1; i++) {
351: PetscCall(VecGetArrayRead(mfqP->Xhist[mfqP->model_indices[i]], &x));
352: mfqP->M[(mfqP->n + 1) * i] = 1.0;
353: for (j = 0; j < mfqP->n; j++) mfqP->M[j + 1 + ((mfqP->n + 1) * i)] = (x[j] - mfqP->xmin[j]) / mfqP->delta;
354: PetscCall(VecRestoreArrayRead(mfqP->Xhist[mfqP->model_indices[i]], &x));
355: PetscCall(phi2eval(&mfqP->M[1 + ((mfqP->n + 1) * i)], mfqP->n, &mfqP->N[mfqP->n * (mfqP->n + 1) / 2 * i]));
356: }
358: /* Now we add points until we have npmax starting with the most recent ones */
359: point = mfqP->nHist - 1;
360: mfqP->nmodelpoints = mfqP->n + 1;
361: while (mfqP->nmodelpoints < mfqP->npmax && point >= 0) {
362: /* Reject any points already in the model */
363: reject = 0;
364: for (j = 0; j < mfqP->n + 1; j++) {
365: if (point == mfqP->model_indices[j]) {
366: reject = 1;
367: break;
368: }
369: }
371: /* Reject if norm(d) >c2 */
372: if (!reject) {
373: PetscCall(VecCopy(mfqP->Xhist[point], mfqP->workxvec));
374: PetscCall(VecAXPY(mfqP->workxvec, -1.0, mfqP->Xhist[mfqP->minindex]));
375: PetscCall(VecNorm(mfqP->workxvec, NORM_2, &normd));
376: normd /= mfqP->delta;
377: if (normd > mfqP->c2) reject = 1;
378: }
379: if (reject) {
380: point--;
381: continue;
382: }
384: PetscCall(VecGetArrayRead(mfqP->Xhist[point], &x));
385: mfqP->M[(mfqP->n + 1) * mfqP->nmodelpoints] = 1.0;
386: for (j = 0; j < mfqP->n; j++) mfqP->M[j + 1 + ((mfqP->n + 1) * mfqP->nmodelpoints)] = (x[j] - mfqP->xmin[j]) / mfqP->delta;
387: PetscCall(VecRestoreArrayRead(mfqP->Xhist[point], &x));
388: PetscCall(phi2eval(&mfqP->M[1 + (mfqP->n + 1) * mfqP->nmodelpoints], mfqP->n, &mfqP->N[mfqP->n * (mfqP->n + 1) / 2 * (mfqP->nmodelpoints)]));
390: /* Update QR factorization */
391: /* Copy M' to Q_tmp */
392: for (i = 0; i < mfqP->n + 1; i++) {
393: for (j = 0; j < mfqP->npmax; j++) mfqP->Q_tmp[j + mfqP->npmax * i] = mfqP->M[i + (mfqP->n + 1) * j];
394: }
395: PetscCall(PetscBLASIntCast(mfqP->nmodelpoints + 1, &blasnp));
396: /* Q_tmp,R = qr(M') */
397: PetscCall(PetscBLASIntCast(PetscMax(mfqP->m, mfqP->n + 1), &blasmaxmn));
398: PetscCallBLAS("LAPACKgeqrf", LAPACKgeqrf_(&blasnp, &blasnplus1, mfqP->Q_tmp, &blasnpmax, mfqP->tau_tmp, mfqP->mwork, &blasmaxmn, &info));
399: PetscCheck(info == 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "LAPACK routine geqrf returned with value %" PetscBLASInt_FMT, info);
401: /* Reject if min(svd(N*Q(:,n+2:np+1)) <= theta2 */
402: /* L = N*Qtmp */
403: PetscCall(PetscBLASIntCast(mfqP->n * (mfqP->n + 1) / 2, &blasint2));
404: /* Copy N to L_tmp */
405: for (i = 0; i < mfqP->n * (mfqP->n + 1) / 2 * mfqP->npmax; i++) mfqP->L_tmp[i] = mfqP->N[i];
406: /* Copy L_save to L_tmp */
408: /* L_tmp = N*Qtmp' */
409: PetscCallBLAS("LAPACKormqr", LAPACKormqr_("R", "N", &blasint2, &blasnp, &blasnplus1, mfqP->Q_tmp, &blasnpmax, mfqP->tau_tmp, mfqP->L_tmp, &blasint2, mfqP->npmaxwork, &blasnmax, &info));
410: PetscCheck(info == 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "LAPACK routine ormqr returned with value %" PetscBLASInt_FMT, info);
412: /* Copy L_tmp to L_save */
413: for (i = 0; i < mfqP->npmax * mfqP->n * (mfqP->n + 1) / 2; i++) mfqP->L_save[i] = mfqP->L_tmp[i];
415: /* Get svd for L_tmp(:,n+2:np+1) (L_tmp is modified in process) */
416: PetscCall(PetscBLASIntCast(mfqP->nmodelpoints - mfqP->n, &blasint));
417: PetscCall(PetscFPTrapPush(PETSC_FP_TRAP_OFF));
418: PetscCallBLAS("LAPACKgesvd", LAPACKgesvd_("N", "N", &blasint2, &blasint, &mfqP->L_tmp[(mfqP->n + 1) * blasint2], &blasint2, mfqP->beta, mfqP->work, &blasn, mfqP->work, &blasn, mfqP->npmaxwork, &blasnmax, &info));
419: PetscCall(PetscFPTrapPop());
420: PetscCheck(info == 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "LAPACK routine gesvd returned with value %" PetscBLASInt_FMT, info);
422: if (mfqP->beta[PetscMin(blasint, blasint2) - 1] > mfqP->theta2) {
423: /* accept point */
424: mfqP->model_indices[mfqP->nmodelpoints] = point;
425: /* Copy Q_tmp to Q */
426: for (i = 0; i < mfqP->npmax * mfqP->npmax; i++) mfqP->Q[i] = mfqP->Q_tmp[i];
427: for (i = 0; i < mfqP->npmax; i++) mfqP->tau[i] = mfqP->tau_tmp[i];
428: mfqP->nmodelpoints++;
429: PetscCall(PetscBLASIntCast(mfqP->nmodelpoints, &blasnp));
431: /* Copy L_save to L */
432: for (i = 0; i < mfqP->npmax * mfqP->n * (mfqP->n + 1) / 2; i++) mfqP->L[i] = mfqP->L_save[i];
433: }
434: point--;
435: }
437: PetscCall(PetscBLASIntCast(mfqP->nmodelpoints, &blasnp));
438: /* Copy Q(:,n+2:np) to Z */
439: /* First set Q_tmp to I */
440: for (i = 0; i < mfqP->npmax * mfqP->npmax; i++) mfqP->Q_tmp[i] = 0.0;
441: for (i = 0; i < mfqP->npmax; i++) mfqP->Q_tmp[i + mfqP->npmax * i] = 1.0;
443: /* Q_tmp = I * Q */
444: PetscCallBLAS("LAPACKormqr", LAPACKormqr_("R", "N", &blasnp, &blasnp, &blasnplus1, mfqP->Q, &blasnpmax, mfqP->tau, mfqP->Q_tmp, &blasnpmax, mfqP->npmaxwork, &blasnmax, &info));
445: PetscCheck(info == 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "LAPACK routine ormqr returned with value %" PetscBLASInt_FMT, info);
447: /* Copy Q_tmp(:,n+2:np) to Z) */
448: offset = mfqP->npmax * (mfqP->n + 1);
449: for (i = offset; i < mfqP->npmax * mfqP->npmax; i++) mfqP->Z[i - offset] = mfqP->Q_tmp[i];
451: if (mfqP->nmodelpoints == mfqP->n + 1) {
452: /* Set L to I_{n+1} */
453: for (i = 0; i < mfqP->npmax * mfqP->n * (mfqP->n + 1) / 2; i++) mfqP->L[i] = 0.0;
454: for (i = 0; i < mfqP->n; i++) mfqP->L[(mfqP->n * (mfqP->n + 1) / 2) * i + i] = 1.0;
455: }
456: PetscFunctionReturn(PETSC_SUCCESS);
457: }
459: /* Only call from modelimprove, addpoint() needs ->Q_tmp and ->work to be set */
460: static PetscErrorCode addpoint(Tao tao, TAO_POUNDERS *mfqP, PetscInt index)
461: {
462: PetscFunctionBegin;
463: /* Create new vector in history: X[newidx] = X[mfqP->index] + delta*X[index]*/
464: PetscCall(VecDuplicate(mfqP->Xhist[0], &mfqP->Xhist[mfqP->nHist]));
465: PetscCall(VecSetValues(mfqP->Xhist[mfqP->nHist], mfqP->n, mfqP->indices, &mfqP->Q_tmp[index * mfqP->npmax], INSERT_VALUES));
466: PetscCall(VecAssemblyBegin(mfqP->Xhist[mfqP->nHist]));
467: PetscCall(VecAssemblyEnd(mfqP->Xhist[mfqP->nHist]));
468: PetscCall(VecAYPX(mfqP->Xhist[mfqP->nHist], mfqP->delta, mfqP->Xhist[mfqP->minindex]));
470: /* Project into feasible region */
471: if (tao->XU && tao->XL) PetscCall(VecMedian(mfqP->Xhist[mfqP->nHist], tao->XL, tao->XU, mfqP->Xhist[mfqP->nHist]));
473: /* Compute value of new vector */
474: PetscCall(VecDuplicate(mfqP->Fhist[0], &mfqP->Fhist[mfqP->nHist]));
475: CHKMEMQ;
476: PetscCall(pounders_feval(tao, mfqP->Xhist[mfqP->nHist], mfqP->Fhist[mfqP->nHist], &mfqP->Fres[mfqP->nHist]));
478: /* Add new vector to model */
479: mfqP->model_indices[mfqP->nmodelpoints] = mfqP->nHist;
480: mfqP->nmodelpoints++;
481: mfqP->nHist++;
482: PetscFunctionReturn(PETSC_SUCCESS);
483: }
485: static PetscErrorCode modelimprove(Tao tao, TAO_POUNDERS *mfqP, PetscInt addallpoints)
486: {
487: /* modeld = Q(:,np+1:n)' */
488: PetscInt i, j, minindex = 0;
489: PetscReal dp, half = 0.5, one = 1.0, minvalue = PETSC_INFINITY;
490: PetscBLASInt blasn, blasnpmax, blask, info;
491: PetscBLASInt blas1 = 1, blasnmax;
493: PetscFunctionBegin;
494: PetscCall(PetscBLASIntCast(mfqP->n, &blasn));
495: PetscCall(PetscBLASIntCast(mfqP->npmax, &blasnpmax));
496: PetscCall(PetscBLASIntCast(mfqP->nmodelpoints, &blask));
497: PetscCall(PetscBLASIntCast(mfqP->nmax, &blasnmax));
499: /* Qtmp = I(n x n) */
500: for (i = 0; i < mfqP->n; i++) {
501: for (j = 0; j < mfqP->n; j++) mfqP->Q_tmp[i + mfqP->npmax * j] = 0.0;
502: }
503: for (j = 0; j < mfqP->n; j++) mfqP->Q_tmp[j + mfqP->npmax * j] = 1.0;
505: /* Qtmp = Q * I */
506: PetscCallBLAS("LAPACKormqr", LAPACKormqr_("R", "N", &blasn, &blasn, &blask, mfqP->Q, &blasnpmax, mfqP->tau, mfqP->Q_tmp, &blasnpmax, mfqP->npmaxwork, &blasnmax, &info));
508: for (i = mfqP->nmodelpoints; i < mfqP->n; i++) {
509: PetscCallBLAS("BLASdot", dp = BLASdot_(&blasn, &mfqP->Q_tmp[i * mfqP->npmax], &blas1, mfqP->Gres, &blas1));
510: if (dp > 0.0) { /* Model says use the other direction! */
511: for (j = 0; j < mfqP->n; j++) mfqP->Q_tmp[i * mfqP->npmax + j] *= -1;
512: }
513: /* mfqP->work[i] = Cres+Modeld(i,:)*(Gres+.5*Hres*Modeld(i,:)') */
514: for (j = 0; j < mfqP->n; j++) mfqP->work2[j] = mfqP->Gres[j];
515: PetscCallBLAS("BLASgemv", BLASgemv_("N", &blasn, &blasn, &half, mfqP->Hres, &blasn, &mfqP->Q_tmp[i * mfqP->npmax], &blas1, &one, mfqP->work2, &blas1));
516: PetscCallBLAS("BLASdot", mfqP->work[i] = BLASdot_(&blasn, &mfqP->Q_tmp[i * mfqP->npmax], &blas1, mfqP->work2, &blas1));
517: if (i == mfqP->nmodelpoints || mfqP->work[i] < minvalue) {
518: minindex = i;
519: minvalue = mfqP->work[i];
520: }
521: if (addallpoints != 0) PetscCall(addpoint(tao, mfqP, i));
522: }
523: if (!addallpoints) PetscCall(addpoint(tao, mfqP, minindex));
524: PetscFunctionReturn(PETSC_SUCCESS);
525: }
527: static PetscErrorCode affpoints(TAO_POUNDERS *mfqP, PetscReal *xmin, PetscReal c)
528: {
529: PetscInt i, j;
530: PetscBLASInt blasm, blasj, blask, blasn, ione = 1, info;
531: PetscBLASInt blasnpmax, blasmaxmn;
532: PetscReal proj, normd;
533: const PetscReal *x;
535: PetscFunctionBegin;
536: PetscCall(PetscBLASIntCast(mfqP->npmax, &blasnpmax));
537: PetscCall(PetscBLASIntCast(mfqP->m, &blasm));
538: PetscCall(PetscBLASIntCast(mfqP->n, &blasn));
539: for (i = mfqP->nHist - 1; i >= 0; i--) {
540: PetscCall(VecGetArrayRead(mfqP->Xhist[i], &x));
541: for (j = 0; j < mfqP->n; j++) mfqP->work[j] = (x[j] - xmin[j]) / mfqP->delta;
542: PetscCall(VecRestoreArrayRead(mfqP->Xhist[i], &x));
543: PetscCallBLAS("BLAScopy", BLAScopy_(&blasn, mfqP->work, &ione, mfqP->work2, &ione));
544: PetscCallBLAS("BLASnrm2", normd = BLASnrm2_(&blasn, mfqP->work, &ione));
545: if (normd <= c) {
546: PetscCall(PetscBLASIntCast(PetscMax(mfqP->n - mfqP->nmodelpoints, 0), &blasj));
547: if (!mfqP->q_is_I) {
548: /* project D onto null */
549: PetscCall(PetscBLASIntCast(mfqP->nmodelpoints, &blask));
550: PetscCallBLAS("LAPACKormqr", LAPACKormqr_("R", "N", &ione, &blasn, &blask, mfqP->Q, &blasnpmax, mfqP->tau, mfqP->work2, &ione, mfqP->mwork, &blasm, &info));
551: PetscCheck(info >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "ormqr returned value %" PetscBLASInt_FMT, info);
552: }
553: PetscCallBLAS("BLASnrm2", proj = BLASnrm2_(&blasj, &mfqP->work2[mfqP->nmodelpoints], &ione));
555: if (proj >= mfqP->theta1) { /* add this index to model */
556: mfqP->model_indices[mfqP->nmodelpoints] = i;
557: mfqP->nmodelpoints++;
558: PetscCallBLAS("BLAScopy", BLAScopy_(&blasn, mfqP->work, &ione, &mfqP->Q_tmp[mfqP->npmax * (mfqP->nmodelpoints - 1)], &ione));
559: PetscCall(PetscBLASIntCast(mfqP->npmax * (mfqP->nmodelpoints), &blask));
560: PetscCallBLAS("BLAScopy", BLAScopy_(&blask, mfqP->Q_tmp, &ione, mfqP->Q, &ione));
561: PetscCall(PetscBLASIntCast(mfqP->nmodelpoints, &blask));
562: PetscCall(PetscBLASIntCast(PetscMax(mfqP->m, mfqP->n), &blasmaxmn));
563: PetscCallBLAS("LAPACKgeqrf", LAPACKgeqrf_(&blasn, &blask, mfqP->Q, &blasnpmax, mfqP->tau, mfqP->mwork, &blasmaxmn, &info));
564: PetscCheck(info >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "geqrf returned value %" PetscBLASInt_FMT, info);
565: mfqP->q_is_I = 0;
566: }
567: if (mfqP->nmodelpoints == mfqP->n) break;
568: }
569: }
570: PetscFunctionReturn(PETSC_SUCCESS);
571: }
573: static PetscErrorCode TaoSolve_POUNDERS(Tao tao)
574: {
575: TAO_POUNDERS *mfqP = (TAO_POUNDERS *)tao->data;
576: PetscInt i, ii, j, k, l;
577: PetscReal step = 1.0;
578: PetscInt low, high;
579: PetscReal minnorm;
580: PetscReal *x, *f;
581: const PetscReal *xmint, *fmin;
582: PetscReal deltaold;
583: PetscReal gnorm;
584: PetscBLASInt info, ione = 1, iblas;
585: PetscBool valid, same;
586: PetscReal mdec, rho, normxsp;
587: PetscReal one = 1.0, zero = 0.0, ratio;
588: PetscBLASInt blasm, blasn, blasncopy, blasnpmax;
589: static PetscBool set = PETSC_FALSE;
591: /* n = # of parameters
592: m = dimension (components) of function */
593: PetscFunctionBegin;
594: PetscCall(PetscCitationsRegister("@article{UNEDF0,\n"
595: "title = {Nuclear energy density optimization},\n"
596: "author = {Kortelainen, M. and Lesinski, T. and Mor\'e, J. and Nazarewicz, W.\n"
597: " and Sarich, J. and Schunck, N. and Stoitsov, M. V. and Wild, S. },\n"
598: "journal = {Phys. Rev. C},\n"
599: "volume = {82},\n"
600: "number = {2},\n"
601: "pages = {024313},\n"
602: "numpages = {18},\n"
603: "year = {2010},\n"
604: "month = {Aug},\n"
605: "doi = {10.1103/PhysRevC.82.024313}\n}\n",
606: &set));
607: tao->niter = 0;
608: if (tao->XL && tao->XU) {
609: /* Check x0 <= XU */
610: PetscReal val;
612: PetscCall(VecCopy(tao->solution, mfqP->Xhist[0]));
613: PetscCall(VecAXPY(mfqP->Xhist[0], -1.0, tao->XU));
614: PetscCall(VecMax(mfqP->Xhist[0], NULL, &val));
615: PetscCheck(val <= 1e-10, PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_OUTOFRANGE, "X0 > upper bound");
617: /* Check x0 >= xl */
618: PetscCall(VecCopy(tao->XL, mfqP->Xhist[0]));
619: PetscCall(VecAXPY(mfqP->Xhist[0], -1.0, tao->solution));
620: PetscCall(VecMax(mfqP->Xhist[0], NULL, &val));
621: PetscCheck(val <= 1e-10, PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_OUTOFRANGE, "X0 < lower bound");
623: /* Check x0 + delta < XU -- should be able to get around this eventually */
625: PetscCall(VecSet(mfqP->Xhist[0], mfqP->delta));
626: PetscCall(VecAXPY(mfqP->Xhist[0], 1.0, tao->solution));
627: PetscCall(VecAXPY(mfqP->Xhist[0], -1.0, tao->XU));
628: PetscCall(VecMax(mfqP->Xhist[0], NULL, &val));
629: PetscCheck(val <= 1e-10, PetscObjectComm((PetscObject)tao), PETSC_ERR_ARG_OUTOFRANGE, "X0 + delta > upper bound");
630: }
632: PetscCall(PetscBLASIntCast(mfqP->m, &blasm));
633: PetscCall(PetscBLASIntCast(mfqP->n, &blasn));
634: PetscCall(PetscBLASIntCast(mfqP->npmax, &blasnpmax));
635: for (i = 0; i < mfqP->n * mfqP->n * mfqP->m; ++i) mfqP->H[i] = 0;
637: PetscCall(VecCopy(tao->solution, mfqP->Xhist[0]));
639: /* This provides enough information to approximate the gradient of the objective */
640: /* using a forward difference scheme. */
642: PetscCall(PetscInfo(tao, "Initialize simplex; delta = %10.9e\n", (double)mfqP->delta));
643: PetscCall(pounders_feval(tao, mfqP->Xhist[0], mfqP->Fhist[0], &mfqP->Fres[0]));
644: mfqP->minindex = 0;
645: minnorm = mfqP->Fres[0];
647: PetscCall(VecGetOwnershipRange(mfqP->Xhist[0], &low, &high));
648: for (i = 1; i < mfqP->n + 1; ++i) {
649: PetscCall(VecCopy(mfqP->Xhist[0], mfqP->Xhist[i]));
651: if (i - 1 >= low && i - 1 < high) {
652: PetscCall(VecGetArray(mfqP->Xhist[i], &x));
653: x[i - 1 - low] += mfqP->delta;
654: PetscCall(VecRestoreArray(mfqP->Xhist[i], &x));
655: }
656: CHKMEMQ;
657: PetscCall(pounders_feval(tao, mfqP->Xhist[i], mfqP->Fhist[i], &mfqP->Fres[i]));
658: if (mfqP->Fres[i] < minnorm) {
659: mfqP->minindex = i;
660: minnorm = mfqP->Fres[i];
661: }
662: }
663: PetscCall(VecCopy(mfqP->Xhist[mfqP->minindex], tao->solution));
664: PetscCall(VecCopy(mfqP->Fhist[mfqP->minindex], tao->ls_res));
665: PetscCall(PetscInfo(tao, "Finalize simplex; minnorm = %10.9e\n", (double)minnorm));
667: /* Gather mpi vecs to one big local vec */
669: /* Begin serial code */
671: /* Disp[i] = Xi-xmin, i=1,..,mfqP->minindex-1,mfqP->minindex+1,..,n */
672: /* Fdiff[i] = (Fi-Fmin)', i=1,..,mfqP->minindex-1,mfqP->minindex+1,..,n */
673: /* (Column oriented for blas calls) */
674: ii = 0;
676: PetscCall(PetscInfo(tao, "Build matrix: %" PetscInt_FMT "\n", (PetscInt)mfqP->size));
677: if (1 == mfqP->size) {
678: PetscCall(VecGetArrayRead(mfqP->Xhist[mfqP->minindex], &xmint));
679: for (i = 0; i < mfqP->n; i++) mfqP->xmin[i] = xmint[i];
680: PetscCall(VecRestoreArrayRead(mfqP->Xhist[mfqP->minindex], &xmint));
681: PetscCall(VecGetArrayRead(mfqP->Fhist[mfqP->minindex], &fmin));
682: for (i = 0; i < mfqP->n + 1; i++) {
683: if (i == mfqP->minindex) continue;
685: PetscCall(VecGetArray(mfqP->Xhist[i], &x));
686: for (j = 0; j < mfqP->n; j++) mfqP->Disp[ii + mfqP->npmax * j] = (x[j] - mfqP->xmin[j]) / mfqP->delta;
687: PetscCall(VecRestoreArray(mfqP->Xhist[i], &x));
689: PetscCall(VecGetArray(mfqP->Fhist[i], &f));
690: for (j = 0; j < mfqP->m; j++) mfqP->Fdiff[ii + mfqP->n * j] = f[j] - fmin[j];
691: PetscCall(VecRestoreArray(mfqP->Fhist[i], &f));
693: mfqP->model_indices[ii++] = i;
694: }
695: for (j = 0; j < mfqP->m; j++) mfqP->C[j] = fmin[j];
696: PetscCall(VecRestoreArrayRead(mfqP->Fhist[mfqP->minindex], &fmin));
697: } else {
698: PetscCall(VecSet(mfqP->localxmin, 0));
699: PetscCall(VecScatterBegin(mfqP->scatterx, mfqP->Xhist[mfqP->minindex], mfqP->localxmin, INSERT_VALUES, SCATTER_FORWARD));
700: PetscCall(VecScatterEnd(mfqP->scatterx, mfqP->Xhist[mfqP->minindex], mfqP->localxmin, INSERT_VALUES, SCATTER_FORWARD));
702: PetscCall(VecGetArrayRead(mfqP->localxmin, &xmint));
703: for (i = 0; i < mfqP->n; i++) mfqP->xmin[i] = xmint[i];
704: PetscCall(VecRestoreArrayRead(mfqP->localxmin, &xmint));
706: PetscCall(VecScatterBegin(mfqP->scatterf, mfqP->Fhist[mfqP->minindex], mfqP->localfmin, INSERT_VALUES, SCATTER_FORWARD));
707: PetscCall(VecScatterEnd(mfqP->scatterf, mfqP->Fhist[mfqP->minindex], mfqP->localfmin, INSERT_VALUES, SCATTER_FORWARD));
708: PetscCall(VecGetArrayRead(mfqP->localfmin, &fmin));
709: for (i = 0; i < mfqP->n + 1; i++) {
710: if (i == mfqP->minindex) continue;
712: PetscCall(VecScatterBegin(mfqP->scatterx, mfqP->Xhist[ii], mfqP->localx, INSERT_VALUES, SCATTER_FORWARD));
713: PetscCall(VecScatterEnd(mfqP->scatterx, mfqP->Xhist[ii], mfqP->localx, INSERT_VALUES, SCATTER_FORWARD));
714: PetscCall(VecGetArray(mfqP->localx, &x));
715: for (j = 0; j < mfqP->n; j++) mfqP->Disp[ii + mfqP->npmax * j] = (x[j] - mfqP->xmin[j]) / mfqP->delta;
716: PetscCall(VecRestoreArray(mfqP->localx, &x));
718: PetscCall(VecScatterBegin(mfqP->scatterf, mfqP->Fhist[ii], mfqP->localf, INSERT_VALUES, SCATTER_FORWARD));
719: PetscCall(VecScatterEnd(mfqP->scatterf, mfqP->Fhist[ii], mfqP->localf, INSERT_VALUES, SCATTER_FORWARD));
720: PetscCall(VecGetArray(mfqP->localf, &f));
721: for (j = 0; j < mfqP->m; j++) mfqP->Fdiff[ii + mfqP->n * j] = f[j] - fmin[j];
722: PetscCall(VecRestoreArray(mfqP->localf, &f));
724: mfqP->model_indices[ii++] = i;
725: }
726: for (j = 0; j < mfqP->m; j++) mfqP->C[j] = fmin[j];
727: PetscCall(VecRestoreArrayRead(mfqP->localfmin, &fmin));
728: }
730: /* Determine the initial quadratic models */
731: /* G = D(ModelIn,:) \ (F(ModelIn,1:m)-repmat(F(xkin,1:m),n,1)); */
732: /* D (nxn) Fdiff (nxm) => G (nxm) */
733: blasncopy = blasn;
734: PetscCallBLAS("LAPACKgesv", LAPACKgesv_(&blasn, &blasm, mfqP->Disp, &blasnpmax, mfqP->iwork, mfqP->Fdiff, &blasncopy, &info));
735: PetscCall(PetscInfo(tao, "Linear solve return: %" PetscInt_FMT "\n", (PetscInt)info));
737: PetscCall(pounders_update_res(tao));
739: valid = PETSC_TRUE;
741: PetscCall(VecSetValues(tao->gradient, mfqP->n, mfqP->indices, mfqP->Gres, INSERT_VALUES));
742: PetscCall(VecAssemblyBegin(tao->gradient));
743: PetscCall(VecAssemblyEnd(tao->gradient));
744: PetscCall(VecNorm(tao->gradient, NORM_2, &gnorm));
745: gnorm *= mfqP->delta;
746: PetscCall(VecCopy(mfqP->Xhist[mfqP->minindex], tao->solution));
748: tao->reason = TAO_CONTINUE_ITERATING;
749: PetscCall(TaoLogConvergenceHistory(tao, minnorm, gnorm, 0.0, tao->ksp_its));
750: PetscCall(TaoMonitor(tao, tao->niter, minnorm, gnorm, 0.0, step));
751: PetscUseTypeMethod(tao, convergencetest, tao->cnvP);
753: mfqP->nHist = mfqP->n + 1;
754: mfqP->nmodelpoints = mfqP->n + 1;
755: PetscCall(PetscInfo(tao, "Initial gradient: %20.19e\n", (double)gnorm));
757: while (tao->reason == TAO_CONTINUE_ITERATING) {
758: PetscReal gnm = 1e-4;
759: /* Call general purpose update function */
760: PetscTryTypeMethod(tao, update, tao->niter, tao->user_update);
761: tao->niter++;
762: /* Solve the subproblem min{Q(s): ||s|| <= 1.0} */
763: PetscCall(gqtwrap(tao, &gnm, &mdec));
764: /* Evaluate the function at the new point */
766: for (i = 0; i < mfqP->n; i++) mfqP->work[i] = mfqP->Xsubproblem[i] * mfqP->delta + mfqP->xmin[i];
767: PetscCall(VecDuplicate(tao->solution, &mfqP->Xhist[mfqP->nHist]));
768: PetscCall(VecDuplicate(tao->ls_res, &mfqP->Fhist[mfqP->nHist]));
769: PetscCall(VecSetValues(mfqP->Xhist[mfqP->nHist], mfqP->n, mfqP->indices, mfqP->work, INSERT_VALUES));
770: PetscCall(VecAssemblyBegin(mfqP->Xhist[mfqP->nHist]));
771: PetscCall(VecAssemblyEnd(mfqP->Xhist[mfqP->nHist]));
773: PetscCall(pounders_feval(tao, mfqP->Xhist[mfqP->nHist], mfqP->Fhist[mfqP->nHist], &mfqP->Fres[mfqP->nHist]));
775: rho = (mfqP->Fres[mfqP->minindex] - mfqP->Fres[mfqP->nHist]) / mdec;
776: mfqP->nHist++;
778: /* Update the center */
779: if ((rho >= mfqP->eta1) || (rho > mfqP->eta0 && valid == PETSC_TRUE)) {
780: /* Update model to reflect new base point */
781: for (i = 0; i < mfqP->n; i++) mfqP->work[i] = (mfqP->work[i] - mfqP->xmin[i]) / mfqP->delta;
782: for (j = 0; j < mfqP->m; j++) {
783: /* C(j) = C(j) + work*G(:,j) + .5*work*H(:,:,j)*work';
784: G(:,j) = G(:,j) + H(:,:,j)*work' */
785: for (k = 0; k < mfqP->n; k++) {
786: mfqP->work2[k] = 0.0;
787: for (l = 0; l < mfqP->n; l++) mfqP->work2[k] += mfqP->H[j + mfqP->m * (k + l * mfqP->n)] * mfqP->work[l];
788: }
789: for (i = 0; i < mfqP->n; i++) {
790: mfqP->C[j] += mfqP->work[i] * (mfqP->Fdiff[i + mfqP->n * j] + 0.5 * mfqP->work2[i]);
791: mfqP->Fdiff[i + mfqP->n * j] += mfqP->work2[i];
792: }
793: }
794: /* Cres += work*Gres + .5*work*Hres*work';
795: Gres += Hres*work'; */
797: PetscCallBLAS("BLASgemv", BLASgemv_("N", &blasn, &blasn, &one, mfqP->Hres, &blasn, mfqP->work, &ione, &zero, mfqP->work2, &ione));
798: for (i = 0; i < mfqP->n; i++) mfqP->Gres[i] += mfqP->work2[i];
799: mfqP->minindex = mfqP->nHist - 1;
800: minnorm = mfqP->Fres[mfqP->minindex];
801: PetscCall(VecCopy(mfqP->Fhist[mfqP->minindex], tao->ls_res));
802: /* Change current center */
803: PetscCall(VecGetArrayRead(mfqP->Xhist[mfqP->minindex], &xmint));
804: for (i = 0; i < mfqP->n; i++) mfqP->xmin[i] = xmint[i];
805: PetscCall(VecRestoreArrayRead(mfqP->Xhist[mfqP->minindex], &xmint));
806: }
808: /* Evaluate at a model-improving point if necessary */
809: if (valid == PETSC_FALSE) {
810: mfqP->q_is_I = 1;
811: mfqP->nmodelpoints = 0;
812: PetscCall(affpoints(mfqP, mfqP->xmin, mfqP->c1));
813: if (mfqP->nmodelpoints < mfqP->n) {
814: PetscCall(PetscInfo(tao, "Model not valid -- model-improving\n"));
815: PetscCall(modelimprove(tao, mfqP, 1));
816: }
817: }
819: /* Update the trust region radius */
820: deltaold = mfqP->delta;
821: normxsp = 0;
822: for (i = 0; i < mfqP->n; i++) normxsp += mfqP->Xsubproblem[i] * mfqP->Xsubproblem[i];
823: normxsp = PetscSqrtReal(normxsp);
824: if (rho >= mfqP->eta1 && normxsp > 0.5 * mfqP->delta) {
825: mfqP->delta = PetscMin(mfqP->delta * mfqP->gamma1, mfqP->deltamax);
826: } else if (valid == PETSC_TRUE) {
827: mfqP->delta = PetscMax(mfqP->delta * mfqP->gamma0, mfqP->deltamin);
828: }
830: /* Compute the next interpolation set */
831: mfqP->q_is_I = 1;
832: mfqP->nmodelpoints = 0;
833: PetscCall(PetscInfo(tao, "Affine Points: xmin = %20.19e, c1 = %20.19e\n", (double)*mfqP->xmin, (double)mfqP->c1));
834: PetscCall(affpoints(mfqP, mfqP->xmin, mfqP->c1));
835: if (mfqP->nmodelpoints == mfqP->n) {
836: valid = PETSC_TRUE;
837: } else {
838: valid = PETSC_FALSE;
839: PetscCall(PetscInfo(tao, "Affine Points: xmin = %20.19e, c2 = %20.19e\n", (double)*mfqP->xmin, (double)mfqP->c2));
840: PetscCall(affpoints(mfqP, mfqP->xmin, mfqP->c2));
841: if (mfqP->n > mfqP->nmodelpoints) {
842: PetscCall(PetscInfo(tao, "Model not valid -- adding geometry points\n"));
843: PetscCall(modelimprove(tao, mfqP, mfqP->n - mfqP->nmodelpoints));
844: }
845: }
846: for (i = mfqP->nmodelpoints; i > 0; i--) mfqP->model_indices[i] = mfqP->model_indices[i - 1];
847: mfqP->nmodelpoints++;
848: mfqP->model_indices[0] = mfqP->minindex;
849: PetscCall(morepoints(mfqP));
850: for (i = 0; i < mfqP->nmodelpoints; i++) {
851: PetscCall(VecGetArray(mfqP->Xhist[mfqP->model_indices[i]], &x));
852: for (j = 0; j < mfqP->n; j++) mfqP->Disp[i + mfqP->npmax * j] = (x[j] - mfqP->xmin[j]) / deltaold;
853: PetscCall(VecRestoreArray(mfqP->Xhist[mfqP->model_indices[i]], &x));
854: PetscCall(VecGetArray(mfqP->Fhist[mfqP->model_indices[i]], &f));
855: for (j = 0; j < mfqP->m; j++) {
856: for (k = 0; k < mfqP->n; k++) {
857: mfqP->work[k] = 0.0;
858: for (l = 0; l < mfqP->n; l++) mfqP->work[k] += mfqP->H[j + mfqP->m * (k + mfqP->n * l)] * mfqP->Disp[i + mfqP->npmax * l];
859: }
860: PetscCallBLAS("BLASdot", mfqP->RES[j * mfqP->npmax + i] = -mfqP->C[j] - BLASdot_(&blasn, &mfqP->Fdiff[j * mfqP->n], &ione, &mfqP->Disp[i], &blasnpmax) - 0.5 * BLASdot_(&blasn, mfqP->work, &ione, &mfqP->Disp[i], &blasnpmax) + f[j]);
861: }
862: PetscCall(VecRestoreArray(mfqP->Fhist[mfqP->model_indices[i]], &f));
863: }
865: /* Update the quadratic model */
866: PetscCall(PetscInfo(tao, "Get Quad, size: %" PetscInt_FMT ", points: %" PetscInt_FMT "\n", mfqP->n, mfqP->nmodelpoints));
867: PetscCall(getquadpounders(mfqP));
868: PetscCall(VecGetArrayRead(mfqP->Fhist[mfqP->minindex], &fmin));
869: PetscCallBLAS("BLAScopy", BLAScopy_(&blasm, fmin, &ione, mfqP->C, &ione));
870: /* G = G*(delta/deltaold) + Gdel */
871: ratio = mfqP->delta / deltaold;
872: iblas = blasm * blasn;
873: PetscCallBLAS("BLASscal", BLASscal_(&iblas, &ratio, mfqP->Fdiff, &ione));
874: PetscCallBLAS("BLASaxpy", BLASaxpy_(&iblas, &one, mfqP->Gdel, &ione, mfqP->Fdiff, &ione));
875: /* H = H*(delta/deltaold)^2 + Hdel */
876: iblas = blasm * blasn * blasn;
877: ratio *= ratio;
878: PetscCallBLAS("BLASscal", BLASscal_(&iblas, &ratio, mfqP->H, &ione));
879: PetscCallBLAS("BLASaxpy", BLASaxpy_(&iblas, &one, mfqP->Hdel, &ione, mfqP->H, &ione));
881: /* Get residuals */
882: PetscCall(pounders_update_res(tao));
884: /* Export solution and gradient residual to TAO */
885: PetscCall(VecCopy(mfqP->Xhist[mfqP->minindex], tao->solution));
886: PetscCall(VecSetValues(tao->gradient, mfqP->n, mfqP->indices, mfqP->Gres, INSERT_VALUES));
887: PetscCall(VecAssemblyBegin(tao->gradient));
888: PetscCall(VecAssemblyEnd(tao->gradient));
889: PetscCall(VecNorm(tao->gradient, NORM_2, &gnorm));
890: gnorm *= mfqP->delta;
891: /* final criticality test */
892: PetscCall(TaoLogConvergenceHistory(tao, minnorm, gnorm, 0.0, tao->ksp_its));
893: PetscCall(TaoMonitor(tao, tao->niter, minnorm, gnorm, 0.0, step));
894: PetscUseTypeMethod(tao, convergencetest, tao->cnvP);
895: /* test for repeated model */
896: if (mfqP->nmodelpoints == mfqP->last_nmodelpoints) {
897: same = PETSC_TRUE;
898: } else {
899: same = PETSC_FALSE;
900: }
901: for (i = 0; i < mfqP->nmodelpoints; i++) {
902: if (same) {
903: if (mfqP->model_indices[i] == mfqP->last_model_indices[i]) {
904: same = PETSC_TRUE;
905: } else {
906: same = PETSC_FALSE;
907: }
908: }
909: mfqP->last_model_indices[i] = mfqP->model_indices[i];
910: }
911: mfqP->last_nmodelpoints = mfqP->nmodelpoints;
912: if (same && mfqP->delta == deltaold) {
913: PetscCall(PetscInfo(tao, "Identical model used in successive iterations\n"));
914: tao->reason = TAO_CONVERGED_STEPTOL;
915: }
916: }
917: PetscFunctionReturn(PETSC_SUCCESS);
918: }
920: static PetscErrorCode TaoSetUp_POUNDERS(Tao tao)
921: {
922: TAO_POUNDERS *mfqP = (TAO_POUNDERS *)tao->data;
923: PetscInt i, j;
924: IS isfloc, isfglob, isxloc, isxglob;
926: PetscFunctionBegin;
927: if (!tao->gradient) PetscCall(VecDuplicate(tao->solution, &tao->gradient));
928: if (!tao->stepdirection) PetscCall(VecDuplicate(tao->solution, &tao->stepdirection));
929: PetscCall(VecGetSize(tao->solution, &mfqP->n));
930: PetscCall(VecGetSize(tao->ls_res, &mfqP->m));
931: mfqP->c1 = PetscSqrtReal((PetscReal)mfqP->n);
932: if (mfqP->npmax == PETSC_CURRENT) mfqP->npmax = 2 * mfqP->n + 1;
933: mfqP->npmax = PetscMin((mfqP->n + 1) * (mfqP->n + 2) / 2, mfqP->npmax);
934: mfqP->npmax = PetscMax(mfqP->npmax, mfqP->n + 2);
936: PetscCall(PetscMalloc1(tao->max_funcs + 100, &mfqP->Xhist));
937: PetscCall(PetscMalloc1(tao->max_funcs + 100, &mfqP->Fhist));
938: for (i = 0; i < mfqP->n + 1; i++) {
939: PetscCall(VecDuplicate(tao->solution, &mfqP->Xhist[i]));
940: PetscCall(VecDuplicate(tao->ls_res, &mfqP->Fhist[i]));
941: }
942: PetscCall(VecDuplicate(tao->solution, &mfqP->workxvec));
943: PetscCall(VecDuplicate(tao->ls_res, &mfqP->workfvec));
944: mfqP->nHist = 0;
946: PetscCall(PetscMalloc1(tao->max_funcs + 100, &mfqP->Fres));
947: PetscCall(PetscMalloc1(mfqP->npmax * mfqP->m, &mfqP->RES));
948: PetscCall(PetscMalloc1(mfqP->n, &mfqP->work));
949: PetscCall(PetscMalloc1(mfqP->n, &mfqP->work2));
950: PetscCall(PetscMalloc1(mfqP->n, &mfqP->work3));
951: PetscCall(PetscMalloc1(PetscMax(mfqP->m, mfqP->n + 1), &mfqP->mwork));
952: PetscCall(PetscMalloc1(mfqP->npmax - mfqP->n - 1, &mfqP->omega));
953: PetscCall(PetscMalloc1(mfqP->n * (mfqP->n + 1) / 2, &mfqP->beta));
954: PetscCall(PetscMalloc1(mfqP->n + 1, &mfqP->alpha));
956: PetscCall(PetscMalloc1(mfqP->n * mfqP->n * mfqP->m, &mfqP->H));
957: PetscCall(PetscMalloc1(mfqP->npmax * mfqP->npmax, &mfqP->Q));
958: PetscCall(PetscMalloc1(mfqP->npmax * mfqP->npmax, &mfqP->Q_tmp));
959: PetscCall(PetscMalloc1(mfqP->n * (mfqP->n + 1) / 2 * (mfqP->npmax), &mfqP->L));
960: PetscCall(PetscMalloc1(mfqP->n * (mfqP->n + 1) / 2 * (mfqP->npmax), &mfqP->L_tmp));
961: PetscCall(PetscMalloc1(mfqP->n * (mfqP->n + 1) / 2 * (mfqP->npmax), &mfqP->L_save));
962: PetscCall(PetscMalloc1(mfqP->n * (mfqP->n + 1) / 2 * (mfqP->npmax), &mfqP->N));
963: PetscCall(PetscMalloc1(mfqP->npmax * (mfqP->n + 1), &mfqP->M));
964: PetscCall(PetscMalloc1(mfqP->npmax * (mfqP->npmax - mfqP->n - 1), &mfqP->Z));
965: PetscCall(PetscMalloc1(mfqP->npmax, &mfqP->tau));
966: PetscCall(PetscMalloc1(mfqP->npmax, &mfqP->tau_tmp));
967: mfqP->nmax = PetscMax(5 * mfqP->npmax, mfqP->n * (mfqP->n + 1) / 2);
968: PetscCall(PetscMalloc1(mfqP->nmax, &mfqP->npmaxwork));
969: PetscCall(PetscMalloc1(mfqP->nmax, &mfqP->npmaxiwork));
970: PetscCall(PetscMalloc1(mfqP->n, &mfqP->xmin));
971: PetscCall(PetscMalloc1(mfqP->m, &mfqP->C));
972: PetscCall(PetscMalloc1(mfqP->m * mfqP->n, &mfqP->Fdiff));
973: PetscCall(PetscMalloc1(mfqP->npmax * mfqP->n, &mfqP->Disp));
974: PetscCall(PetscMalloc1(mfqP->n, &mfqP->Gres));
975: PetscCall(PetscMalloc1(mfqP->n * mfqP->n, &mfqP->Hres));
976: PetscCall(PetscMalloc1(mfqP->n * mfqP->n, &mfqP->Gpoints));
977: PetscCall(PetscMalloc1(mfqP->npmax, &mfqP->model_indices));
978: PetscCall(PetscMalloc1(mfqP->npmax, &mfqP->last_model_indices));
979: PetscCall(PetscMalloc1(mfqP->n, &mfqP->Xsubproblem));
980: PetscCall(PetscMalloc1(mfqP->m * mfqP->n, &mfqP->Gdel));
981: PetscCall(PetscMalloc1(mfqP->n * mfqP->n * mfqP->m, &mfqP->Hdel));
982: PetscCall(PetscMalloc1(PetscMax(mfqP->m, mfqP->n), &mfqP->indices));
983: PetscCall(PetscMalloc1(mfqP->n, &mfqP->iwork));
984: PetscCall(PetscMalloc1(mfqP->m * mfqP->m, &mfqP->w));
985: for (i = 0; i < mfqP->m; i++) {
986: for (j = 0; j < mfqP->m; j++) {
987: if (i == j) {
988: mfqP->w[i + mfqP->m * j] = 1.0;
989: } else {
990: mfqP->w[i + mfqP->m * j] = 0.0;
991: }
992: }
993: }
994: for (i = 0; i < PetscMax(mfqP->m, mfqP->n); i++) mfqP->indices[i] = i;
995: PetscCallMPI(MPI_Comm_size(((PetscObject)tao)->comm, &mfqP->size));
996: if (mfqP->size > 1) {
997: PetscCall(VecCreateSeq(PETSC_COMM_SELF, mfqP->n, &mfqP->localx));
998: PetscCall(VecCreateSeq(PETSC_COMM_SELF, mfqP->n, &mfqP->localxmin));
999: PetscCall(VecCreateSeq(PETSC_COMM_SELF, mfqP->m, &mfqP->localf));
1000: PetscCall(VecCreateSeq(PETSC_COMM_SELF, mfqP->m, &mfqP->localfmin));
1001: PetscCall(ISCreateStride(MPI_COMM_SELF, mfqP->n, 0, 1, &isxloc));
1002: PetscCall(ISCreateStride(MPI_COMM_SELF, mfqP->n, 0, 1, &isxglob));
1003: PetscCall(ISCreateStride(MPI_COMM_SELF, mfqP->m, 0, 1, &isfloc));
1004: PetscCall(ISCreateStride(MPI_COMM_SELF, mfqP->m, 0, 1, &isfglob));
1006: PetscCall(VecScatterCreate(tao->solution, isxglob, mfqP->localx, isxloc, &mfqP->scatterx));
1007: PetscCall(VecScatterCreate(tao->ls_res, isfglob, mfqP->localf, isfloc, &mfqP->scatterf));
1009: PetscCall(ISDestroy(&isxloc));
1010: PetscCall(ISDestroy(&isxglob));
1011: PetscCall(ISDestroy(&isfloc));
1012: PetscCall(ISDestroy(&isfglob));
1013: }
1015: if (!mfqP->usegqt) {
1016: KSP ksp;
1017: PC pc;
1018: PetscCall(VecCreateSeqWithArray(PETSC_COMM_SELF, mfqP->n, mfqP->n, mfqP->Xsubproblem, &mfqP->subx));
1019: PetscCall(VecCreateSeq(PETSC_COMM_SELF, mfqP->n, &mfqP->subxl));
1020: PetscCall(VecDuplicate(mfqP->subxl, &mfqP->subb));
1021: PetscCall(VecDuplicate(mfqP->subxl, &mfqP->subxu));
1022: PetscCall(VecDuplicate(mfqP->subxl, &mfqP->subpdel));
1023: PetscCall(VecDuplicate(mfqP->subxl, &mfqP->subndel));
1024: PetscCall(TaoCreate(PETSC_COMM_SELF, &mfqP->subtao));
1025: PetscCall(PetscObjectIncrementTabLevel((PetscObject)mfqP->subtao, (PetscObject)tao, 1));
1026: PetscCall(TaoSetType(mfqP->subtao, TAOBNTR));
1027: PetscCall(TaoSetOptionsPrefix(mfqP->subtao, "pounders_subsolver_"));
1028: PetscCall(TaoSetSolution(mfqP->subtao, mfqP->subx));
1029: PetscCall(TaoSetObjectiveAndGradient(mfqP->subtao, NULL, pounders_fg, (void *)mfqP));
1030: PetscCall(TaoSetMaximumIterations(mfqP->subtao, mfqP->gqt_maxits));
1031: PetscCall(TaoSetFromOptions(mfqP->subtao));
1032: PetscCall(TaoGetKSP(mfqP->subtao, &ksp));
1033: if (ksp) {
1034: PetscCall(KSPGetPC(ksp, &pc));
1035: PetscCall(PCSetType(pc, PCNONE));
1036: }
1037: PetscCall(TaoSetVariableBounds(mfqP->subtao, mfqP->subxl, mfqP->subxu));
1038: PetscCall(MatCreateSeqDense(PETSC_COMM_SELF, mfqP->n, mfqP->n, mfqP->Hres, &mfqP->subH));
1039: PetscCall(TaoSetHessian(mfqP->subtao, mfqP->subH, mfqP->subH, pounders_h, (void *)mfqP));
1040: }
1041: PetscFunctionReturn(PETSC_SUCCESS);
1042: }
1044: static PetscErrorCode TaoDestroy_POUNDERS(Tao tao)
1045: {
1046: TAO_POUNDERS *mfqP = (TAO_POUNDERS *)tao->data;
1047: PetscInt i;
1049: PetscFunctionBegin;
1050: if (!mfqP->usegqt) {
1051: PetscCall(TaoDestroy(&mfqP->subtao));
1052: PetscCall(VecDestroy(&mfqP->subx));
1053: PetscCall(VecDestroy(&mfqP->subxl));
1054: PetscCall(VecDestroy(&mfqP->subxu));
1055: PetscCall(VecDestroy(&mfqP->subb));
1056: PetscCall(VecDestroy(&mfqP->subpdel));
1057: PetscCall(VecDestroy(&mfqP->subndel));
1058: PetscCall(MatDestroy(&mfqP->subH));
1059: }
1060: PetscCall(PetscFree(mfqP->Fres));
1061: PetscCall(PetscFree(mfqP->RES));
1062: PetscCall(PetscFree(mfqP->work));
1063: PetscCall(PetscFree(mfqP->work2));
1064: PetscCall(PetscFree(mfqP->work3));
1065: PetscCall(PetscFree(mfqP->mwork));
1066: PetscCall(PetscFree(mfqP->omega));
1067: PetscCall(PetscFree(mfqP->beta));
1068: PetscCall(PetscFree(mfqP->alpha));
1069: PetscCall(PetscFree(mfqP->H));
1070: PetscCall(PetscFree(mfqP->Q));
1071: PetscCall(PetscFree(mfqP->Q_tmp));
1072: PetscCall(PetscFree(mfqP->L));
1073: PetscCall(PetscFree(mfqP->L_tmp));
1074: PetscCall(PetscFree(mfqP->L_save));
1075: PetscCall(PetscFree(mfqP->N));
1076: PetscCall(PetscFree(mfqP->M));
1077: PetscCall(PetscFree(mfqP->Z));
1078: PetscCall(PetscFree(mfqP->tau));
1079: PetscCall(PetscFree(mfqP->tau_tmp));
1080: PetscCall(PetscFree(mfqP->npmaxwork));
1081: PetscCall(PetscFree(mfqP->npmaxiwork));
1082: PetscCall(PetscFree(mfqP->xmin));
1083: PetscCall(PetscFree(mfqP->C));
1084: PetscCall(PetscFree(mfqP->Fdiff));
1085: PetscCall(PetscFree(mfqP->Disp));
1086: PetscCall(PetscFree(mfqP->Gres));
1087: PetscCall(PetscFree(mfqP->Hres));
1088: PetscCall(PetscFree(mfqP->Gpoints));
1089: PetscCall(PetscFree(mfqP->model_indices));
1090: PetscCall(PetscFree(mfqP->last_model_indices));
1091: PetscCall(PetscFree(mfqP->Xsubproblem));
1092: PetscCall(PetscFree(mfqP->Gdel));
1093: PetscCall(PetscFree(mfqP->Hdel));
1094: PetscCall(PetscFree(mfqP->indices));
1095: PetscCall(PetscFree(mfqP->iwork));
1096: PetscCall(PetscFree(mfqP->w));
1097: for (i = 0; i < mfqP->nHist; i++) {
1098: PetscCall(VecDestroy(&mfqP->Xhist[i]));
1099: PetscCall(VecDestroy(&mfqP->Fhist[i]));
1100: }
1101: PetscCall(VecDestroy(&mfqP->workxvec));
1102: PetscCall(VecDestroy(&mfqP->workfvec));
1103: PetscCall(PetscFree(mfqP->Xhist));
1104: PetscCall(PetscFree(mfqP->Fhist));
1106: if (mfqP->size > 1) {
1107: PetscCall(VecDestroy(&mfqP->localx));
1108: PetscCall(VecDestroy(&mfqP->localxmin));
1109: PetscCall(VecDestroy(&mfqP->localf));
1110: PetscCall(VecDestroy(&mfqP->localfmin));
1111: }
1112: PetscCall(PetscFree(tao->data));
1113: PetscFunctionReturn(PETSC_SUCCESS);
1114: }
1116: static PetscErrorCode TaoSetFromOptions_POUNDERS(Tao tao, PetscOptionItems *PetscOptionsObject)
1117: {
1118: TAO_POUNDERS *mfqP = (TAO_POUNDERS *)tao->data;
1120: PetscFunctionBegin;
1121: PetscOptionsHeadBegin(PetscOptionsObject, "POUNDERS method for least-squares optimization");
1122: PetscCall(PetscOptionsReal("-tao_pounders_delta", "initial delta", "", mfqP->delta, &mfqP->delta0, NULL));
1123: mfqP->delta = mfqP->delta0;
1124: PetscCall(PetscOptionsInt("-tao_pounders_npmax", "max number of points in model", "", mfqP->npmax, &mfqP->npmax, NULL));
1125: PetscCall(PetscOptionsBool("-tao_pounders_gqt", "use gqt algorithm for subproblem", "", mfqP->usegqt, &mfqP->usegqt, NULL));
1126: PetscOptionsHeadEnd();
1127: PetscFunctionReturn(PETSC_SUCCESS);
1128: }
1130: static PetscErrorCode TaoView_POUNDERS(Tao tao, PetscViewer viewer)
1131: {
1132: TAO_POUNDERS *mfqP = (TAO_POUNDERS *)tao->data;
1133: PetscBool isascii;
1135: PetscFunctionBegin;
1136: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1137: if (isascii) {
1138: PetscCall(PetscViewerASCIIPrintf(viewer, "initial delta: %g\n", (double)mfqP->delta0));
1139: PetscCall(PetscViewerASCIIPrintf(viewer, "final delta: %g\n", (double)mfqP->delta));
1140: PetscCall(PetscViewerASCIIPrintf(viewer, "model points: %" PetscInt_FMT "\n", mfqP->nmodelpoints));
1141: if (mfqP->usegqt) {
1142: PetscCall(PetscViewerASCIIPrintf(viewer, "subproblem solver: gqt\n"));
1143: } else {
1144: PetscCall(TaoView(mfqP->subtao, viewer));
1145: }
1146: }
1147: PetscFunctionReturn(PETSC_SUCCESS);
1148: }
1149: /*MC
1150: TAOPOUNDERS - POUNDERS derivate-free model-based algorithm for nonlinear least squares
1152: Options Database Keys:
1153: + -tao_pounders_delta - initial step length
1154: . -tao_pounders_npmax - maximum number of points in model
1155: - -tao_pounders_gqt - use gqt algorithm for subproblem instead of TRON
1157: Level: beginner
1159: M*/
1161: PETSC_EXTERN PetscErrorCode TaoCreate_POUNDERS(Tao tao)
1162: {
1163: TAO_POUNDERS *mfqP = (TAO_POUNDERS *)tao->data;
1165: PetscFunctionBegin;
1166: tao->ops->setup = TaoSetUp_POUNDERS;
1167: tao->ops->solve = TaoSolve_POUNDERS;
1168: tao->ops->view = TaoView_POUNDERS;
1169: tao->ops->setfromoptions = TaoSetFromOptions_POUNDERS;
1170: tao->ops->destroy = TaoDestroy_POUNDERS;
1172: PetscCall(PetscNew(&mfqP));
1173: tao->data = (void *)mfqP;
1175: /* Override default settings (unless already changed) */
1176: PetscCall(TaoParametersInitialize(tao));
1177: PetscObjectParameterSetDefault(tao, max_it, 2000);
1178: PetscObjectParameterSetDefault(tao, max_funcs, 4000);
1180: mfqP->npmax = PETSC_CURRENT;
1181: mfqP->delta0 = 0.1;
1182: mfqP->delta = 0.1;
1183: mfqP->deltamax = 1e3;
1184: mfqP->deltamin = 1e-6;
1185: mfqP->c2 = 10.0;
1186: mfqP->theta1 = 1e-5;
1187: mfqP->theta2 = 1e-4;
1188: mfqP->gamma0 = 0.5;
1189: mfqP->gamma1 = 2.0;
1190: mfqP->eta0 = 0.0;
1191: mfqP->eta1 = 0.1;
1192: mfqP->usegqt = PETSC_FALSE;
1193: mfqP->gqt_rtol = 0.001;
1194: mfqP->gqt_maxits = 50;
1195: mfqP->workxvec = NULL;
1196: PetscFunctionReturn(PETSC_SUCCESS);
1197: }