-
Notifications
You must be signed in to change notification settings - Fork 1.2k
/
implicit_integrator.cc
457 lines (386 loc) · 17.5 KB
/
implicit_integrator.cc
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
#include "drake/systems/analysis/implicit_integrator.h"
#include <cmath>
#include <stdexcept>
#include "drake/common/autodiff.h"
#include "drake/common/drake_assert.h"
#include "drake/common/text_logging.h"
#include "drake/math/autodiff_gradient.h"
namespace drake {
namespace systems {
template <class T>
void ImplicitIntegrator<T>::DoResetStatistics() {
num_iter_factorizations_ = 0;
num_jacobian_function_evaluations_ = 0;
num_jacobian_evaluations_ = 0;
DoResetImplicitIntegratorStatistics();
}
template <class T>
void ImplicitIntegrator<T>::DoReset() {
J_.resize(0, 0);
DoResetCachedJacobianRelatedMatrices();
// Call any Reset() provided by child integrator classes.
DoImplicitIntegratorReset();
}
template <class T>
void ImplicitIntegrator<T>::ComputeAutoDiffJacobian(
const System<T>& system, const T& t, const VectorX<T>& xt,
const Context<T>& context, MatrixX<T>* J) {
DRAKE_LOGGER_DEBUG(" ImplicitIntegrator Compute Autodiff Jacobian t={}", t);
// TODO(antequ): Investigate how to refactor this method to use
// math::jacobian(), if possible.
// Create AutoDiff versions of the state vector.
VectorX<AutoDiffXd> a_xt = xt;
// Set the size of the derivatives and prepare for Jacobian calculation.
const int n_state_dim = a_xt.size();
for (int i = 0; i < n_state_dim; ++i)
a_xt[i].derivatives() = VectorX<T>::Unit(n_state_dim, i);
// Get the system and the context in AutoDiffable format. Inputs must also
// be copied to the context used by the AutoDiff'd system (which is
// accomplished using FixInputPortsFrom()).
// TODO(edrumwri): Investigate means for moving as many of the operations
// below offline (or with lower frequency than once-per-
// Jacobian calculation) as is possible. These operations
// are likely to be expensive.
const auto adiff_system = system.ToAutoDiffXd();
std::unique_ptr<Context<AutoDiffXd>> adiff_context = adiff_system->
AllocateContext();
adiff_context->SetTimeStateAndParametersFrom(context);
adiff_system->FixInputPortsFrom(system, context, adiff_context.get());
adiff_context->SetTime(t);
// Set the continuous state in the context.
adiff_context->SetContinuousState(a_xt);
// Evaluate the derivatives at that state.
const VectorX<AutoDiffXd> result =
this->EvalTimeDerivatives(*adiff_system, *adiff_context).CopyToVector();
*J = math::ExtractGradient(result);
// Sometimes the system's derivatives f(t, x) do not depend on its states, for
// example, when f(t, x) = constant or when f(t, x) depends only on t. In this
// case, make sure that the Jacobian isn't a n ✕ 0 matrix (this will cause a
// segfault when forming Newton iteration matrices); if it is, we set it equal
// to an n x n zero matrix.
if (J->cols() == 0) {
*J = MatrixX<T>::Zero(n_state_dim, n_state_dim);
}
}
template <class T>
void ImplicitIntegrator<T>::ComputeForwardDiffJacobian(
const System<T>&, const T& t, const VectorX<T>& xt, Context<T>* context,
MatrixX<T>* J) {
using std::abs;
// Set epsilon to the square root of machine precision.
const double eps = std::sqrt(std::numeric_limits<double>::epsilon());
// Get the number of continuous state variables xt.
const int n = context->num_continuous_states();
DRAKE_LOGGER_DEBUG(
" ImplicitIntegrator Compute Forwarddiff {}-Jacobian t={}", n, t);
DRAKE_LOGGER_DEBUG(
" computing from state {}", xt.transpose());
// Initialize the Jacobian.
J->resize(n, n);
// Evaluate f(t,xt).
context->SetTimeAndContinuousState(t, xt);
const VectorX<T> f = this->EvalTimeDerivatives(*context).CopyToVector();
// Compute the Jacobian.
VectorX<T> xt_prime = xt;
for (int i = 0; i < n; ++i) {
// Compute a good increment to the dimension using approximately 1/eps
// digits of precision. Note that if |xt| is large, the increment will
// be large as well. If |xt| is small, the increment will be no smaller
// than eps.
const T abs_xi = abs(xt(i));
T dxi(abs_xi);
if (dxi <= 1) {
// When |xt[i]| is small, increment will be eps.
dxi = eps;
} else {
// |xt[i]| not small; make increment a fraction of |xt[i]|.
dxi = eps * abs_xi;
}
// Update xt', minimizing the effect of roundoff error by ensuring that
// x and dx differ by an exactly representable number. See p. 192 of
// Press, W., Teukolsky, S., Vetterling, W., and Flannery, P. Numerical
// Recipes in C++, 2nd Ed., Cambridge University Press, 2002.
xt_prime(i) = xt(i) + dxi;
dxi = xt_prime(i) - xt(i);
// TODO(sherm1) This is invalidating q, v, and z but we only changed one.
// Switch to a method that invalides just the relevant
// partition, and ideally modify only the one changed element.
// Compute f' and set the relevant column of the Jacobian matrix.
context->SetTimeAndContinuousState(t, xt_prime);
J->col(i) = (this->EvalTimeDerivatives(*context).CopyToVector() - f) / dxi;
// Reset xt' to xt.
xt_prime(i) = xt(i);
}
}
template <class T>
void ImplicitIntegrator<T>::ComputeCentralDiffJacobian(
const System<T>&, const T& t, const VectorX<T>& xt, Context<T>* context,
MatrixX<T>* J) {
using std::abs;
// Cube root of machine precision (indicated by theory) seems a bit coarse.
// Pick power of eps halfway between 6/12 (i.e., 1/2) and 4/12 (i.e., 1/3).
const double eps = std::pow(std::numeric_limits<double>::epsilon(), 5.0/12);
// Get the number of continuous state variables xt.
const int n = context->num_continuous_states();
DRAKE_LOGGER_DEBUG(
" ImplicitIntegrator Compute Centraldiff {}-Jacobian t={}", n, t);
// Initialize the Jacobian.
J->resize(n, n);
// Evaluate f(t,xt).
context->SetTimeAndContinuousState(t, xt);
const VectorX<T> f = this->EvalTimeDerivatives(*context).CopyToVector();
// Compute the Jacobian.
VectorX<T> xt_prime = xt;
for (int i = 0; i < n; ++i) {
// Compute a good increment to the dimension using approximately 1/eps
// digits of precision. Note that if |xt| is large, the increment will
// be large as well. If |xt| is small, the increment will be no smaller
// than eps.
const T abs_xi = abs(xt(i));
T dxi(abs_xi);
if (dxi <= 1) {
// When |xt[i]| is small, increment will be eps.
dxi = eps;
} else {
// |xt[i]| not small; make increment a fraction of |xt[i]|.
dxi = eps * abs_xi;
}
// Update xt', minimizing the effect of roundoff error, by ensuring that
// x and dx differ by an exactly representable number. See p. 192 of
// Press, W., Teukolsky, S., Vetterling, W., and Flannery, P. Numerical
// Recipes in C++, 2nd Ed., Cambridge University Press, 2002.
xt_prime(i) = xt(i) + dxi;
const T dxi_plus = xt_prime(i) - xt(i);
// TODO(sherm1) This is invalidating q, v, and z but we only changed one.
// Switch to a method that invalides just the relevant
// partition, and ideally modify only the one changed element.
// Compute f(x+dx).
context->SetContinuousState(xt_prime);
VectorX<T> fprime_plus = this->EvalTimeDerivatives(*context).CopyToVector();
// Update xt' again, minimizing the effect of roundoff error.
xt_prime(i) = xt(i) - dxi;
const T dxi_minus = xt(i) - xt_prime(i);
// Compute f(x-dx).
context->SetContinuousState(xt_prime);
VectorX<T> fprime_minus = this->EvalTimeDerivatives(
*context).CopyToVector();
// Set the Jacobian column.
J->col(i) = (fprime_plus - fprime_minus) / (dxi_plus + dxi_minus);
// Reset xt' to xt.
xt_prime(i) = xt(i);
}
}
template <class T>
void ImplicitIntegrator<T>::IterationMatrix::SetAndFactorIterationMatrix(
const MatrixX<T>& iteration_matrix) {
LU_.compute(iteration_matrix);
matrix_factored_ = true;
}
template <class T>
VectorX<T> ImplicitIntegrator<T>::IterationMatrix::Solve(
const VectorX<T>& b) const {
return LU_.solve(b);
}
template <typename T>
typename ImplicitIntegrator<T>::ConvergenceStatus
ImplicitIntegrator<T>::CheckNewtonConvergence(
int iteration, const VectorX<T>& xtplus, const VectorX<T>& dx,
const T& dx_norm, const T& last_dx_norm) const {
// The check below looks for convergence by identifying cases where the
// update to the state results in no change.
// Note: Since we are performing this check at the end of the iteration,
// after xtplus has been updated, we also know that there is at least some
// change to the state, no matter how small, on a non-stationary system.
// Future maintainers should make sure this check only occurs after a change
// has been made to the state.
if (this->IsUpdateZero(xtplus, dx)) {
DRAKE_LOGGER_DEBUG("magnitude of state update indicates convergence");
return ConvergenceStatus::kConverged;
}
// Compute the convergence rate and check convergence.
// [Hairer, 1996] notes that this convergence strategy should only be applied
// after *at least* two iterations (p. 121). In practice, we find that it
// needs to run at least three iterations otherwise some error-controlled runs
// may choke, hence we check if iteration > 1.
if (iteration > 1) {
// TODO(edrumwri) Hairer's RADAU5 implementation (allegedly) uses
// theta = sqrt(dx[k] / dx[k-2]) while DASSL uses
// theta = pow(dx[k] / dx[0], 1/k), so investigate setting
// theta to these alternative values for minimizing convergence failures.
const T theta = dx_norm / last_dx_norm;
const T eta = theta / (1 - theta);
DRAKE_LOGGER_DEBUG("Newton-Raphson loop {} theta: {}, eta: {}",
iteration, theta, eta);
// Look for divergence.
if (theta > 1) {
DRAKE_LOGGER_DEBUG("Newton-Raphson divergence detected");
return ConvergenceStatus::kDiverged;
}
// Look for convergence using Equation IV.8.10 from [Hairer, 1996].
// [Hairer, 1996] determined values of kappa in [0.01, 0.1] work most
// efficiently on a number of test problems with *Radau5* (a fifth order
// implicit integrator), p. 121. We select a value halfway in-between.
const double kappa = 0.05;
const double k_dot_tol = kappa * this->get_accuracy_in_use();
if (eta * dx_norm < k_dot_tol) {
DRAKE_LOGGER_DEBUG("Newton-Raphson converged; η = {}", eta);
return ConvergenceStatus::kConverged;
}
}
return ConvergenceStatus::kNotConverged;
}
template <class T>
bool ImplicitIntegrator<T>::IsBadJacobian(const MatrixX<T>& J) const {
return !J.allFinite();
}
template <class T>
const MatrixX<T>& ImplicitIntegrator<T>::CalcJacobian(const T& t,
const VectorX<T>& x) {
// We change the context but will change it back.
Context<T>* context = this->get_mutable_context();
// Get the current time and state.
const T t_current = context->get_time();
const VectorX<T> x_current = context->get_continuous_state_vector().
CopyToVector();
// Update the time and state.
context->SetTimeAndContinuousState(t, x);
num_jacobian_evaluations_++;
// Get the current number of ODE evaluations.
int64_t current_ODE_evals = this->get_num_derivative_evaluations();
// Get a the system.
const System<T>& system = this->get_system();
// TODO(edrumwri): Give the caller the option to provide their own Jacobian.
[this, context, &system, &t, &x]() {
switch (jacobian_scheme_) {
case JacobianComputationScheme::kForwardDifference:
ComputeForwardDiffJacobian(system, t, x, &*context, &J_);
break;
case JacobianComputationScheme::kCentralDifference:
ComputeCentralDiffJacobian(system, t, x, &*context, &J_);
break;
case JacobianComputationScheme::kAutomatic:
ComputeAutoDiffJacobian(system, t, x, *context, &J_);
break;
}
}();
// Use the new number of ODE evaluations to determine the number of Jacobian
// evaluations.
num_jacobian_function_evaluations_ += this->get_num_derivative_evaluations()
- current_ODE_evals;
// Reset the time and state.
context->SetTimeAndContinuousState(t_current, x_current);
// Mark the Jacobian as fresh, so that we don't recompute it unnecessarily
// during the step.
jacobian_is_fresh_ = true;
return J_;
}
template <class T>
void ImplicitIntegrator<T>::FreshenMatricesIfFullNewton(
const T& t, const VectorX<T>& xt, const T& h,
const std::function<void(const MatrixX<T>&, const T&,
typename ImplicitIntegrator<T>::IterationMatrix*)>&
compute_and_factor_iteration_matrix,
typename ImplicitIntegrator<T>::IterationMatrix* iteration_matrix) {
DRAKE_DEMAND(iteration_matrix != nullptr);
// Return immediately if full-Newton is not in use.
if (!get_use_full_newton()) return;
// Compute the initial Jacobian and iteration matrices and factor them.
MatrixX<T>& J = get_mutable_jacobian();
J = CalcJacobian(t, xt);
++num_iter_factorizations_;
compute_and_factor_iteration_matrix(J, h, iteration_matrix);
}
template <class T>
bool ImplicitIntegrator<T>::MaybeFreshenMatrices(
const T& t, const VectorX<T>& xt, const T& h, int trial,
const std::function<void(const MatrixX<T>&, const T&,
typename ImplicitIntegrator<T>::IterationMatrix*)>&
compute_and_factor_iteration_matrix,
typename ImplicitIntegrator<T>::IterationMatrix* iteration_matrix) {
// Compute the initial Jacobian and iteration matrices and factor them, if
// necessary.
MatrixX<T>& J = get_mutable_jacobian();
if (!get_reuse() || J.rows() == 0 || IsBadJacobian(J)) {
J = CalcJacobian(t, xt);
++num_iter_factorizations_;
compute_and_factor_iteration_matrix(J, h, iteration_matrix);
return true; // Indicate success.
}
// Reuse is activated, Jacobian is fully sized, and Jacobian is not "bad".
// If the iteration matrix has not been set and factored, do only that.
// In most cases, the iteration matrix is already factorized if the Jacobian
// has been properly computed. However, one example where this code block
// might be triggered would be if the child integrator uses the same Jacobian,
// but two different iteration matrices, for two methods, such as implicit
// Euler with implicit Trapezoid error estimation. During the first implicit
// Euler step, the Jacobian is computed and the implicit Euler matrix is
// factorized. Afterwards, during the first implicit Trapezoid step,
// the Jacobian (which it shares with implicit Euler) is fresh, but the
// implicit Trapezoid iteration matrix is not factorized, and so this block
// of code will factorize it.
if (!iteration_matrix->matrix_factored()) {
++num_iter_factorizations_;
compute_and_factor_iteration_matrix(J, h, iteration_matrix);
return true; // Indicate success.
}
switch (trial) {
case 1:
// For the first trial, we do nothing: this will cause the Newton-Raphson
// process to use the last computed (and already factored) iteration
// matrix. This matrix may be from a previous time-step or a previously-
// attempted step size.
return true; // Indicate success.
case 2: {
// For the second trial, we know the first trial, which uses the last
// computed iteration matrix, has already failed. The last computed
// iteration matrix may be from many time steps ago, or it may be from a
// different step size. We perform the (likely) next least expensive
// operation, which is re-constructing and factoring the iteration
// matrix, using the last computed Jacobian. The last computed Jacobian
// may also be from many time steps ago, or it may be from a previously-
// attempted step size.
// TODO(antequ): In two particular cases, this may compute the same
// iteration matrix twice. Currently they are rare and unimportant, but
// in the future, it may be worth it to investigate optimizing these two
// cases if they make a performance difference:
// 1. During the first time step of the simulation, trial 1 will compute
// the initial iteration matrix, and trial 2 will compute the same
// iteration matrix again if trial 1 fails.
// 2. For implicit Euler with step doubling, it is possible that trial 3
// gets triggered on the first small step, which then fails, and after the
// step size is halved, trial 2 is triggered on the first large step,
// which requires the same iteration matrix (so the matrix is correct
// and does not actually need recomputation).
// In both cases, the right thing to do would be to skip to trial 3.
++num_iter_factorizations_;
compute_and_factor_iteration_matrix(J, h, iteration_matrix);
return true;
}
case 3: {
// For the third trial, we know that the first two trials, which
// exhausted all our options short of recomputing the Jacobian, have
// failed.
// The Jacobian matrix may already be "fresh", meaning that there is
// nothing more that can be tried (Jacobian and iteration matrix are both
// fresh), and we need to indicate failure.
if (jacobian_is_fresh_)
return false;
// Otherwise, we can reform the Jacobian matrix and refactor the
// iteration matrix.
J = CalcJacobian(t, xt);
++num_iter_factorizations_;
compute_and_factor_iteration_matrix(J, h, iteration_matrix);
return true;
case 4: {
// Trial #4 indicates failure.
return false;
}
default:
throw std::domain_error("Unexpected trial number.");
}
}
}
} // namespace systems
} // namespace drake
DRAKE_DEFINE_CLASS_TEMPLATE_INSTANTIATIONS_ON_DEFAULT_NONSYMBOLIC_SCALARS(
class drake::systems::ImplicitIntegrator)