47 using state_type =
typename base_type::state_type;
48 using time_type =
typename base_type::time_type;
49 using value_type =
typename base_type::value_type;
52 explicit SRIIntegrator(std::shared_ptr<typename base_type::sde_problem_type> problem,
53 std::shared_ptr<typename base_type::wiener_process_type> wiener =
nullptr,
54 tableau_type tableau = SRIIntegrator::create_sriw1_tableau())
56 , tableau_(std::move(tableau)) {}
58 void step(state_type& state, time_type dt)
override {
59 const int stages = tableau_.stages;
62 std::vector<state_type> H0(stages), H1(stages);
63 for (
int i = 0; i < stages; ++i) {
74 this->wiener_->generate_increment(dW, dt);
75 this->wiener_->generate_increment(dZ, dt);
78 value_type sqrt3 = std::sqrt(
static_cast<value_type
>(3));
79 value_type sqrt_dt = std::sqrt(
static_cast<value_type
>(dt));
88 for (
size_t j = 0; j < state.size(); ++j) {
89 auto dW_it = dW.begin();
90 auto dZ_it = dZ.begin();
91 auto chi1_it = chi1.begin();
92 auto chi2_it = chi2.begin();
93 auto chi3_it = chi3.begin();
95 value_type dW_val = dW_it[j];
96 value_type dW_squared = dW_val * dW_val;
98 chi1_it[j] =
static_cast<value_type
>(0.5) * (dW_squared - dt) / sqrt_dt;
99 chi2_it[j] =
static_cast<value_type
>(0.5) * (dW_val + dZ_it[j] / sqrt3);
100 chi3_it[j] =
static_cast<value_type
>(1.0/6.0) * (dW_val * dW_squared - 3 * dW_val * dt) / dt;
104 for (
size_t j = 0; j < state.size(); ++j) {
105 auto state_it = state.begin();
106 auto H0_0_it = H0[0].begin();
107 auto H1_0_it = H1[0].begin();
108 H0_0_it[j] = state_it[j];
109 H1_0_it[j] = state_it[j];
113 for (
int i = 1; i < stages; ++i) {
119 for (
int j = 0; j < i; ++j) {
120 this->problem_->drift(this->current_time_ + tableau_.c0[j] * dt, H0[j], ftmp);
121 this->problem_->diffusion(this->current_time_ + tableau_.c1[j] * dt, H1[j], gtmp);
123 for (
size_t k = 0; k < state.size(); ++k) {
124 auto A0temp_it = A0temp.begin();
125 auto A1temp_it = A1temp.begin();
126 auto B0temp_it = B0temp.begin();
127 auto B1temp_it = B1temp.begin();
128 auto ftmp_it = ftmp.begin();
129 auto gtmp_it = gtmp.begin();
130 auto chi1_it = chi1.begin();
131 auto chi2_it = chi2.begin();
133 A0temp_it[k] += tableau_.A0[j][i] * ftmp_it[k];
134 A1temp_it[k] += tableau_.A1[j][i] * ftmp_it[k];
135 B0temp_it[k] += tableau_.B0[j][i] * gtmp_it[k];
136 B1temp_it[k] += tableau_.B1[j][i] * gtmp_it[k] * chi1_it[k];
141 for (
size_t k = 0; k < state.size(); ++k) {
142 auto state_it = state.begin();
143 auto H0_i_it = H0[i].begin();
144 auto H1_i_it = H1[i].begin();
145 auto A0temp_it = A0temp.begin();
146 auto A1temp_it = A1temp.begin();
147 auto B0temp_it = B0temp.begin();
148 auto B1temp_it = B1temp.begin();
149 auto chi2_it = chi2.begin();
150 auto dW_it = dW.begin();
152 H0_i_it[k] = state_it[k] + dt * A0temp_it[k] + B0temp_it[k] * dW_it[k];
153 H1_i_it[k] = state_it[k] + dt * A1temp_it[k] + B0temp_it[k] * sqrt_dt + B1temp_it[k] + chi2_it[k] * B0temp_it[k];
163 std::fill(drift_sum.begin(), drift_sum.end(), value_type(0));
164 std::fill(E1.begin(), E1.end(), value_type(0));
165 std::fill(E2.begin(), E2.end(), value_type(0));
166 std::fill(E3.begin(), E3.end(), value_type(0));
168 for (
int i = 0; i < stages; ++i) {
169 this->problem_->drift(this->current_time_ + tableau_.c0[i] * dt, H0[i], ftmp);
170 this->problem_->diffusion(this->current_time_ + tableau_.c1[i] * dt, H1[i], gtmp);
172 for (
size_t k = 0; k < state.size(); ++k) {
173 auto drift_sum_it = drift_sum.begin();
174 auto E1_it = E1.begin();
175 auto E2_it = E2.begin();
176 auto E3_it = E3.begin();
177 auto ftmp_it = ftmp.begin();
178 auto gtmp_it = gtmp.begin();
179 auto dW_it = dW.begin();
180 auto chi1_it = chi1.begin();
181 auto chi2_it = chi2.begin();
182 auto chi3_it = chi3.begin();
184 drift_sum_it[k] += tableau_.alpha[i] * ftmp_it[k];
185 E1_it[k] += tableau_.beta1[i] * gtmp_it[k] * dW_it[k];
186 E2_it[k] += tableau_.beta2[i] * gtmp_it[k] * chi1_it[k];
187 E2_it[k] += tableau_.beta3[i] * gtmp_it[k] * chi2_it[k];
188 E3_it[k] += tableau_.beta4[i] * gtmp_it[k] * chi3_it[k];
193 for (
size_t k = 0; k < state.size(); ++k) {
194 auto state_it = state.begin();
195 auto drift_sum_it = drift_sum.begin();
196 auto E1_it = E1.begin();
197 auto E2_it = E2.begin();
198 auto E3_it = E3.begin();
200 state_it[k] += dt * drift_sum_it[k] + E1_it[k] + E2_it[k] + E3_it[k];
203 this->advance_time(dt);
206 std::string name()
const override {
207 return "SRI (Strong Order 1.5 for General Itô SDEs)";
221 tableau.order =
static_cast<value_type
>(1.5);
224 tableau.A0 = {{0, 0}, {1, 0}};
225 tableau.A1 = {{0, 0}, {1, 0}};
227 tableau.alpha = {
static_cast<value_type
>(0.5),
static_cast<value_type
>(0.5)};
229 tableau.B0 = {{0, 0}, {1, 0}};
230 tableau.B1 = {{0, 0}, {1, 0}};
232 tableau.beta1 = {
static_cast<value_type
>(0.5),
static_cast<value_type
>(0.5)};
233 tableau.beta2 = {0, 1};
234 tableau.beta3 = {0,
static_cast<value_type
>(0.5)};
235 tableau.beta4 = {0,
static_cast<value_type
>(1.0/6.0)};