void LrDpleToDple::init() {
// Initialize the base classes
LrDpleInternal::init();
MX As = MX::sym("As", input(LR_DPLE_A).sparsity());
MX Vs = MX::sym("Vs", input(LR_DPLE_V).sparsity());
MX Cs = MX::sym("Cs", input(LR_DPLE_C).sparsity());
MX Hs = MX::sym("Hs", input(LR_DPLE_H).sparsity());
int n_ = A_[0].size1();
// Chop-up the arguments
std::vector<MX> As_ = horzsplit(As, n_);
std::vector<MX> Vs_ = horzsplit(Vs, V_[0].size2());
std::vector<MX> Cs_ = horzsplit(Cs, V_[0].size2());
std::vector<MX> Hss_ = horzsplit(Hs, Hsi_);
std::vector<MX> V_(Vs_.size());
for (int k=0;k<V_.size();++k) {
V_[k] = mul(Cs_[k], mul(Vs_[k], Cs_[k].T()));
}
std::vector<Sparsity> Vsp(Vs_.size());
for (int k=0;k<V_.size();++k) {
Vsp[k] = V_[k].sparsity();
}
// Solver options
Dict options;
if (hasSetOption(optionsname())) {
options = getOption(optionsname());
}
// Create an dplesolver instance
std::map<std::string, std::vector<Sparsity> > tmp;
tmp["a"] = A_;
tmp["v"] = Vsp;
solver_ = DpleSolver("solver", getOption(solvername()), tmp, options);
MX P = solver_(make_map("a", horzcat(As_), "v", horzcat(V_))).at("p");
std::vector<MX> Ps_ = horzsplit(P, n_);
std::vector<MX> HPH(K_);
for (int k=0;k<K_;++k) {
std::vector<MX> hph = horzsplit(Hss_[k], cumsum0(Hs_[k]));
for (int kk=0;kk<hph.size();++kk) {
hph[kk] = mul(hph[kk].T(), mul(Ps_[k], hph[kk]));
}
HPH[k] = diagcat(hph);
}
f_ = MXFunction(name_, lrdpleIn("a", As, "v", Vs, "c", Cs, "h", Hs),
lrdpleOut("y", horzcat(HPH)));
Wrapper<LrDpleToDple>::checkDimensions();
}
void Diagsplit::evalAdj(const std::vector<pv_MX>& adjSeed, const std::vector<pv_MX>& adjSens) {
int nadj = adjSens.size();
int nx = offset_.size()-1;
// Get offsets
vector<int> offset1;
offset1.reserve(offset_.size());
offset1.push_back(0);
vector<int> offset2;
offset2.reserve(offset_.size());
offset2.push_back(0);
for (std::vector<Sparsity>::const_iterator it=output_sparsity_.begin();
it!=output_sparsity_.end();
++it) {
offset1.push_back(offset1.back() + it->size1());
offset2.push_back(offset2.back() + it->size2());
}
for (int d=0; d<nadj; ++d) {
if (adjSens[d][0]!=0) {
vector<MX> v;
for (int i=0; i<nx; ++i) {
MX* x_i = adjSeed[d][i];
if (x_i!=0) {
v.push_back(*x_i);
*x_i = MX();
} else {
v.push_back(MX(output_sparsity_[i].shape()));
}
}
adjSens[d][0]->addToSum(diagcat(v));
}
}
}
std::vector<Sparsity>
LrDpleInternal::getSparsity(const std::map<std::string, std::vector<Sparsity> >& st,
const std::vector< std::vector<int> > &Hs_) {
// Chop-up the arguments
std::vector<Sparsity> As, Vs, Cs, Hs;
if (st.count("a")) As = st.at("a");
if (st.count("v")) Vs = st.at("v");
if (st.count("c")) Cs = st.at("c");
if (st.count("h")) Hs = st.at("h");
bool with_H = !Hs.empty();
int K = As.size();
Sparsity A;
if (K==1) {
A = As[0];
} else {
Sparsity AL = diagcat(vector_slice(As, range(As.size()-1)));
Sparsity AL2 = horzcat(AL, Sparsity(AL.size1(), As[0].size2()));
Sparsity AT = horzcat(Sparsity(As[0].size1(), AL.size2()), As.back());
A = vertcat(AT, AL2);
}
Sparsity V = diagcat(Vs.back(), diagcat(vector_slice(Vs, range(Vs.size()-1))));
Sparsity C;
if (!Cs.empty()) {
C = diagcat(Cs.back(), diagcat(vector_slice(Cs, range(Cs.size()-1))));
}
Sparsity H;
std::vector<int> Hs_agg;
std::vector<int> Hi(1, 0);
if (Hs_.size()>0 && with_H) {
H = diagcat(Hs.back(), diagcat(vector_slice(Hs, range(Hs.size()-1))));
casadi_assert(K==Hs_.size());
for (int k=0;k<K;++k) {
Hs_agg.insert(Hs_agg.end(), Hs_[k].begin(), Hs_[k].end());
int sum=0;
for (int i=0;i<Hs_[k].size();++i) {
sum+= Hs_[k][i];
}
Hi.push_back(Hi.back()+sum);
}
}
Sparsity res = LrDleInternal::getSparsity(make_map("a", A, "v", V, "c", C, "h", H), Hs_agg);
if (with_H) {
return diagsplit(res, Hi);
} else {
return diagsplit(res, As[0].size2());
}
}
void Diagcat::evaluateMX(const MXPtrV& input, MXPtrV& output, const MXPtrVV& fwdSeed,
MXPtrVV& fwdSens, const MXPtrVV& adjSeed, MXPtrVV& adjSens,
bool output_given) {
int nfwd = fwdSens.size();
int nadj = adjSeed.size();
// Non-differentiated output
if (!output_given) {
*output[0] = diagcat(getVector(input));
}
// Forward sensitivities
for (int d = 0; d<nfwd; ++d) {
*fwdSens[d][0] = diagcat(getVector(fwdSeed[d]));
}
// Quick return?
if (nadj==0) return;
// Get offsets for each row and column
vector<int> offset1(ndep()+1, 0);
vector<int> offset2(ndep()+1, 0);
for (int i=0; i<ndep(); ++i) {
int ncol = dep(i).sparsity().size2();
int nrow = dep(i).sparsity().size1();
offset2[i+1] = offset2[i] + ncol;
offset1[i+1] = offset1[i] + nrow;
}
// Adjoint sensitivities
for (int d=0; d<nadj; ++d) {
MX& aseed = *adjSeed[d][0];
vector<MX> s = diagsplit(aseed, offset1, offset2);
aseed = MX();
for (int i=0; i<ndep(); ++i) {
adjSens[d][i]->addToSum(s[i]);
}
}
}
void FixedSmithLrDleInternal::init() {
iter_ = getOption("iter");
LrDleInternal::init();
casadi_assert_message(!pos_def_,
"pos_def option set to True: Solver only handles the indefinite case.");
MX H = MX::sym("H", H_);
MX A = MX::sym("A", A_);
MX C = MX::sym("C", C_);
MX V = MX::sym("V", V_);
MX Vs = (V+V.T())/2;
MX D = with_C_ ? C : DMatrix::eye(A_.size1());
std::vector<MX> HPH(Hs_.size(), 0);
std::vector<MX> Hs = with_H_? horzsplit(H, Hi_) : std::vector<MX>();
MX out = 0;
for (int i=0;i<iter_;++i) {
if (with_H_) {
for (int k=0;k<Hs.size();++k) {
MX v = mul(D.T(), Hs[k]);
HPH[k]+= mul(v.T(), mul(Vs, v));
}
} else {
out += mul(D, mul(Vs, D.T()));
}
D = mul(A, D);
}
std::vector<MX> dle_in(LR_DLE_NUM_IN);
dle_in[LR_DLE_A] = A;
dle_in[LR_DLE_V] = V;
if (with_C_) dle_in[LR_DLE_C] = C;
if (with_H_) dle_in[LR_DLE_H] = H;
f_ = MXFunction(dle_in, lrdleOut("y", with_H_? diagcat(HPH): out));
f_.init();
Wrapper<FixedSmithLrDleInternal>::checkDimensions();
}
void StabilizedQpToQp::init() {
// Initialize the base classes
StabilizedQpSolverInternal::init();
// Form augmented QP
Sparsity H_sparsity_qp = diagcat(st_[QP_STRUCT_H], Sparsity::diag(nc_));
Sparsity A_sparsity_qp = horzcat(st_[QP_STRUCT_A], Sparsity::diag(nc_));
std::string qp_solver_name = getOption("qp_solver");
qp_solver_ = QpSolver(qp_solver_name,
qpStruct("h", H_sparsity_qp, "a", A_sparsity_qp));
// Pass options if provided
if (hasSetOption("qp_solver_options")) {
Dictionary qp_solver_options = getOption("qp_solver_options");
qp_solver_.setOption(qp_solver_options);
}
// Initialize the QP solver
qp_solver_.init();
}
void Diagsplit::evalAdj(const std::vector<std::vector<MX> >& aseed,
std::vector<std::vector<MX> >& asens) {
int nadj = asens.size();
// Get offsets
vector<int> offset1;
offset1.reserve(offset_.size());
offset1.push_back(0);
vector<int> offset2;
offset2.reserve(offset_.size());
offset2.push_back(0);
for (std::vector<Sparsity>::const_iterator it=output_sparsity_.begin();
it!=output_sparsity_.end();
++it) {
offset1.push_back(offset1.back() + it->size1());
offset2.push_back(offset2.back() + it->size2());
}
for (int d=0; d<nadj; ++d) {
asens[d][0] += diagcat(aseed[d]);
}
}
void LiftingLrDpleInternal::init() {
form_ = getOptionEnumValue("form");
// Initialize the base classes
LrDpleInternal::init();
casadi_assert_message(!pos_def_,
"pos_def option set to True: Solver only handles the indefinite case.");
casadi_assert_message(const_dim_,
"const_dim option set to False: Solver only handles the True case.");
// We will construct an MXFunction to facilitate the calculation of derivatives
MX As = MX::sym("As", input(LR_DLE_A).sparsity());
MX Vs = MX::sym("Vs", input(LR_DLE_V).sparsity());
MX Cs = MX::sym("Cs", input(LR_DLE_C).sparsity());
MX Hs = MX::sym("Hs", input(LR_DLE_H).sparsity());
n_ = A_[0].size1();
// Chop-up the arguments
std::vector<MX> As_ = horzsplit(As, n_);
std::vector<MX> Vs_ = horzsplit(Vs, V_[0].size2());
std::vector<MX> Cs_ = horzsplit(Cs, V_[0].size2());
std::vector<MX> Hs_;
if (with_H_) {
Hs_ = horzsplit(Hs, Hsi_);
}
MX A;
if (K_==1) {
A = As;
} else {
if (form_==0) {
MX AL = diagcat(vector_slice(As_, range(As_.size()-1)));
MX AL2 = horzcat(AL, MX::sparse(AL.size1(), As_[0].size2()));
MX AT = horzcat(MX::sparse(As_[0].size1(), AL.size2()), As_.back());
A = vertcat(AT, AL2);
} else {
MX AL = diagcat(reverse(vector_slice(As_, range(As_.size()-1))));
MX AL2 = horzcat(MX::sparse(AL.size1(), As_[0].size2()), AL);
MX AT = horzcat(As_.back(), MX::sparse(As_[0].size1(), AL.size2()));
A = vertcat(AL2, AT);
}
}
MX V;
MX C;
MX H;
if (form_==0) {
V = diagcat(Vs_.back(), diagcat(vector_slice(Vs_, range(Vs_.size()-1))));
if (with_C_) {
C = diagcat(Cs_.back(), diagcat(vector_slice(Cs_, range(Cs_.size()-1))));
}
} else {
V = diagcat(diagcat(reverse(vector_slice(Vs_, range(Vs_.size()-1)))), Vs_.back());
if (with_C_) {
C = diagcat(diagcat(reverse(vector_slice(Cs_, range(Cs_.size()-1)))), Cs_.back());
}
}
if (with_H_) {
H = diagcat(form_==0? Hs_ : reverse(Hs_));
}
// Create an LrDleSolver instance
solver_ = LrDleSolver(getOption(solvername()),
lrdleStruct("a", A.sparsity(),
"v", V.sparsity(),
"c", C.sparsity(),
"h", H.sparsity()));
solver_.setOption("Hs", Hss_);
if (hasSetOption(optionsname())) solver_.setOption(getOption(optionsname()));
solver_.init();
std::vector<MX> v_in(LR_DPLE_NUM_IN);
v_in[LR_DLE_A] = As;
v_in[LR_DLE_V] = Vs;
if (with_C_) {
v_in[LR_DLE_C] = Cs;
}
if (with_H_) {
v_in[LR_DLE_H] = Hs;
}
std::vector<MX> Pr = solver_.call(lrdpleIn("a", A, "v", V, "c", C, "h", H));
MX Pf = Pr[0];
std::vector<MX> Ps = with_H_ ? diagsplit(Pf, Hsi_) : diagsplit(Pf, n_);
if (form_==1) {
Ps = reverse(Ps);
}
f_ = MXFunction(v_in, dpleOut("p", horzcat(Ps)));
f_.setInputScheme(SCHEME_LR_DPLEInput);
f_.setOutputScheme(SCHEME_LR_DPLEOutput);
f_.init();
Wrapper::checkDimensions();
}
void SimpleIndefDleInternal::init() {
DleInternal::init();
casadi_assert_message(!pos_def_,
"pos_def option set to True: Solver only handles the indefinite case.");
n_ = A_.size1();
MX As = MX::sym("A", A_);
MX Vs = MX::sym("V", V_);
MX Cs = MX::sym("C", C_);
MX Hs = MX::sym("H", H_);
MX Vss = (Vs+Vs.T())/2;
if (with_C_) Vss = mul(mul(Cs, Vss), Cs.T());
MX A_total = DMatrix::eye(n_*n_) - kron(As,As);
// Should be treated by solve node
MX Pf = solve(A_total, vec(Vss), getOption("linear_solver"));
std::vector<MX> v_in;
v_in.push_back(As);
v_in.push_back(Vs);
v_in.push_back(Cs);
MX P = reshape(Pf,n_,n_);
std::vector<MX> HPH;
if (with_H_) {
std::vector<MX> H = horzsplit(Hs,Hi_);
for (int k=0;k<H.size();++k) {
HPH.push_back(mul(H[k].T(),mul(P,H[k])));
}
}
std::vector<MX> dle_in(DLE_NUM_IN);
dle_in[DLE_A] = As;
dle_in[DLE_V] = Vs;
if (with_C_) dle_in[DLE_C] = Cs;
if (with_H_) dle_in[DLE_H] = Hs;
f_ = MXFunction(dle_in,dleOut("p",with_H_? diagcat(HPH) : P(output().sparsity())));
f_.init();
casadi_assert(nOut()==f_.nOut());
for (int i=0;i<nIn();++i) {
casadi_assert_message(input(i).sparsity()==f_.input(i).sparsity(),
"Sparsity mismatch for input " << i << ":" <<
input(i).dimString() << " <-> " << f_.input(i).dimString() << ".");
}
for (int i=0;i<nOut();++i) {
casadi_assert_message(output(i).sparsity()==f_.output(i).sparsity(),
"Sparsity mismatch for output " << i << ":" <<
output(i).dimString() << " <-> " << f_.output(i).dimString() << ".");
}
}
void SdqpToSdp::init() {
// Initialize the base classes
SdqpSolverInternal::init();
cholesky_ = LinearSolver("cholesky", "csparsecholesky", st_[SDQP_STRUCT_H]);
MX g_socp = MX::sym("x", cholesky_.getFactorizationSparsity(true));
MX h_socp = MX::sym("h", n_);
MX f_socp = sqrt(inner_prod(h_socp, h_socp));
MX en_socp = 0.5/f_socp;
MX f_sdqp = MX::sym("f", input(SDQP_SOLVER_F).sparsity());
MX g_sdqp = MX::sym("g", input(SDQP_SOLVER_G).sparsity());
std::vector<MX> fi(n_+1);
MX znp = MX(n_+1, n_+1);
for (int k=0;k<n_;++k) {
MX gk = vertcat(g_socp(ALL, k), MX(1, 1));
MX fk = -blockcat(znp, gk, gk.T(), MX(1, 1));
// TODO(Joel): replace with ALL
fi.push_back(diagcat(f_sdqp(ALL, Slice(f_sdqp.size1()*k, f_sdqp.size1()*(k+1))), fk));
}
MX fin = en_socp*DMatrix::eye(n_+2);
fin(n_, n_+1) = en_socp;
fin(n_+1, n_) = en_socp;
fi.push_back(diagcat(DMatrix(f_sdqp.size1(), f_sdqp.size1()), -fin));
MX h0 = vertcat(h_socp, DMatrix(1, 1));
MX g = blockcat(f_socp*DMatrix::eye(n_+1), h0, h0.T(), f_socp);
g = diagcat(g_sdqp, g);
Dict opts;
opts["input_scheme"] = IOScheme("g_socp", "h_socp", "f_sdqp", "g_sdqp");
opts["output_scheme"] = IOScheme("f", "g");
mapping_ = MXFunction("mapping", make_vector(g_socp, h_socp, f_sdqp, g_sdqp),
make_vector(horzcat(fi), g), opts);
Dict options;
if (hasSetOption(optionsname())) options = getOption(optionsname());
// Create an SdpSolver instance
solver_ = SdpSolver("sdpsolver", getOption(solvername()),
make_map("g", mapping_.output("g").sparsity(),
"f", mapping_.output("f").sparsity(),
"a", horzcat(input(SDQP_SOLVER_A).sparsity(),
Sparsity(nc_, 1))),
options);
solver_.input(SDP_SOLVER_C).at(n_)=1;
// Output arguments
obuf_.resize(SDQP_SOLVER_NUM_OUT);
output(SDQP_SOLVER_X) = DMatrix::zeros(n_, 1);
std::vector<int> r = range(input(SDQP_SOLVER_G).size1());
output(SDQP_SOLVER_P) = solver_.output(SDP_SOLVER_P).isempty() ? DMatrix() :
solver_.output(SDP_SOLVER_P)(r, r);
output(SDQP_SOLVER_DUAL) = solver_.output(SDP_SOLVER_DUAL).isempty() ? DMatrix() :
solver_.output(SDP_SOLVER_DUAL)(r, r);
output(SDQP_SOLVER_COST) = 0.0;
output(SDQP_SOLVER_DUAL_COST) = 0.0;
output(SDQP_SOLVER_LAM_X) = DMatrix::zeros(n_, 1);
output(SDQP_SOLVER_LAM_A) = DMatrix::zeros(nc_, 1);
}
void LrDpleInternal::init() {
const_dim_ = getOption("const_dim");
pos_def_ = getOption("pos_def");
error_unstable_ = getOption("error_unstable");
eps_unstable_ = getOption("eps_unstable");
Hs_ = getOption("Hs");
casadi_assert(const_dim_);
A_ = st_[LR_Dple_STRUCT_A];
casadi_assert(A_.size()>0);
int n = A_[0].size1();
V_ = st_[LR_Dple_STRUCT_V];
C_ = st_[LR_Dple_STRUCT_C];
H_ = st_[LR_Dple_STRUCT_H];
with_H_ = true;
if (H_.empty()) {
with_H_ = false;
}
with_C_ = true;
if (C_.empty()) {
with_C_ = false;
}
if (with_H_) {
casadi_assert_message(A_.size()==H_.size(),
"A and H arguments must be of same length, but got "
<< A_.size() << " and " << H_.size() << ".");
casadi_assert_message(H_.size()==Hs_.size(),
"H and Hs arguments must be of same length, but got "
<< H_.size() << " and " << Hs_.size() << ".");
Hsi_.resize(1, 0);
for (int k=0;k<H_.size();++k) {
// Default hs: [H.size2()]
if (Hs_[k].size()==0) {
Hs_[k].push_back(H_[k].size2());
}
// Assert that sum of Hs entries match up to H.size2()
double sum = 0;
for (int i=0;i<Hs_[k].size();++i) {
sum += Hs_[k][i];
}
Hsi_.push_back(Hsi_.back()+sum);
casadi_assert_message(H_[k].size2()==sum,
"Number of columns in H @ k = " << k << " (" << H_[k].size2() << "),"
<< "must match sum(Hs[k]): " << sum << ".");
}
}
// Dimension sanity checks
casadi_assert_message(A_.size()==V_.size(), "A and V arguments must be of same length, but got "
<< A_.size() << " and " << V_.size() << ".");
K_ = A_.size();
int m = with_C_? V_[0].size1(): n;
for (int k=0;k<K_;++k) {
casadi_assert_message(V_[k].issymmetric(), "V_i must be symmetric but got "
<< V_[k].dimString() << " for i = " << k << ".");
if (with_C_) {
casadi_assert_message(n==C_[k].size1(), "Number of rows in C ("
<< C_[k].size1() << ") must match dimension of square A @ k = "
<< k << " (" << n << ")" << ".");
casadi_assert_message(m==C_[k].size2(), "Number of columns in C ("
<< C_[k].size2() << ") must match dimension of symmetric V @ k = "
<< k << " (" << m << ")" << ".");
} else {
casadi_assert_message(A_[k].size1()==V_[k].size1(), "First dimension of A ("
<< A_[k].size1() << ") must match dimension of symmetric V @ k = "
<< k << " (" << V_[k].size1() << ")" << ".");
}
}
if (const_dim_) {
int n = A_[0].size1();
for (int k=1;k<K_;++k) {
casadi_assert_message(A_[k].size1()==n, "You have set const_dim option, but found "
"an A_i with dimension ( " << A_[k].dimString()
<< " ) deviating from n = " << n << " at i = " << k << ".");
}
}
Hss_.resize(0);
if (Hs_.size()>0) {
for (int k=0;k<K_;++k) {
Hss_.insert(Hss_.end(), Hs_[k].begin(), Hs_[k].end());
}
}
// Allocate inputs
ibuf_.resize(LR_DPLE_NUM_IN);
if (const_dim_) {
input(LR_DPLE_A) = DMatrix::zeros(horzcat(A_));
if (with_C_) {
input(LR_DPLE_C) = DMatrix::zeros(horzcat(C_));
}
input(LR_DPLE_V) = DMatrix::zeros(horzcat(V_));
if (with_H_) {
input(LR_DPLE_H) = DMatrix::zeros(horzcat(H_));
}
} else {
input(LR_DPLE_A) = DMatrix::zeros(diagcat(A_));
}
/**for (int i=0;i<nrhs_;++i) {
if (const_dim_) {
input(1+i) = DMatrix::zeros(horzcat(V_));
} else {
input(1+i) = DMatrix::zeros(diagcat(V_));
}
}*/
// Allocate outputs
std::map<std::string, std::vector<Sparsity> > tmp;
tmp["a"] = st_[LR_Dple_STRUCT_A];
tmp["v"] = st_[LR_Dple_STRUCT_V];
tmp["c"] = st_[LR_Dple_STRUCT_C];
tmp["h"] = st_[LR_Dple_STRUCT_H];
std::vector<Sparsity> P = getSparsity(tmp, Hs_);
obuf_.resize(nrhs_*LR_DPLE_NUM_OUT);
for (int i=0;i<nrhs_;++i) {
if (const_dim_) {
output(i) = DMatrix::zeros(horzcat(P));
} else {
output(i) = DMatrix::zeros(diagcat(P));
}
}
FunctionInternal::init();
}
void DpleInternal::init() {
const_dim_ = getOption("const_dim");
pos_def_ = getOption("pos_def");
error_unstable_ = getOption("error_unstable");
eps_unstable_ = getOption("eps_unstable");
A_ = st_[Dple_STRUCT_A];
V_ = st_[Dple_STRUCT_V];
// Dimension sanity checks
casadi_assert_message(A_.size()==V_.size(), "A and V arguments must be of same length, but got "
<< A_.size() << " and " << V_.size() << ".");
K_ = A_.size();
for (int k=0;k<K_;++k) {
casadi_assert_message(V_[k].issymmetric(), "V_i must be symmetric but got "
<< V_[k].dimString() << " for i = " << k << ".");
casadi_assert_message(A_[k].size1()==V_[k].size1(), "First dimension of A ("
<< A_[k].size1() << ") must match dimension of symmetric V_i ("
<< V_[k].size1() << ")" << " for i = " << k << ".");
}
if (const_dim_) {
int n = A_[0].size1();
for (int k=1;k<K_;++k) {
casadi_assert_message(A_[k].size1()==n, "You have set const_dim option, but found "
"an A_i with dimension ( " << A_[k].dimString()
<< " ) deviating from n = " << n << " at i = " << k << ".");
}
}
// Allocate inputs
ibuf_.resize(1+nrhs_);
if (const_dim_) {
input(0) = DMatrix::zeros(horzcat(A_));
} else {
input(0) = DMatrix::zeros(diagcat(A_));
}
for (int i=0;i<nrhs_;++i) {
if (const_dim_) {
input(1+i) = DMatrix::zeros(horzcat(V_));
} else {
input(1+i) = DMatrix::zeros(diagcat(V_));
}
}
// Allocate outputs
std::map<std::string, std::vector<Sparsity> > tmp;
tmp["a"] = A_;
tmp["v"] = V_;
std::vector<Sparsity> P = LrDpleInternal::getSparsity(tmp);
obuf_.resize(nrhs_);
for (int i=0;i<nrhs_;++i) {
if (const_dim_) {
output(i) = DMatrix::zeros(horzcat(P));
} else {
output(i) = DMatrix::zeros(diagcat(P));
}
}
FunctionInternal::init();
}