| /petsc/src/tao/unconstrained/impls/ntr/ |
| H A D | ntr.c | 71 tao->trust = tao->trust0; in TaoSolve_NTR() 72 tao->trust = PetscMax(tao->trust, tr->min_radius); in TaoSolve_NTR() 73 tao->trust = PetscMin(tao->trust, tr->max_radius); in TaoSolve_NTR() 123 PetscCall(VecAXPY(tr->W, -tao->trust / gnorm, tao->gradient)); in TaoSolve_NTR() 131 sigma = -tao->trust / gnorm; in TaoSolve_NTR() 137 prered = tao->trust * (gnorm - 0.5 * tao->trust * prered / (gnorm * gnorm)); in TaoSolve_NTR() 145 …tau_1 = tr->theta_i * gnorm * tao->trust / (tr->theta_i * gnorm * tao->trust + (1.0 - tr->theta_… in TaoSolve_NTR() 146 …tau_2 = tr->theta_i * gnorm * tao->trust / (tr->theta_i * gnorm * tao->trust - (1.0 + tr->theta_… in TaoSolve_NTR() 152 max_radius = PetscMax(max_radius, tao->trust); in TaoSolve_NTR() 163 max_radius = PetscMax(max_radius, tao->trust); in TaoSolve_NTR() [all …]
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| /petsc/src/tao/unconstrained/impls/ntl/ |
| H A D | ntl.c | 65 tao->trust = tao->trust0; in TaoSolve_NTL() 66 tao->trust = PetscMax(tao->trust, tl->min_radius); in TaoSolve_NTL() 67 tao->trust = PetscMin(tao->trust, tl->max_radius); in TaoSolve_NTL() 117 PetscCall(VecAXPY(tl->W, -tao->trust / gnorm, tao->gradient)); in TaoSolve_NTL() 125 sigma = -tao->trust / gnorm; in TaoSolve_NTL() 131 prered = tao->trust * (gnorm - 0.5 * tao->trust * prered / (gnorm * gnorm)); in TaoSolve_NTL() 139 …tau_1 = tl->theta_i * gnorm * tao->trust / (tl->theta_i * gnorm * tao->trust + (1.0 - tl->theta_… in TaoSolve_NTL() 140 …tau_2 = tl->theta_i * gnorm * tao->trust / (tl->theta_i * gnorm * tao->trust - (1.0 + tl->theta_… in TaoSolve_NTL() 146 max_radius = PetscMax(max_radius, tao->trust); in TaoSolve_NTL() 161 max_radius = PetscMax(max_radius, tao->trust); in TaoSolve_NTL() [all …]
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| /petsc/src/tao/unconstrained/impls/nls/ |
| H A D | nls.c | 90 tao->trust = tao->trust0; in TaoSolve_NLS() 91 tao->trust = PetscMax(tao->trust, nlsP->min_radius); in TaoSolve_NLS() 92 tao->trust = PetscMin(tao->trust, nlsP->max_radius); in TaoSolve_NLS() 144 PetscCall(VecAXPY(nlsP->W, -tao->trust / gnorm, tao->gradient)); in TaoSolve_NLS() 151 sigma = -tao->trust / gnorm; in TaoSolve_NLS() 157 prered = tao->trust * (gnorm - 0.5 * tao->trust * prered / (gnorm * gnorm)); in TaoSolve_NLS() 165 …tau_1 = nlsP->theta_i * gnorm * tao->trust / (nlsP->theta_i * gnorm * tao->trust + (1.0 - nlsP->… in TaoSolve_NLS() 166 …tau_2 = nlsP->theta_i * gnorm * tao->trust / (nlsP->theta_i * gnorm * tao->trust - (1.0 + nlsP->… in TaoSolve_NLS() 172 max_radius = PetscMax(max_radius, tao->trust); in TaoSolve_NLS() 187 max_radius = PetscMax(max_radius, tao->trust); in TaoSolve_NLS() [all …]
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| /petsc/src/tao/bound/impls/bnk/ |
| H A D | bnk.c | 126 tao->trust = tao->trust0; in TaoBNKInitialize() 132 tao->trust = tao->trust0; in TaoBNKInitialize() 148 PetscCall(VecAXPY(tao->solution, -tao->trust / bnk->gnorm, tao->gradient)); in TaoBNKInitialize() 162 sigma = -tao->trust / bnk->gnorm; in TaoBNKInitialize() 179 … prered = tao->trust * (bnk->gnorm - 0.5 * tao->trust * prered / (bnk->gnorm * bnk->gnorm)); in TaoBNKInitialize() 187 …tau_1 = bnk->theta_i * bnk->gnorm * tao->trust / (bnk->theta_i * bnk->gnorm * tao->trust + (1.0 … in TaoBNKInitialize() 188 …tau_2 = bnk->theta_i * bnk->gnorm * tao->trust / (bnk->theta_i * bnk->gnorm * tao->trust - (1.0 … in TaoBNKInitialize() 194 max_radius = PetscMax(max_radius, tao->trust); in TaoBNKInitialize() 205 max_radius = PetscMax(max_radius, tao->trust); in TaoBNKInitialize() 231 tao->trust = tau * tao->trust; in TaoBNKInitialize() [all …]
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| H A D | bntl.c | 146 oldTrust = tao->trust; in TaoSolve_BNTL() 200 tao->trust = 0.0; in TaoSolve_BNTL() 211 tao->trust = oldTrust; in TaoSolve_BNTL()
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| H A D | bntr.c | 159 oldTrust = tao->trust; in TaoSolve_BNTR() 180 if (oldTrust == tao->trust) { in TaoSolve_BNTR()
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| /petsc/src/tao/bound/impls/tron/ |
| H A D | tron.c | 77 tao->trust = tao->trust0; in TaoSolve_TRON() 95 tao->trust = tao->trust0; in TaoSolve_TRON() 96 if (tao->trust <= 0) tao->trust = PetscMax(tron->gnorm * tron->gnorm, 1.0); in TaoSolve_TRON() 100 tron->stepsize = tao->trust; in TaoSolve_TRON() 121 delta = tao->trust; in TaoSolve_TRON() 224 tao->trust = delta; in TaoSolve_TRON()
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| /petsc/src/tao/leastsquares/tutorials/output/ |
| H A D | cs1_view_lm.out | 5 Downhill trust region decrease factor:: 0.2 6 Uphill trust region increase factor:: 1.5
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| /petsc/doc/manual/ |
| H A D | tao.md | 89 gradient, Newton with line search or trust region) but also can 559 Other stopping criteria include a minimum trust-region radius or a 863 Newton line search (`tao_nls`), Newton trust-region (`tao_ntr`), 864 and Newton trust-region line-search (`tao_ntl`) 869 trust-region methods will likely perform best. When a Hessian evaluation 881 for unconstrained optimization: line search (NLS), trust region (NTR), and trust 1190 of equation, a trust-region radius needs to be initialized and updated. 1191 This trust-region radius simultaneously limits the size of the step 1193 method. The method for initializing the trust-region radius is set with 1203 algorithm. The `constant` method initializes the trust-region radius [all …]
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| H A D | snes.md | 24 core of the package, including both line search and trust region 260 and trust region methods. Also provided are several nonlinear Krylov 469 The trust region method in `SNES` for solving systems of nonlinear 472 set to control the variation of the trust region size during the 473 solution process. In particular, the user can control the initial trust
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| /petsc/doc/overview/ |
| H A D | linear_solve_table.md | 523 * - Nash Conjugate Gradient with trust region constraint 528 * - Conjugate Gradient with trust region constraint 533 * - Gould et al Conjugate Gradient with trust region constraint 538 * - Steinhaug Conjugate Gradient with trust region constraint
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| H A D | nonlinear_solve_table.md | 20 * - Newton's method with trust region
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| /petsc/src/tao/leastsquares/tutorials/matlab/ |
| H A D | ProblemInitialize.m | 22 delta = 0.1; % Initial trust region radius
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| /petsc/doc/changes/ |
| H A D | 2015.md | 84 -snes_eq_tr\_\<parameter_name> - nonlinear equations, trust region 87 minimization, trust region method Run program with -help for a
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| H A D | 322.md | 126 …lerances()` and `SNESNewtonTRSetUpdateParameters()` to programmatically set trust region parameters
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| H A D | 312.md | 179 positive since tiny trust region would indicate trouble, not
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| H A D | 321.md | 134 - Add support for trust region norm customization in `SNESNEWTONTR` via `SNESNewtonTRSetNormType`
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| /petsc/include/petsc/private/ |
| H A D | taoimpl.h | 140 PetscReal trust; /* Current trust region */ member
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| /petsc/src/binding/petsc4py/docs/source/ |
| H A D | overview.rst | 81 for nonlinear systems. Includes both line search and trust
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| /petsc/src/binding/petsc4py/src/petsc4py/PETSc/ |
| H A D | SNES.pyx | 327 """Set the tolerance parameters used for the trust region. 334 The minimum allowed trust region size. Defaults to `CURRENT`. 336 The maximum allowed trust region size. Defaults to `CURRENT`. 338 The initial trust region size. Defaults to `CURRENT`. 354 """Return the tolerance parameters used for the trust region. 361 The minimum allowed trust region size. 363 The maximum allowed trust region size. 365 The initial trust region size. 380 """Set the update parameters used for the trust region. 413 """Return the update parameters used for the trust region.
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| H A D | TAO.pyx | 275 """Set the initial trust region radius.
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| /petsc/src/tao/interface/ |
| H A D | taosolver.c | 663 if (tao->trust < tao->steptol) { in TaoView() 665 … PetscCall(PetscViewerASCIIPrintf(viewer, "Final trust region radius:=%g\n", (double)tao->trust)); in TaoView() 1237 *radius = tao->trust; in TaoGetCurrentTrustRegionRadius() 1689 …PetscViewerASCIIPrintf(viewer, " Step: %g, Trust: %g\n", (double)tao->step, (double)tao->trust)); in TaoMonitorGlobalization()
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| /petsc/doc/community/meetings/2023/ |
| H A D | index.md | 508 PETSc the general-purpose nonlinear solver, Newton trust-region dogleg 509 Cauchy (NTRDC) and Newton trust-region (NTR) to demonstrate the
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| /petsc/doc/faq/ |
| H A D | index.md | 1522 - Try a trust region method (`-ts_type tr`, may have to adjust parameters).
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| /petsc/doc/ |
| H A D | petsc.bib | 939 title = {Solving the trust-region subproblem using the Lanczos method}, 29666 @InCollection{ gabriel.pang:trust, 31261 @Article{ jiang.fukushima.ea:trust,
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