Actual source code: petsctao.h
1: #pragma once
3: #include <petscsnes.h>
4: #include <petsctaoterm.h>
6: /* SUBMANSEC = Tao */
8: PETSC_EXTERN PetscErrorCode MatDSFischer(Mat, Vec, Vec, Vec, Vec, PetscReal, Vec, Vec, Vec, Vec, Vec);
9: PETSC_EXTERN PetscErrorCode TaoSoftThreshold(Vec, PetscReal, PetscReal, Vec);
11: /*E
12: TaoSubsetType - Type representing the way the `Tao` solvers handle active sets
14: Values:
15: + `TAO_SUBSET_SUBVEC` - Tao uses `MatCreateSubMatrix()` and `VecGetSubVector()`
16: . `TAO_SUBSET_MASK` - Matrices are zeroed out corresponding to active set entries
17: - `TAO_SUBSET_MATRIXFREE` - Same as `TAO_SUBSET_MASK` but it can be applied to matrix-free operators
19: Options database Key:
20: . -different_hessian - `Tao` will use a copy of the Hessian operator for masking. By default `Tao` will directly alter the Hessian operator.
22: Level: intermediate
24: .seealso: [](ch_tao), `TaoVecGetSubVec()`, `TaoMatGetSubMat()`, `Tao`, `TaoCreate()`, `TaoDestroy()`, `TaoSetType()`, `TaoType`
25: E*/
26: typedef enum {
27: TAO_SUBSET_SUBVEC,
28: TAO_SUBSET_MASK,
29: TAO_SUBSET_MATRIXFREE
30: } TaoSubsetType;
31: PETSC_EXTERN const char *const TaoSubsetTypes[];
33: /*E
34: TaoADMMUpdateType - Determine the spectral penalty update routine for the Lagrange augmented term for `TAOADMM`.
36: Level: advanced
38: .seealso: [](ch_tao), `Tao`, `TAOADMM`, `TaoADMMSetUpdateType()`
39: E*/
40: typedef enum {
41: TAO_ADMM_UPDATE_BASIC,
42: TAO_ADMM_UPDATE_ADAPTIVE,
43: TAO_ADMM_UPDATE_ADAPTIVE_RELAXED
44: } TaoADMMUpdateType;
45: PETSC_EXTERN const char *const TaoADMMUpdateTypes[];
47: /*MC
48: TAO_ADMM_UPDATE_BASIC - Use same spectral penalty set at the beginning. This never performs an update to the penalty
50: Level: advanced
52: Note:
53: Most basic implementation of `TAOADMM`. Generally slower than adaptive or adaptive relaxed version.
55: .seealso: [](ch_tao), `Tao`, `TAOADMM`, `TaoADMMSetUpdateType()`, `TAO_ADMM_UPDATE_ADAPTIVE`, `TAO_ADMM_UPDATE_ADAPTIVE_RELAXED`
56: M*/
58: /*MC
59: TAO_ADMM_UPDATE_ADAPTIVE - Adaptively update the spectral penalty
61: Level: advanced
63: Note:
64: Adaptively updates spectral penalty of `TAOADMM` by using both steepest descent and minimum gradient.
66: .seealso: [](ch_tao), `Tao`, `TAOADMM`, `TaoADMMSetUpdateType()`, `TAO_ADMM_UPDATE_BASIC`, `TAO_ADMM_UPDATE_ADAPTIVE_RELAXED`
67: M*/
69: /*MC
70: ADMM_UPDATE_ADAPTIVE_RELAXED - Adaptively update spectral penalty, and relaxes parameter update
72: Level: advanced
74: Note:
75: With adaptive spectral penalty update, it also relaxes the `x` vector update by a factor.
77: .seealso: [](ch_tao), `Tao`, `TaoADMMSetUpdateType()`, `TAO_ADMM_UPDATE_BASIC`, `TAO_ADMM_UPDATE_ADAPTIVE`
78: M*/
80: /*E
81: TaoADMMRegularizerType - Determine regularizer routine - either user provided or soft threshold for `TAOADMM`
83: Level: advanced
85: .seealso: [](ch_tao), `Tao`, `TAOADMM`, `TaoADMMSetRegularizerType()`
86: E*/
87: typedef enum {
88: TAO_ADMM_REGULARIZER_USER,
89: TAO_ADMM_REGULARIZER_SOFT_THRESH
90: } TaoADMMRegularizerType;
91: PETSC_EXTERN const char *const TaoADMMRegularizerTypes[];
93: /*MC
94: TAO_ADMM_REGULARIZER_USER - User provided routines for regularizer part of `TAOADMM`
96: Level: advanced
98: Note:
99: User needs to provided appropriate routines and type for regularizer solver
101: .seealso: [](ch_tao), `Tao`, `TAOADMM`, `TaoADMMSetRegularizerType()`, `TAO_ADMM_REGULARIZER_SOFT_THRESH`
102: M*/
104: /*MC
105: TAO_ADMM_REGULARIZER_SOFT_THRESH - Soft threshold to solve regularizer part of `TAOADMM`
107: Level: advanced
109: Note:
110: Utilizes built-in SoftThreshold routines
112: .seealso: [](ch_tao), `Tao`, `TAOADMM`, `TaoSoftThreshold()`, `TaoADMMSetRegularizerObjectiveAndGradientRoutine()`,
113: `TaoADMMSetRegularizerHessianRoutine()`, `TaoADMMSetRegularizerType()`, `TAO_ADMM_REGULARIZER_USER`
114: M*/
116: /*E
117: TaoALMMType - Determine the augmented Lagrangian formulation used in the `TAOALMM` subproblem.
119: Values:
120: + `TAO_ALMM_CLASSIC` - classic augmented Lagrangian definition including slack variables for inequality constraints
121: - `TAO_ALMM_PHR` - Powell-Hestenes-Rockafellar formulation without slack variables, uses pointwise `min()` for inequalities
123: Level: advanced
125: .seealso: [](ch_tao), `Tao`, `TAOALMM`, `TaoALMMSetType()`, `TaoALMMGetType()`
126: E*/
127: typedef enum {
128: TAO_ALMM_CLASSIC,
129: TAO_ALMM_PHR
130: } TaoALMMType;
131: PETSC_EXTERN const char *const TaoALMMTypes[];
133: /*E
134: TaoBNCGType - Determine the conjugate gradient update formula used in the `TAOBNCG` algorithm.
136: Values:
137: + `TAO_BNCG_GD` - basic gradient descent, no CG update
138: . `TAO_BNCG_PCGD` - preconditioned/scaled gradient descent
139: . `TAO_BNCG_HS` - Hestenes-Stiefel
140: . `TAO_BNCG_FR` - Fletcher-Reeves
141: . `TAO_BNCG_PRP` - Polak-Ribiere-Polyak (PRP)
142: . `TAO_BNCG_PRP_PLUS` - Polak-Ribiere-Polyak "plus" (PRP+)
143: . `TAO_BNCG_DY` - Dai-Yuan
144: . `TAO_BNCG_HZ` - Hager-Zhang (CG_DESCENT 5.3)
145: . `TAO_BNCG_DK` - Dai-Kou (2013)
146: . `TAO_BNCG_KD` - Kou-Dai (2015)
147: . `TAO_BNCG_SSML_BFGS` - Self-Scaling Memoryless BFGS (Perry-Shanno)
148: . `TAO_BNCG_SSML_DFP` - Self-Scaling Memoryless DFP
149: - `TAO_BNCG_SSML_BRDN` - Self-Scaling Memoryless (Symmetric) Broyden
151: Level: advanced
153: .seealso: `Tao`, `TAOBNCG`, `TaoBNCGSetType()`, `TaoBNCGGetType()`
154: E*/
156: typedef enum {
157: TAO_BNCG_GD,
158: TAO_BNCG_PCGD,
159: TAO_BNCG_HS,
160: TAO_BNCG_FR,
161: TAO_BNCG_PRP,
162: TAO_BNCG_PRP_PLUS,
163: TAO_BNCG_DY,
164: TAO_BNCG_HZ,
165: TAO_BNCG_DK,
166: TAO_BNCG_KD,
167: TAO_BNCG_SSML_BFGS,
168: TAO_BNCG_SSML_DFP,
169: TAO_BNCG_SSML_BRDN
170: } TaoBNCGType;
171: PETSC_EXTERN const char *const TaoBNCGTypes[];
173: /*J
174: TaoType - String with the name of a `Tao` method
176: Values:
177: + `TAONLS` - nls Newton's method with line search for unconstrained minimization
178: . `TAONTR` - ntr Newton's method with trust region for unconstrained minimization
179: . `TAONTL` - ntl Newton's method with trust region, line search for unconstrained minimization
180: . `TAOLMVM` - lmvm Limited memory variable metric method for unconstrained minimization
181: . `TAOCG` - cg Nonlinear conjugate gradient method for unconstrained minimization
182: . `TAONM` - nm Nelder-Mead algorithm for derivate-free unconstrained minimization
183: . `TAOTRON` - tron Newton Trust Region method for bound constrained minimization
184: . `TAOGPCG` - gpcg Newton Trust Region method for quadratic bound constrained minimization
185: . `TAOBLMVM` - blmvm Limited memory variable metric method for bound constrained minimization
186: . `TAOLCL` - lcl Linearly constrained Lagrangian method for pde-constrained minimization
187: - `TAOPOUNDERS` - Pounders Model-based algorithm for nonlinear least squares
189: Level: beginner
191: .seealso: [](doc_taosolve), [](ch_tao), `Tao`, `TaoCreate()`, `TaoSetType()`
192: J*/
193: typedef const char *TaoType;
194: #define TAOLMVM "lmvm"
195: #define TAONLS "nls"
196: #define TAONTR "ntr"
197: #define TAONTL "ntl"
198: #define TAOCG "cg"
199: #define TAOTRON "tron"
200: #define TAOOWLQN "owlqn"
201: #define TAOBMRM "bmrm"
202: #define TAOBLMVM "blmvm"
203: #define TAOBQNLS "bqnls"
204: #define TAOBNCG "bncg"
205: #define TAOBNLS "bnls"
206: #define TAOBNTR "bntr"
207: #define TAOBNTL "bntl"
208: #define TAOBNK "bnk"
209: #define TAOBQNKLS "bqnkls"
210: #define TAOBQNKTR "bqnktr"
211: #define TAOBQNKTL "bqnktl"
212: #define TAOBQPIP "bqpip"
213: #define TAOGPCG "gpcg"
214: #define TAONM "nm"
215: #define TAOPOUNDERS "pounders"
216: #define TAOBRGN "brgn"
217: #define TAOLCL "lcl"
218: #define TAOSSILS "ssils"
219: #define TAOSSFLS "ssfls"
220: #define TAOASILS "asils"
221: #define TAOASFLS "asfls"
222: #define TAOIPM "ipm"
223: #define TAOPDIPM "pdipm"
224: #define TAOSHELL "shell"
225: #define TAOADMM "admm"
226: #define TAOALMM "almm"
227: #define TAOPYTHON "python"
228: #define TAOSNES "snes"
230: PETSC_EXTERN PetscClassId TAO_CLASSID;
231: PETSC_EXTERN PetscFunctionList TaoList;
233: /*E
234: TaoConvergedReason - reason a `Tao` optimizer was said to have converged or diverged
236: Values:
237: + `TAO_CONVERGED_GATOL` - $||g(X)|| < gatol$
238: . `TAO_CONVERGED_GRTOL` - $||g(X)|| / f(X) < grtol$
239: . `TAO_CONVERGED_GTTOL` - $||g(X)|| / ||g(X0)|| < gttol$
240: . `TAO_CONVERGED_STEPTOL` - step size smaller than tolerance
241: . `TAO_CONVERGED_MINF` - $F < F_min$
242: . `TAO_CONVERGED_USER` - the user indicates the optimization has succeeded
243: . `TAO_DIVERGED_MAXITS` - the maximum number of iterations allowed has been achieved
244: . `TAO_DIVERGED_NAN` - not a number appeared in the computations
245: . `TAO_DIVERGED_MAXFCN` - the maximum number of function evaluations has been computed
246: . `TAO_DIVERGED_LS_FAILURE` - a linesearch failed
247: . `TAO_DIVERGED_TR_REDUCTION` - trust region failure
248: . `TAO_DIVERGED_USER` - the user has indicated the optimization has failed
249: - `TAO_CONTINUE_ITERATING` - the optimization is still running, `TaoSolve()`
251: where
252: + X - current solution
253: . X0 - initial guess
254: . f(X) - current function value
255: . f(X*) - true solution (estimated)
256: . g(X) - current gradient
257: . its - current iterate number
258: . maxits - maximum number of iterates
259: . fevals - number of function evaluations
260: - max_funcsals - maximum number of function evaluations
262: Level: beginner
264: Note:
265: The two most common reasons for divergence are an incorrectly coded or computed gradient or Hessian failure or lack of convergence
266: in the linear system solve (in this case we recommend testing with `-pc_type lu` to eliminate the linear solver as the cause of the problem).
268: Developer Note:
269: The names in `KSPConvergedReason`, `SNESConvergedReason`, and `TaoConvergedReason` should be uniformized
271: .seealso: [](ch_tao), `Tao`, `TaoSolve()`, `TaoGetConvergedReason()`, `KSPConvergedReason`, `SNESConvergedReason`
272: E*/
273: typedef enum { /* converged */
274: TAO_CONVERGED_GATOL = 3, /* ||g(X)|| < gatol */
275: TAO_CONVERGED_GRTOL = 4, /* ||g(X)|| / f(X) < grtol */
276: TAO_CONVERGED_GTTOL = 5, /* ||g(X)|| / ||g(X0)|| < gttol */
277: TAO_CONVERGED_STEPTOL = 6, /* step size small */
278: TAO_CONVERGED_MINF = 7, /* F < F_min */
279: TAO_CONVERGED_USER = 8, /* User defined */
280: /* diverged */
281: TAO_DIVERGED_MAXITS = -2,
282: TAO_DIVERGED_NAN = -4,
283: TAO_DIVERGED_MAXFCN = -5,
284: TAO_DIVERGED_LS_FAILURE = -6,
285: TAO_DIVERGED_TR_REDUCTION = -7,
286: TAO_DIVERGED_USER = -8, /* User defined */
287: /* keep going */
288: TAO_CONTINUE_ITERATING = 0
289: } TaoConvergedReason;
291: PETSC_EXTERN const char **TaoConvergedReasons;
293: PETSC_EXTERN PetscErrorCode TaoInitializePackage(void);
294: PETSC_EXTERN PetscErrorCode TaoFinalizePackage(void);
295: PETSC_EXTERN PetscErrorCode TaoCreate(MPI_Comm, Tao *);
296: PETSC_EXTERN PetscErrorCode TaoSetFromOptions(Tao);
297: PETSC_EXTERN PetscErrorCode TaoSetUp(Tao);
298: PETSC_EXTERN PetscErrorCode TaoSetType(Tao, TaoType);
299: PETSC_EXTERN PetscErrorCode TaoGetType(Tao, TaoType *);
300: PETSC_EXTERN PetscErrorCode TaoSetApplicationContext(Tao, PetscCtx);
301: PETSC_EXTERN PetscErrorCode TaoGetApplicationContext(Tao, PetscCtxRt);
302: PETSC_EXTERN PetscErrorCode TaoDestroy(Tao *);
303: PETSC_EXTERN PetscErrorCode TaoParametersInitialize(Tao);
305: PETSC_EXTERN PetscErrorCode TaoSetOptionsPrefix(Tao, const char[]);
306: PETSC_EXTERN PetscErrorCode TaoView(Tao, PetscViewer);
307: PETSC_EXTERN PetscErrorCode TaoViewFromOptions(Tao, PetscObject, const char[]);
309: PETSC_EXTERN PetscErrorCode TaoSolve(Tao);
311: PETSC_EXTERN PetscErrorCode TaoRegister(const char[], PetscErrorCode (*)(Tao));
312: PETSC_EXTERN PetscErrorCode TaoRegisterDestroy(void);
314: PETSC_EXTERN PetscErrorCode TaoGetConvergedReason(Tao, TaoConvergedReason *);
315: PETSC_EXTERN PetscErrorCode TaoGetSolutionStatus(Tao, PetscInt *, PetscReal *, PetscReal *, PetscReal *, PetscReal *, TaoConvergedReason *);
316: PETSC_EXTERN PetscErrorCode TaoSetConvergedReason(Tao, TaoConvergedReason);
317: PETSC_EXTERN PetscErrorCode TaoSetSolution(Tao, Vec);
318: PETSC_EXTERN PetscErrorCode TaoGetSolution(Tao, Vec *);
320: PETSC_EXTERN PetscErrorCode TaoSetObjective(Tao, PetscErrorCode (*)(Tao, Vec, PetscReal *, PetscCtx), PetscCtx);
321: PETSC_EXTERN PetscErrorCode TaoGetObjective(Tao, PetscErrorCode (**)(Tao, Vec, PetscReal *, PetscCtx), PetscCtxRt);
322: PETSC_EXTERN PetscErrorCode TaoSetGradient(Tao, Vec, PetscErrorCode (*)(Tao, Vec, Vec, PetscCtx), PetscCtx);
323: PETSC_EXTERN PetscErrorCode TaoGetGradient(Tao, Vec *, PetscErrorCode (**)(Tao, Vec, Vec, PetscCtx), PetscCtxRt);
324: PETSC_EXTERN PetscErrorCode TaoSetObjectiveAndGradient(Tao, Vec, PetscErrorCode (*)(Tao, Vec, PetscReal *, Vec, PetscCtx), PetscCtx);
325: PETSC_EXTERN PetscErrorCode TaoGetObjectiveAndGradient(Tao, Vec *, PetscErrorCode (**)(Tao, Vec, PetscReal *, Vec, PetscCtx), PetscCtxRt);
326: PETSC_EXTERN PetscErrorCode TaoSetHessian(Tao, Mat, Mat, PetscErrorCode (*)(Tao, Vec, Mat, Mat, PetscCtx), PetscCtx);
327: PETSC_EXTERN PetscErrorCode TaoGetHessian(Tao, Mat *, Mat *, PetscErrorCode (**)(Tao, Vec, Mat, Mat, PetscCtx), PetscCtxRt);
328: PETSC_EXTERN PetscErrorCode TaoGetHessianMatrices(Tao, Mat *, Mat *);
330: PETSC_EXTERN PetscErrorCode TaoSetGradientNorm(Tao, Mat);
331: PETSC_EXTERN PetscErrorCode TaoGetGradientNorm(Tao, Mat *);
332: PETSC_EXTERN PetscErrorCode TaoSetLMVMMatrix(Tao, Mat);
333: PETSC_EXTERN PetscErrorCode TaoGetLMVMMatrix(Tao, Mat *);
334: PETSC_EXTERN PetscErrorCode TaoSetRecycleHistory(Tao, PetscBool);
335: PETSC_EXTERN PetscErrorCode TaoGetRecycleHistory(Tao, PetscBool *);
336: PETSC_EXTERN PetscErrorCode TaoLMVMSetH0(Tao, Mat);
337: PETSC_EXTERN PetscErrorCode TaoLMVMGetH0(Tao, Mat *);
338: PETSC_EXTERN PetscErrorCode TaoLMVMGetH0KSP(Tao, KSP *);
339: PETSC_EXTERN PetscErrorCode TaoLMVMRecycle(Tao, PetscBool);
340: PETSC_EXTERN PetscErrorCode TaoSetResidualRoutine(Tao, Vec, PetscErrorCode (*)(Tao, Vec, Vec, PetscCtx), PetscCtx);
341: PETSC_EXTERN PetscErrorCode TaoSetResidualWeights(Tao, Vec, PetscInt, PetscInt *, PetscInt *, PetscReal *);
342: PETSC_EXTERN PetscErrorCode TaoSetConstraintsRoutine(Tao, Vec, PetscErrorCode (*)(Tao, Vec, Vec, PetscCtx), PetscCtx);
343: PETSC_EXTERN PetscErrorCode TaoSetInequalityConstraintsRoutine(Tao, Vec, PetscErrorCode (*)(Tao, Vec, Vec, PetscCtx), PetscCtx);
344: PETSC_EXTERN PetscErrorCode TaoGetInequalityConstraintsRoutine(Tao, Vec *, PetscErrorCode (**)(Tao, Vec, Vec, PetscCtx), PetscCtxRt);
345: PETSC_EXTERN PetscErrorCode TaoSetEqualityConstraintsRoutine(Tao, Vec, PetscErrorCode (*)(Tao, Vec, Vec, PetscCtx), PetscCtx);
346: PETSC_EXTERN PetscErrorCode TaoGetEqualityConstraintsRoutine(Tao, Vec *, PetscErrorCode (**)(Tao, Vec, Vec, PetscCtx), PetscCtxRt);
347: PETSC_EXTERN PetscErrorCode TaoSetJacobianResidualRoutine(Tao, Mat, Mat, PetscErrorCode (*)(Tao, Vec, Mat, Mat, PetscCtx), PetscCtx);
348: PETSC_EXTERN PetscErrorCode TaoSetJacobianRoutine(Tao, Mat, Mat, PetscErrorCode (*)(Tao, Vec, Mat, Mat, PetscCtx), PetscCtx);
349: PETSC_EXTERN PetscErrorCode TaoSetJacobianStateRoutine(Tao, Mat, Mat, Mat, PetscErrorCode (*)(Tao, Vec, Mat, Mat, Mat, PetscCtx), PetscCtx);
350: PETSC_EXTERN PetscErrorCode TaoSetJacobianDesignRoutine(Tao, Mat, PetscErrorCode (*)(Tao, Vec, Mat, PetscCtx), PetscCtx);
351: PETSC_EXTERN PetscErrorCode TaoSetJacobianInequalityRoutine(Tao, Mat, Mat, PetscErrorCode (*)(Tao, Vec, Mat, Mat, PetscCtx), PetscCtx);
352: PETSC_EXTERN PetscErrorCode TaoGetJacobianInequalityRoutine(Tao, Mat *, Mat *, PetscErrorCode (**)(Tao, Vec, Mat, Mat, PetscCtx), PetscCtxRt);
353: PETSC_EXTERN PetscErrorCode TaoSetJacobianEqualityRoutine(Tao, Mat, Mat, PetscErrorCode (*)(Tao, Vec, Mat, Mat, PetscCtx), PetscCtx);
354: PETSC_EXTERN PetscErrorCode TaoGetJacobianEqualityRoutine(Tao, Mat *, Mat *, PetscErrorCode (**)(Tao, Vec, Mat, Mat, PetscCtx), PetscCtxRt);
356: PETSC_EXTERN PetscErrorCode TaoPythonSetType(Tao, const char[]);
357: PETSC_EXTERN PetscErrorCode TaoPythonGetType(Tao, const char *[]);
359: PETSC_EXTERN PetscErrorCode TaoShellSetSolve(Tao, PetscErrorCode (*)(Tao));
360: PETSC_EXTERN PetscErrorCode TaoShellSetContext(Tao, PetscCtx);
361: PETSC_EXTERN PetscErrorCode TaoShellGetContext(Tao, PetscCtxRt);
363: PETSC_EXTERN PetscErrorCode TaoSetStateDesignIS(Tao, IS, IS);
365: PETSC_EXTERN PetscErrorCode TaoComputeObjective(Tao, Vec, PetscReal *);
366: PETSC_EXTERN PetscErrorCode TaoComputeResidual(Tao, Vec, Vec);
367: PETSC_EXTERN PetscErrorCode TaoTestGradient(Tao, Vec, Vec);
368: PETSC_EXTERN PetscErrorCode TaoComputeGradient(Tao, Vec, Vec);
369: PETSC_EXTERN PetscErrorCode TaoComputeObjectiveAndGradient(Tao, Vec, PetscReal *, Vec);
370: PETSC_EXTERN PetscErrorCode TaoComputeConstraints(Tao, Vec, Vec);
371: PETSC_EXTERN PetscErrorCode TaoComputeInequalityConstraints(Tao, Vec, Vec);
372: PETSC_EXTERN PetscErrorCode TaoComputeEqualityConstraints(Tao, Vec, Vec);
373: PETSC_EXTERN PetscErrorCode TaoDefaultComputeGradient(Tao, Vec, Vec, PetscCtx);
374: PETSC_EXTERN PetscErrorCode TaoIsObjectiveDefined(Tao, PetscBool *);
375: PETSC_EXTERN PetscErrorCode TaoIsGradientDefined(Tao, PetscBool *);
376: PETSC_EXTERN PetscErrorCode TaoIsObjectiveAndGradientDefined(Tao, PetscBool *);
378: PETSC_EXTERN PetscErrorCode TaoTestHessian(Tao);
379: PETSC_EXTERN PetscErrorCode TaoComputeHessian(Tao, Vec, Mat, Mat);
380: PETSC_EXTERN PetscErrorCode TaoComputeResidualJacobian(Tao, Vec, Mat, Mat);
381: PETSC_EXTERN PetscErrorCode TaoComputeJacobian(Tao, Vec, Mat, Mat);
382: PETSC_EXTERN PetscErrorCode TaoComputeJacobianState(Tao, Vec, Mat, Mat, Mat);
383: PETSC_EXTERN PetscErrorCode TaoComputeJacobianEquality(Tao, Vec, Mat, Mat);
384: PETSC_EXTERN PetscErrorCode TaoComputeJacobianInequality(Tao, Vec, Mat, Mat);
385: PETSC_EXTERN PetscErrorCode TaoComputeJacobianDesign(Tao, Vec, Mat);
387: PETSC_EXTERN PetscErrorCode TaoDefaultComputeHessian(Tao, Vec, Mat, Mat, PetscCtx);
388: PETSC_EXTERN PetscErrorCode TaoDefaultComputeHessianColor(Tao, Vec, Mat, Mat, PetscCtx);
389: PETSC_EXTERN PetscErrorCode TaoDefaultComputeHessianMFFD(Tao, Vec, Mat, Mat, PetscCtx);
390: PETSC_EXTERN PetscErrorCode TaoComputeDualVariables(Tao, Vec, Vec);
391: PETSC_EXTERN PetscErrorCode TaoSetVariableBounds(Tao, Vec, Vec);
392: PETSC_EXTERN PetscErrorCode TaoGetVariableBounds(Tao, Vec *, Vec *);
393: PETSC_EXTERN PetscErrorCode TaoGetDualVariables(Tao, Vec *, Vec *);
394: PETSC_EXTERN PetscErrorCode TaoSetInequalityBounds(Tao, Vec, Vec);
395: PETSC_EXTERN PetscErrorCode TaoGetInequalityBounds(Tao, Vec *, Vec *);
396: PETSC_EXTERN PetscErrorCode TaoSetVariableBoundsRoutine(Tao, PetscErrorCode (*)(Tao, Vec, Vec, PetscCtx), PetscCtx);
397: PETSC_EXTERN PetscErrorCode TaoComputeVariableBounds(Tao);
399: PETSC_EXTERN PetscErrorCode TaoGetTolerances(Tao, PetscReal *, PetscReal *, PetscReal *);
400: PETSC_EXTERN PetscErrorCode TaoSetTolerances(Tao, PetscReal, PetscReal, PetscReal);
401: PETSC_EXTERN PetscErrorCode TaoGetConstraintTolerances(Tao, PetscReal *, PetscReal *);
402: PETSC_EXTERN PetscErrorCode TaoSetConstraintTolerances(Tao, PetscReal, PetscReal);
403: PETSC_EXTERN PetscErrorCode TaoSetFunctionLowerBound(Tao, PetscReal);
404: PETSC_EXTERN PetscErrorCode TaoSetInitialTrustRegionRadius(Tao, PetscReal);
405: PETSC_EXTERN PetscErrorCode TaoSetMaximumIterations(Tao, PetscInt);
406: PETSC_EXTERN PetscErrorCode TaoSetMaximumFunctionEvaluations(Tao, PetscInt);
407: PETSC_EXTERN PetscErrorCode TaoGetFunctionLowerBound(Tao, PetscReal *);
408: PETSC_EXTERN PetscErrorCode TaoGetInitialTrustRegionRadius(Tao, PetscReal *);
409: PETSC_EXTERN PetscErrorCode TaoGetCurrentTrustRegionRadius(Tao, PetscReal *);
410: PETSC_EXTERN PetscErrorCode TaoGetMaximumIterations(Tao, PetscInt *);
411: PETSC_EXTERN PetscErrorCode TaoGetCurrentFunctionEvaluations(Tao, PetscInt *);
412: PETSC_EXTERN PetscErrorCode TaoGetMaximumFunctionEvaluations(Tao, PetscInt *);
413: PETSC_EXTERN PetscErrorCode TaoGetIterationNumber(Tao, PetscInt *);
414: PETSC_EXTERN PetscErrorCode TaoSetIterationNumber(Tao, PetscInt);
415: PETSC_EXTERN PetscErrorCode TaoGetTotalIterationNumber(Tao, PetscInt *);
416: PETSC_EXTERN PetscErrorCode TaoSetTotalIterationNumber(Tao, PetscInt);
417: PETSC_EXTERN PetscErrorCode TaoGetResidualNorm(Tao, PetscReal *);
419: PETSC_EXTERN PetscErrorCode TaoAppendOptionsPrefix(Tao, const char[]);
420: PETSC_EXTERN PetscErrorCode TaoGetOptionsPrefix(Tao, const char *[]);
421: PETSC_EXTERN PetscErrorCode TaoResetStatistics(Tao);
422: PETSC_EXTERN PetscErrorCode TaoSetUpdate(Tao, PetscErrorCode (*)(Tao, PetscInt, PetscCtx), PetscCtx);
424: PETSC_EXTERN PetscErrorCode TaoGetKSP(Tao, KSP *);
425: PETSC_EXTERN PetscErrorCode TaoGetLinearSolveIterations(Tao, PetscInt *);
426: PETSC_EXTERN PetscErrorCode TaoKSPSetUseEW(Tao, PetscBool);
428: #include <petsctaolinesearch.h>
430: PETSC_EXTERN PetscErrorCode TaoGetLineSearch(Tao, TaoLineSearch *);
432: PETSC_EXTERN PetscErrorCode TaoSetConvergenceHistory(Tao, PetscReal *, PetscReal *, PetscReal *, PetscInt *, PetscInt, PetscBool);
433: PETSC_EXTERN PetscErrorCode TaoGetConvergenceHistory(Tao, PetscReal **, PetscReal **, PetscReal **, PetscInt **, PetscInt *);
434: PETSC_EXTERN PetscErrorCode TaoMonitorSet(Tao, PetscErrorCode (*)(Tao, PetscCtx), PetscCtx, PetscCtxDestroyFn *);
435: PETSC_EXTERN PetscErrorCode TaoMonitorSetFromOptions(Tao, const char[], const char[], const char[], PetscErrorCode (*)(Tao, PetscViewerAndFormat *));
436: PETSC_EXTERN PetscErrorCode TaoMonitorCancel(Tao);
437: PETSC_EXTERN PetscErrorCode TaoMonitorDefault(Tao, PetscViewerAndFormat *);
438: PETSC_EXTERN PetscErrorCode TaoMonitorGlobalization(Tao, PetscViewerAndFormat *);
439: PETSC_EXTERN PetscErrorCode TaoMonitorDefaultShort(Tao, PetscViewerAndFormat *);
440: PETSC_EXTERN PetscErrorCode TaoMonitorConstraintNorm(Tao, PetscViewerAndFormat *);
441: PETSC_EXTERN PetscErrorCode TaoMonitorSolution(Tao, PetscViewerAndFormat *);
442: PETSC_EXTERN PetscErrorCode TaoMonitorResidual(Tao, PetscViewerAndFormat *);
443: PETSC_EXTERN PetscErrorCode TaoMonitorGradient(Tao, PetscViewerAndFormat *);
444: PETSC_EXTERN PetscErrorCode TaoMonitorStep(Tao, PetscViewerAndFormat *);
445: PETSC_EXTERN PetscErrorCode TaoMonitorSolutionDraw(Tao, PetscCtx);
446: PETSC_EXTERN PetscErrorCode TaoMonitorStepDraw(Tao, PetscCtx);
447: PETSC_EXTERN PetscErrorCode TaoMonitorGradientDraw(Tao, PetscCtx);
448: PETSC_EXTERN PetscErrorCode TaoAddLineSearchCounts(Tao);
450: PETSC_EXTERN PetscErrorCode TaoDefaultConvergenceTest(Tao, PetscCtx);
451: PETSC_EXTERN PetscErrorCode TaoSetConvergenceTest(Tao, PetscErrorCode (*)(Tao, PetscCtx), PetscCtx);
453: PETSC_EXTERN PetscErrorCode TaoLCLSetStateDesignIS(Tao, IS, IS);
454: PETSC_EXTERN PetscErrorCode TaoMonitor(Tao, PetscInt, PetscReal, PetscReal, PetscReal, PetscReal);
455: typedef struct _n_TaoMonitorDrawCtx *TaoMonitorDrawCtx;
456: PETSC_EXTERN PetscErrorCode TaoMonitorDrawCtxCreate(MPI_Comm, const char[], const char[], int, int, int, int, PetscInt, TaoMonitorDrawCtx *);
457: PETSC_EXTERN PetscErrorCode TaoMonitorDrawCtxDestroy(TaoMonitorDrawCtx *);
459: /*E
460: TaoBRGNRegularizationType - The regularization added in the `TAOBRGN` solver.
462: Values:
463: + TAOBRGN_REGULARIZATION_USER - A user-defined regularizer
464: . TAOBRGN_REGULARIZATION_L2PROX - $\tfrac{1}{2}\|x - x_k\|_2^$, where $x_k$ is the latest solution
465: . TAOBRGN_REGULARIZATION_L2PURE - $\tfrac{1}{2}\|x\|_2^2$
466: . TAOBRGN_REGULARIZATION_L1DICT - $\|D x\|_1$, where $D$ is a dictionary matrix
467: - TAOBRGN_REGULARIZATION_LM - Levenberg-Marquardt, $\tfrac{1}{2} x^T \mathrm{diag}(J^T J) x$, where $J$ is the Jacobian of the least-squares residual
469: Options database Key:
470: . -tao_brgn_regularization_type (l2prox|l2pure|l1dict|lm|user) - select one of the regularization types
472: Level: advanced
474: Notes:
475: If `TAOBRGN_REGULARIZATION_USER`, the regularizer is set either by calling
476: `TaoBRGNSetRegularizerObjectiveAndGradientRoutine()` and
477: `TaoBRGNSetRegulazerHessianRoutine()`
479: If `TAOBRGN_REGULARIZATION_L1DICT`, the dictionary matrix is set with `TaoBRGNSetDictionaryMatrix()` and the smoothing parameter of the
480: approximate $\ell_1$ norm is set with `TaoBRGNSetL1SmoothEpsilon()`.
482: If `TAOBRGN_REGULARIZATION_LM`, the diagonal damping vector $\mathrm{diag}(J^T J)$ can be obtained with `TaoBRGNGetDampingVector()`.
484: .seealso: [](ch_tao), `Tao`, `TaoBRGNGetSubsolver()`, `TaoBRGNSetRegularizerWeight()`, `TaoBRGNSetL1SmoothEpsilon()`, `TaoBRGNSetDictionaryMatrix()`,
485: `TaoBRGNSetRegularizerObjectiveAndGradientRoutine()`, `TaoBRGNSetRegularizerHessianRoutine()`,
486: `TaoBRGNGetRegularizationType()`, `TaoBRGNSetRegularizationType()`
487: E*/
488: typedef enum {
489: TAOBRGN_REGULARIZATION_USER,
490: TAOBRGN_REGULARIZATION_L2PROX,
491: TAOBRGN_REGULARIZATION_L2PURE,
492: TAOBRGN_REGULARIZATION_L1DICT,
493: TAOBRGN_REGULARIZATION_LM,
494: } TaoBRGNRegularizationType;
496: PETSC_EXTERN const char *const TaoBRGNRegularizationTypes[];
498: PETSC_EXTERN PetscErrorCode TaoBRGNGetSubsolver(Tao, Tao *);
499: PETSC_EXTERN PetscErrorCode TaoBRGNGetRegularizationType(Tao, TaoBRGNRegularizationType *);
500: PETSC_EXTERN PetscErrorCode TaoBRGNSetRegularizationType(Tao, TaoBRGNRegularizationType);
501: PETSC_EXTERN PetscErrorCode TaoBRGNSetRegularizerObjectiveAndGradientRoutine(Tao, PetscErrorCode (*)(Tao, Vec, PetscReal *, Vec, PetscCtx), PetscCtx);
502: PETSC_EXTERN PetscErrorCode TaoBRGNSetRegularizerHessianRoutine(Tao, Mat, PetscErrorCode (*)(Tao, Vec, Mat, PetscCtx), PetscCtx);
503: PETSC_EXTERN PetscErrorCode TaoBRGNSetRegularizerWeight(Tao, PetscReal);
504: PETSC_EXTERN PetscErrorCode TaoBRGNSetL1SmoothEpsilon(Tao, PetscReal);
505: PETSC_EXTERN PetscErrorCode TaoBRGNSetDictionaryMatrix(Tao, Mat);
506: PETSC_EXTERN PetscErrorCode TaoBRGNGetDampingVector(Tao, Vec *);
508: PETSC_EXTERN PetscErrorCode TaoBNCGSetType(Tao, TaoBNCGType);
509: PETSC_EXTERN PetscErrorCode TaoBNCGGetType(Tao, TaoBNCGType *);
511: PETSC_EXTERN PetscErrorCode TaoADMMGetMisfitSubsolver(Tao, Tao *);
512: PETSC_EXTERN PetscErrorCode TaoADMMGetRegularizationSubsolver(Tao, Tao *);
513: PETSC_EXTERN PetscErrorCode TaoADMMGetDualVector(Tao, Vec *);
514: PETSC_EXTERN PetscErrorCode TaoADMMGetSpectralPenalty(Tao, PetscReal *);
515: PETSC_EXTERN PetscErrorCode TaoADMMSetSpectralPenalty(Tao, PetscReal);
516: PETSC_EXTERN PetscErrorCode TaoGetADMMParentTao(Tao, Tao *);
517: PETSC_EXTERN PetscErrorCode TaoADMMSetConstraintVectorRHS(Tao, Vec);
518: PETSC_EXTERN PetscErrorCode TaoADMMSetRegularizerCoefficient(Tao, PetscReal);
519: PETSC_EXTERN PetscErrorCode TaoADMMGetRegularizerCoefficient(Tao, PetscReal *);
520: PETSC_EXTERN PetscErrorCode TaoADMMSetMisfitConstraintJacobian(Tao, Mat, Mat, PetscErrorCode (*)(Tao, Vec, Mat, Mat, PetscCtx), PetscCtx);
521: PETSC_EXTERN PetscErrorCode TaoADMMSetRegularizerConstraintJacobian(Tao, Mat, Mat, PetscErrorCode (*)(Tao, Vec, Mat, Mat, PetscCtx), PetscCtx);
522: PETSC_EXTERN PetscErrorCode TaoADMMSetRegularizerHessianRoutine(Tao, Mat, Mat, PetscErrorCode (*)(Tao, Vec, Mat, Mat, PetscCtx), PetscCtx);
523: PETSC_EXTERN PetscErrorCode TaoADMMSetRegularizerObjectiveAndGradientRoutine(Tao, PetscErrorCode (*)(Tao, Vec, PetscReal *, Vec, PetscCtx), PetscCtx);
524: PETSC_EXTERN PetscErrorCode TaoADMMSetMisfitHessianRoutine(Tao, Mat, Mat, PetscErrorCode (*)(Tao, Vec, Mat, Mat, PetscCtx), PetscCtx);
525: PETSC_EXTERN PetscErrorCode TaoADMMSetMisfitObjectiveAndGradientRoutine(Tao, PetscErrorCode (*)(Tao, Vec, PetscReal *, Vec, PetscCtx), PetscCtx);
526: PETSC_EXTERN PetscErrorCode TaoADMMSetMisfitHessianChangeStatus(Tao, PetscBool);
527: PETSC_EXTERN PetscErrorCode TaoADMMSetRegHessianChangeStatus(Tao, PetscBool);
528: PETSC_EXTERN PetscErrorCode TaoADMMSetMinimumSpectralPenalty(Tao, PetscReal);
529: PETSC_EXTERN PetscErrorCode TaoADMMSetRegularizerType(Tao, TaoADMMRegularizerType);
530: PETSC_EXTERN PetscErrorCode TaoADMMGetRegularizerType(Tao, TaoADMMRegularizerType *);
531: PETSC_EXTERN PetscErrorCode TaoADMMSetUpdateType(Tao, TaoADMMUpdateType);
532: PETSC_EXTERN PetscErrorCode TaoADMMGetUpdateType(Tao, TaoADMMUpdateType *);
534: PETSC_EXTERN PetscErrorCode TaoALMMGetType(Tao, TaoALMMType *);
535: PETSC_EXTERN PetscErrorCode TaoALMMSetType(Tao, TaoALMMType);
536: PETSC_EXTERN PetscErrorCode TaoALMMGetSubsolver(Tao, Tao *);
537: PETSC_EXTERN PetscErrorCode TaoALMMSetSubsolver(Tao, Tao);
538: PETSC_EXTERN PetscErrorCode TaoALMMGetMultipliers(Tao, Vec *);
539: PETSC_EXTERN PetscErrorCode TaoALMMSetMultipliers(Tao, Vec);
540: PETSC_EXTERN PetscErrorCode TaoALMMGetPrimalIS(Tao, IS *, IS *);
541: PETSC_EXTERN PetscErrorCode TaoALMMGetDualIS(Tao, IS *, IS *);
543: PETSC_EXTERN PetscErrorCode TaoVecGetSubVec(Vec, IS, TaoSubsetType, PetscReal, Vec *);
544: PETSC_EXTERN PetscErrorCode TaoMatGetSubMat(Mat, IS, Vec, TaoSubsetType, Mat *);
545: PETSC_EXTERN PetscErrorCode TaoGradientNorm(Tao, Vec, NormType, PetscReal *);
546: PETSC_EXTERN PetscErrorCode TaoEstimateActiveBounds(Vec, Vec, Vec, Vec, Vec, Vec, PetscReal, PetscReal *, IS *, IS *, IS *, IS *, IS *);
547: PETSC_EXTERN PetscErrorCode TaoBoundStep(Vec, Vec, Vec, IS, IS, IS, PetscReal, Vec);
548: PETSC_EXTERN PetscErrorCode TaoBoundSolution(Vec, Vec, Vec, PetscReal, PetscInt *, Vec);
550: PETSC_EXTERN PetscErrorCode MatCreateSubMatrixFree(Mat, IS, IS, Mat *);
552: PETSC_EXTERN PetscErrorCode TaoGetTerm(Tao, PetscReal *, TaoTerm *, Vec *, Mat *);
553: PETSC_EXTERN PetscErrorCode TaoAddTerm(Tao, const char[], PetscReal, TaoTerm, Vec, Mat);
555: #include <petsctao_deprecations.h>