static action_t cont_macroexpand1() {
if (iscons(expr)) {
ref_t symbol = check_symbol(car(expr));
if (has_function(symbol)) {
ref_t func = get_function(symbol);
if (ismacro(func)) {
C(cont)->fn = cont_apply_apply, C(cont)->val[0] = func;
expr = cdr(expr);
return ACTION_APPLY_CONT;
}
}
}
pop_cont();
return ACTION_APPLY_CONT;
}
void BonminInterface::init(const Dict& opts) {
// Call the init method of the base class
Nlpsol::init(opts);
// Default options
pass_nonlinear_variables_ = true;
pass_nonlinear_constraints_ = true;
Dict hess_lag_options, jac_g_options, grad_f_options;
std::vector< std::vector<int> > sos1_groups;
std::vector< std::vector<double> > sos1_weights;
// Read user options
for (auto&& op : opts) {
if (op.first=="bonmin") {
opts_ = op.second;
} else if (op.first=="pass_nonlinear_variables") {
pass_nonlinear_variables_ = op.second;
} else if (op.first=="pass_nonlinear_constraints") {
pass_nonlinear_constraints_ = op.second;
} else if (op.first=="var_string_md") {
var_string_md_ = op.second;
} else if (op.first=="var_integer_md") {
var_integer_md_ = op.second;
} else if (op.first=="var_numeric_md") {
var_numeric_md_ = op.second;
} else if (op.first=="con_string_md") {
con_string_md_ = op.second;
} else if (op.first=="con_integer_md") {
con_integer_md_ = op.second;
} else if (op.first=="con_numeric_md") {
con_numeric_md_ = op.second;
} else if (op.first=="hess_lag_options") {
hess_lag_options = op.second;
} else if (op.first=="jac_g_options") {
jac_g_options = op.second;
} else if (op.first=="grad_f_options") {
grad_f_options = op.second;
} else if (op.first=="hess_lag") {
Function f = op.second;
casadi_assert_dev(f.n_in()==4);
casadi_assert_dev(f.n_out()==1);
set_function(f, "nlp_hess_l");
} else if (op.first=="jac_g") {
Function f = op.second;
casadi_assert_dev(f.n_in()==2);
casadi_assert_dev(f.n_out()==2);
set_function(f, "nlp_jac_g");
} else if (op.first=="grad_f") {
Function f = op.second;
casadi_assert_dev(f.n_in()==2);
casadi_assert_dev(f.n_out()==2);
set_function(f, "nlp_grad_f");
} else if (op.first=="sos1_groups") {
sos1_groups = to_int(op.second.to_int_vector_vector());
for (auto & g : sos1_groups) {
for (auto & e : g) e-= GlobalOptions::start_index;
}
} else if (op.first=="sos1_weights") {
sos1_weights = op.second.to_double_vector_vector();
} else if (op.first=="sos1_priorities") {
sos1_priorities_ = to_int(op.second.to_int_vector());
}
}
// Do we need second order derivatives?
exact_hessian_ = true;
auto hessian_approximation = opts_.find("hessian_approximation");
if (hessian_approximation!=opts_.end()) {
exact_hessian_ = hessian_approximation->second == "exact";
}
// Setup NLP functions
create_function("nlp_f", {"x", "p"}, {"f"});
create_function("nlp_g", {"x", "p"}, {"g"});
if (!has_function("nlp_grad_f")) {
create_function("nlp_grad_f", {"x", "p"}, {"f", "grad:f:x"});
}
if (!has_function("nlp_jac_g")) {
create_function("nlp_jac_g", {"x", "p"}, {"g", "jac:g:x"});
}
jacg_sp_ = get_function("nlp_jac_g").sparsity_out(1);
// By default, assume all nonlinear
nl_ex_.resize(nx_, true);
nl_g_.resize(ng_, true);
// Allocate temporary work vectors
if (exact_hessian_) {
if (!has_function("nlp_hess_l")) {
create_function("nlp_hess_l", {"x", "p", "lam:f", "lam:g"},
{"hess:gamma:x:x"}, {{"gamma", {"f", "g"}}});
}
hesslag_sp_ = get_function("nlp_hess_l").sparsity_out(0);
if (pass_nonlinear_variables_) {
const casadi_int* col = hesslag_sp_.colind();
for (casadi_int i=0;i<nx_;++i) nl_ex_[i] = col[i+1]-col[i];
}
} else {
if (pass_nonlinear_variables_)
nl_ex_ = oracle_.which_depends("x", {"f", "g"}, 2, false);
}
if (pass_nonlinear_constraints_)
nl_g_ = oracle_.which_depends("x", {"g"}, 2, true);
// Create sos info
// Declare size
sos_num_ = sos1_groups.size();
// sos1 type
sos1_types_.resize(sos_num_, 1);
casadi_assert(sos1_weights.empty() || sos1_weights.size()==sos_num_,
"sos1_weights has incorrect size");
casadi_assert(sos1_priorities_.empty() || sos1_priorities_.size()==sos_num_,
"sos1_priorities has incorrect size");
if (sos1_priorities_.empty()) sos1_priorities_.resize(sos_num_, 1);
sos_num_nz_ = 0;
for (casadi_int i=0;i<sos_num_;++i) {
// get local group
const std::vector<int>& sos1_group = sos1_groups[i];
// Get local weights
std::vector<double> default_weights(sos1_group.size(), 1.0);
const std::vector<double>& sos1_weight =
sos1_weights.empty() ? default_weights : sos1_weights[i];
casadi_assert(sos1_weight.size()==sos1_group.size(),
"sos1_weights has incorrect size");
// Populate lookup vector
sos1_starts_.push_back(sos_num_nz_);
sos_num_nz_+=sos1_group.size();
sos1_weights_.insert(sos1_weights_.end(), sos1_weight.begin(), sos1_weight.end());
sos1_indices_.insert(sos1_indices_.end(), sos1_group.begin(), sos1_group.end());
}
sos1_starts_.push_back(sos_num_nz_);
// Allocate work vectors
alloc_w(nx_, true); // xk_
alloc_w(nx_, true); // lam_xk_
alloc_w(ng_, true); // gk_
alloc_w(nx_, true); // grad_fk_
alloc_w(jacg_sp_.nnz(), true); // jac_gk_
if (exact_hessian_) {
alloc_w(hesslag_sp_.nnz(), true); // hess_lk_
}
}
void BonminInterface::init(const Dict& opts) {
// Call the init method of the base class
Nlpsol::init(opts);
// Default options
pass_nonlinear_variables_ = false;
Dict hess_lag_options, jac_g_options, grad_f_options;
// Read user options
for (auto&& op : opts) {
if (op.first=="bonmin") {
opts_ = op.second;
} else if (op.first=="pass_nonlinear_variables") {
pass_nonlinear_variables_ = op.second;
} else if (op.first=="var_string_md") {
var_string_md_ = op.second;
} else if (op.first=="var_integer_md") {
var_integer_md_ = op.second;
} else if (op.first=="var_numeric_md") {
var_numeric_md_ = op.second;
} else if (op.first=="con_string_md") {
con_string_md_ = op.second;
} else if (op.first=="con_integer_md") {
con_integer_md_ = op.second;
} else if (op.first=="con_numeric_md") {
con_numeric_md_ = op.second;
} else if (op.first=="hess_lag_options") {
hess_lag_options = op.second;
} else if (op.first=="jac_g_options") {
jac_g_options = op.second;
} else if (op.first=="grad_f_options") {
grad_f_options = op.second;
} else if (op.first=="hess_lag") {
Function f = op.second;
casadi_assert(f.n_in()==4);
casadi_assert(f.n_out()==1);
set_function(f, "nlp_hess_l");
} else if (op.first=="jac_g") {
Function f = op.second;
casadi_assert(f.n_in()==2);
casadi_assert(f.n_out()==2);
set_function(f, "nlp_jac_g");
} else if (op.first=="grad_f") {
Function f = op.second;
casadi_assert(f.n_in()==2);
casadi_assert(f.n_out()==2);
set_function(f, "nlp_grad_f");
}
}
// Do we need second order derivatives?
exact_hessian_ = true;
auto hessian_approximation = opts_.find("hessian_approximation");
if (hessian_approximation!=opts_.end()) {
exact_hessian_ = hessian_approximation->second == "exact";
}
// Setup NLP functions
create_function("nlp_f", {"x", "p"}, {"f"});
create_function("nlp_g", {"x", "p"}, {"g"});
if (!has_function("nlp_grad_f")) {
create_function("nlp_grad_f", {"x", "p"}, {"f", "grad:f:x"});
}
if (!has_function("nlp_jac_g")) {
create_function("nlp_jac_g", {"x", "p"}, {"g", "jac:g:x"});
}
jacg_sp_ = get_function("nlp_jac_g").sparsity_out(1);
// Allocate temporary work vectors
if (exact_hessian_) {
if (!has_function("nlp_hess_l")) {
create_function("nlp_hess_l", {"x", "p", "lam:f", "lam:g"},
{"hess:gamma:x:x"}, {{"gamma", {"f", "g"}}});
}
hesslag_sp_ = get_function("nlp_hess_l").sparsity_out(0);
} else if (pass_nonlinear_variables_) {
nl_ex_ = oracle_.which_depends("x", {"f", "g"}, 2, false);
}
// Allocate work vectors
alloc_w(nx_, true); // xk_
alloc_w(ng_, true); // lam_gk_
alloc_w(nx_, true); // lam_xk_
alloc_w(ng_, true); // gk_
alloc_w(nx_, true); // grad_fk_
alloc_w(jacg_sp_.nnz(), true); // jac_gk_
if (exact_hessian_) {
alloc_w(hesslag_sp_.nnz(), true); // hess_lk_
}
}
