# source:branches/2925_AutoDiffForDynamicalModels/HeuristicLab.Problems.DynamicalSystemsModelling/3.3/sundials/include/sundials/sundials_iterative.h@16222

Last change on this file since 16222 was 16222, checked in by gkronber, 4 years ago
• added source code of cvodes library (part of sundials) which provides functionality to calculate gradients for the parameters of partial differential equation models efficiently using the 'adjoint state method'.
• added compiled version of cvodes
File size: 13.8 KB
Line
1/*
2 * -----------------------------------------------------------------
3 * $Revision$
4 * $Date$
5 * -----------------------------------------------------------------
6 * Programmer(s): Scott D. Cohen and Alan C. Hindmarsh @ LLNL
7 * -----------------------------------------------------------------
9 * Copyright (c) 2014, Lawrence Livermore National Security
10 * This work was performed under the auspices of the U.S. Department
11 * of Energy by Lawrence Livermore National Laboratory in part under
12 * Contract W-7405-Eng-48 and in part under Contract DE-AC52-07NA27344.
13 * Produced at the Lawrence Livermore National Laboratory.
15 * For details, see the LICENSE file.
17 * -----------------------------------------------------------------
18 * This header file contains declarations intended for use by
19 * generic iterative solvers of Ax = b. The enumeration gives
20 * symbolic names for the type  of preconditioning to be used.
21 * The function type declarations give the prototypes for the
22 * functions to be called within an iterative linear solver, that
23 * are responsible for
24 *    multiplying A by a given vector v (ATimesFn),
25 *    setting up a preconditioner P (PSetupFn), and
26 *    solving the preconditioner equation Pz = r (PSolveFn).
27 * -----------------------------------------------------------------
28 */
29
30#ifndef _ITERATIVE_H
31#define _ITERATIVE_H
32
33#include <sundials/sundials_nvector.h>
34
35#ifdef __cplusplus  /* wrapper to enable C++ usage */
36extern "C" {
37#endif
38
39
40/*
41 * -----------------------------------------------------------------
42 * enum : types of preconditioning
43 * -----------------------------------------------------------------
44 * PREC_NONE  : The iterative linear solver should not use
45 *              preconditioning.
46 *
47 * PREC_LEFT  : The iterative linear solver uses preconditioning on
48 *              the left only.
49 *
50 * PREC_RIGHT : The iterative linear solver uses preconditioning on
51 *              the right only.
52 *
53 * PREC_BOTH  : The iterative linear solver uses preconditioning on
54 *              both the left and the right.
55 * -----------------------------------------------------------------
56 */
57
58enum { PREC_NONE, PREC_LEFT, PREC_RIGHT, PREC_BOTH };
59
60/*
61 * -----------------------------------------------------------------
62 * enum : types of Gram-Schmidt routines
63 * -----------------------------------------------------------------
64 * MODIFIED_GS  : The iterative solver uses the modified
65 *                Gram-Schmidt routine ModifiedGS listed in this
66 *                file.
67 *
68 * CLASSICAL_GS : The iterative solver uses the classical
69 *                Gram-Schmidt routine ClassicalGS listed in this
70 *                file.
71 * -----------------------------------------------------------------
72 */
73
74enum { MODIFIED_GS = 1, CLASSICAL_GS = 2 };
75
76/*
77 * -----------------------------------------------------------------
78 * Type: ATimesFn
79 * -----------------------------------------------------------------
80 * An ATimesFn multiplies Av and stores the result in z. The
81 * caller is responsible for allocating memory for the z vector.
82 * The parameter A_data is a pointer to any information about A
83 * which the function needs in order to do its job. The vector v
84 * is unchanged. An ATimesFn returns 0 if successful and a
85 * non-zero value if unsuccessful.
86 * -----------------------------------------------------------------
87 */
88
89typedef int (*ATimesFn)(void *A_data, N_Vector v, N_Vector z);
90
91/*
92 * -----------------------------------------------------------------
93 * Type: PSetupFn
94 * -----------------------------------------------------------------
95 * A PSetupFn is an integrator-supplied routine that accesses data
96 * stored in the integrator memory structure (P_data), and calls
97 * the user-supplied, integrator-specific preconditioner setup
98 * routine.
99 * -----------------------------------------------------------------
100 */
101
102typedef int (*PSetupFn)(void *P_data);
103
104/*
105 * -----------------------------------------------------------------
106 * Type: PSolveFn
107 * -----------------------------------------------------------------
108 * A PSolveFn solves the preconditioner equation Pz = r for the
109 * vector z. The caller is responsible for allocating memory for
110 * the z vector. The parameter P_data is a pointer to any
111 * information about P which the function needs in order to do
112 * its job. The parameter lr is input, and indicates whether P
113 * is to be taken as the left preconditioner or the right
114 * preconditioner: lr = 1 for left and lr = 2 for right.
115 * If preconditioning is on one side only, lr can be ignored.
116 * If the preconditioner is iterative, then it should strive to
117 * solve the preconditioner equation so that
118 *     || Pz - r ||_wrms < tol
119 * where the weight vector for the WRMS norm may be accessed from
120 * the main integrator memory structure.
121 * The vector r should not be modified by the PSolveFn.
122 * A PSolveFn returns 0 if successful and a non-zero value if
123 * unsuccessful.  On a failure, a negative return value indicates
124 * an unrecoverable condition, while a positive value indicates
125 * a recoverable one, in which the calling routine may reattempt
126 * the solution after updating preconditioner data.
127 * -----------------------------------------------------------------
128 */
129
130typedef int (*PSolveFn)(void *P_data, N_Vector r, N_Vector z,
131                        realtype tol, int lr);
132
133/*
134 * -----------------------------------------------------------------
135 * Function: ModifiedGS
136 * -----------------------------------------------------------------
137 * ModifiedGS performs a modified Gram-Schmidt orthogonalization
138 * of the N_Vector v[k] against the p unit N_Vectors at
139 * v[k-1], v[k-2], ..., v[k-p].
140 *
141 * v is an array of (k+1) N_Vectors v[i], i=0, 1, ..., k.
142 * v[k-1], v[k-2], ..., v[k-p] are assumed to have L2-norm
143 * equal to 1.
144 *
145 * h is the output k by k Hessenberg matrix of inner products.
146 * This matrix must be allocated row-wise so that the (i,j)th
147 * entry is h[i][j]. The inner products (v[i],v[k]),
148 * i=i0, i0+1, ..., k-1, are stored at h[i][k-1]. Here
149 * i0=SUNMAX(0,k-p).
150 *
151 * k is the index of the vector in the v array that needs to be
152 * orthogonalized against previous vectors in the v array.
153 *
154 * p is the number of previous vectors in the v array against
155 * which v[k] is to be orthogonalized.
156 *
157 * new_vk_norm is a pointer to memory allocated by the caller to
158 * hold the Euclidean norm of the orthogonalized vector v[k].
159 *
160 * If (k-p) < 0, then ModifiedGS uses p=k. The orthogonalized
161 * v[k] is NOT normalized and is stored over the old v[k]. Once
162 * the orthogonalization has been performed, the Euclidean norm
163 * of v[k] is stored in (*new_vk_norm).
164 *
165 * ModifiedGS returns 0 to indicate success. It cannot fail.
166 * -----------------------------------------------------------------
167 */
168
169SUNDIALS_EXPORT int ModifiedGS(N_Vector *v, realtype **h, int k, int p,
170             realtype *new_vk_norm);
171
172/*
173 * -----------------------------------------------------------------
174 * Function: ClassicalGS
175 * -----------------------------------------------------------------
176 * ClassicalGS performs a classical Gram-Schmidt
177 * orthogonalization of the N_Vector v[k] against the p unit
178 * N_Vectors at v[k-1], v[k-2], ..., v[k-p]. The parameters v, h,
179 * k, p, and new_vk_norm are as described in the documentation
180 * for ModifiedGS.
181 *
182 * temp is an N_Vector which can be used as workspace by the
183 * ClassicalGS routine.
184 *
185 * s is a length k array of realtype which can be used as
186 * workspace by the ClassicalGS routine.
187 *
188 * ClassicalGS returns 0 to indicate success. It cannot fail.
189 * -----------------------------------------------------------------
190 */
191
192SUNDIALS_EXPORT int ClassicalGS(N_Vector *v, realtype **h, int k, int p,
193        realtype *new_vk_norm, N_Vector temp, realtype *s);
194
195/*
196 * -----------------------------------------------------------------
197 * Function: QRfact
198 * -----------------------------------------------------------------
199 * QRfact performs a QR factorization of the Hessenberg matrix H.
200 *
201 * n is the problem size; the matrix H is (n+1) by n.
202 *
203 * h is the (n+1) by n Hessenberg matrix H to be factored. It is
204 * stored row-wise.
205 *
206 * q is an array of length 2*n containing the Givens rotations
207 * computed by this function. A Givens rotation has the form:
208 * | c  -s |
209 * | s   c |.
210 * The components of the Givens rotations are stored in q as
211 * (c, s, c, s, ..., c, s).
212 *
213 * job is a control flag. If job==0, then a new QR factorization
214 * is performed. If job!=0, then it is assumed that the first
215 * n-1 columns of h have already been factored and only the last
216 * column needs to be updated.
217 *
218 * QRfact returns 0 if successful. If a zero is encountered on
219 * the diagonal of the triangular factor R, then QRfact returns
220 * the equation number of the zero entry, where the equations are
221 * numbered from 1, not 0. If QRsol is subsequently called in
222 * this situation, it will return an error because it could not
223 * divide by the zero diagonal entry.
224 * -----------------------------------------------------------------
225 */
226
227SUNDIALS_EXPORT int QRfact(int n, realtype **h, realtype *q, int job);
228
229/*
230 * -----------------------------------------------------------------
231 * Function: QRsol
232 * -----------------------------------------------------------------
233 * QRsol solves the linear least squares problem
234 *
235 * min (b - H*x, b - H*x), x in R^n,
236 *
237 * where H is a Hessenberg matrix, and b is in R^(n+1).
238 * It uses the QR factors of H computed by QRfact.
239 *
240 * n is the problem size; the matrix H is (n+1) by n.
241 *
242 * h is a matrix (computed by QRfact) containing the upper
243 * triangular factor R of the original Hessenberg matrix H.
244 *
245 * q is an array of length 2*n (computed by QRfact) containing
246 * the Givens rotations used to factor H.
247 *
248 * b is the (n+1)-vector appearing in the least squares problem
249 * above.
250 *
251 * On return, b contains the solution x of the least squares
252 * problem, if QRsol was successful.
253 *
254 * QRsol returns a 0 if successful.  Otherwise, a zero was
255 * encountered on the diagonal of the triangular factor R.
256 * In this case, QRsol returns the equation number (numbered
257 * from 1, not 0) of the zero entry.
258 * -----------------------------------------------------------------
259 */
260
261SUNDIALS_EXPORT int QRsol(int n, realtype **h, realtype *q, realtype *b);
262
263#ifdef __cplusplus
264}
265#endif
266
267#endif
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