/*
* Compute the following entities:
* - responsibilities r(k|ij)
* - mu_ij_k
* - Lambda_ij_k
* - log determinante Lamda_ij_k
*
* And then use these to compute likelihood and gradients
*/
void Likelihood_Protein::compute_f_df(int hessian_pseudocount)
{
//initialize parameters for second gaussian
mu_ij_k.zeros(400, this->nr_components);
lambda_ij_k.zeros(400, 400, this->nr_components);
lambda_ij_k_inv.zeros(400, 400, this->nr_components);
log_det_lambda_ij_k.zeros(this->nr_components);
responsibilities.zeros(this->nr_components);
//initialize likelihood vector
log_likelihood.zeros(this->number_of_pairs);
//intialize gradients to zero
grad_weight.zeros(this->nr_components, 2);
grad_mu.zeros(400,this-> nr_components);
grad_precMat.zeros(400, this->nr_components);
for(int pair = 0; pair < this->number_of_pairs; pair++){
int i = this->i_indices(pair);
int j = this->j_indices(pair);
int lin_index = i*(L - (i+1)/2.0 - 1) + j - 1; //i*L - i*(i+1)/2 + j-(i+1);
int contact = this->protein_contacts(pair);
//for computation of likelihood
arma::vec log_density(this->nr_components, arma::fill::zeros);
arma::vec vqij = mq_ij.col(lin_index);
double N_ij = mN_ij(i,j);
arma::vec w_ij = w_ij3d.tube(i,j);
//diagonal matrix Qij = diag(q'ji)
//q'ijab = q(x_i=a, x_j=b) - (lambda_w * wijab / N_ij) --> has been precomputed
arma::mat Qij = arma::diagmat(vqij);
//outer product
arma::mat qij_prod = vqij * vqij.t();
//determine negative Hessian = Hij
arma::mat diff = Qij - qij_prod;
arma::mat H_ij = N_ij * diff + regularizer_w; //eq 37
// remove after debugging ---------------------------------------------------------------------------------
//debugging artefacts in learning
//add counts to Hessian to see if we have problems with non-informative data
H_ij.diag() += hessian_pseudocount;
//H_ij = arma::diagmat(H_ij);
// remove after debugging ---------------------------------------------------------------------------------
//precompute product H_ij * wij
arma::vec Hij_wij_prod = H_ij * w_ij;
for(int k = 0; k < this->nr_components; k++){
//gaussian parameters of coupling prior
double weight_k = this->parameters.get_weight(k, contact);
arma::vec mu_k = this->parameters.get_mean(k);
arma::mat lambda_k = this->parameters.get_precMat(k);
//---------------- simplify computation in case that lambda_k is diagonal matrix
// A, A_inv, lambda_k, Qij are diagonal matrices
arma::vec A = N_ij * vqij + lambda_k.diag(); //represents diagonal matrix
arma::vec A_inv = 1.0 / A; //represents diagonal matrix
double log_det_A = arma::sum(arma::log(A));
arma::vec Ainv_qij_product = A_inv % vqij; ////column vector
double triple_product = arma::sum(vqij % Ainv_qij_product);
//---------------- matrix computations in case lambda_k is NOT diagonal matrix
//A is a diagonal matrix, as Qij and lambda_k are diagonal matrices
//arma::mat A = N_ij * Qij + lambda_k; //diagonal
//arma::mat A_inv = arma::diagmat(1.0 / A.diag()); //diagonal
//double log_det_A = arma::sum(arma::log(A.diag()));
//arma::vec Ainv_qij_product = arma::vec(A_inv * vqij); //400x1 dim matrix
//double triple_product = arma::as_scalar(vqij.t() * Ainv_qij_product);
//compute lambda_ij_k_mat
arma::mat lambda_ij_k_mat = H_ij - regularizer_w + lambda_k;
lambda_ij_k.slice(k) = lambda_ij_k_mat;
//debugging: we assume diagonal Hessian ================================================================
// arma::mat lambda_ij_k_mat_inv(400,400,arma::fill::zeros);
// lambda_ij_k_mat_inv.diag() = 1.0 / lambda_ij_k_mat.diag();
//debugging=======================================================================================
//compute inverse of lambda_ij_k_mat
//---------------- simplify computation in case that lambda_k is diagonal matrix
arma::mat lambda_ij_k_mat_inv = arma::diagmat(A_inv) + (Ainv_qij_product * Ainv_qij_product.t()) / (1.0/N_ij - triple_product);
//---------------- matrix computations in case lambda_k is NOT diagonal matrix
//arma::mat lambda_ij_k_mat_inv = A_inv + (Ainv_qij_product * Ainv_qij_product.t()) / (1.0/N_ij - triple_product);
lambda_ij_k_inv.slice(k) = lambda_ij_k_mat_inv;
//compute mu_ij_k from precomputed entities
arma::vec mu_ij_k_vec = lambda_ij_k_mat_inv * ( Hij_wij_prod + lambda_k * mu_k);
mu_ij_k.col(k) = mu_ij_k_vec;
//debugging: we assume diagonal Hessian ================================================================
// log_det_lambda_ij_k(k) = arma::sum(arma::log(lambda_ij_k_mat.diag()));
//debugging=======================================================================================
//save log determinant of lambda_ij_k, see page 16 Saikats theory
log_det_lambda_ij_k(k) = log(1 - N_ij * triple_product) + log_det_A;
//ratio of two gaussians in log space
// N(0 | mu_k, lambda_k)
//------------------------------
// N(0 | mu_ij_k, lambda_ij,k)
double gaussian_ratio_logdensity = log_density_gaussian_ratio( mu_k,
mu_ij_k_vec,
lambda_k,
lambda_ij_k_mat,
this->parameters.get_log_det_inv_covMat(k),
log_det_lambda_ij_k(k) );
if(this->debug > 0 and gaussian_ratio_logdensity>1000){
std::cout << protein_id << " " << i << " " << j << " " << pair << " " << contact << " " << k << std::endl;
std::cout << "Gaussian log density > 1: " << gaussian_ratio_logdensity << std::endl;
std::cout << "A_inv.max() : " << A_inv.max() << std::endl;
arma::uword max_ind = index_max(A_inv);
std::cout << "A(max_ind) : " << A(max_ind) << std::endl;
std::cout << "vqij(max_ind): " << vqij(max_ind) << std::endl;
std::cout << "N_ij: " << N_ij << std::endl;
std::cout << "mu_ij_k_vec(max_ind) : " << mu_ij_k_vec(max_ind) << std::endl;
std::cout << "mu_k(max_ind) : " << mu_k(max_ind) << std::endl;
std::cout << "lambda_ij_k_mat(max_ind, max_ind) : " << lambda_ij_k_mat(max_ind, max_ind) << std::endl;
std::cout << "lambda_ij_k_mat_inv(max_ind, max_ind) : " << lambda_ij_k_mat_inv(max_ind, max_ind) << std::endl;
std::cout << "lambda_k(max_ind, max_ind) : " << lambda_k(max_ind, max_ind) << std::endl;
std::cout << "parameters.get_log_det_inv_covMat(k) : " << this->parameters.get_log_det_inv_covMat(k) << std::endl;
std::cout << "log_det_lambda_ij_k(k) : " << log_det_lambda_ij_k(k) << std::endl;
}
log_density(k) = log(weight_k) + gaussian_ratio_logdensity;
}//end loop over components k
//Johannes suggestion how to precisely compute the responsibilities
double a_max = arma::max(log_density);
this->responsibilities = arma::exp(log_density - a_max);//r_nk = exp( a_nk - a_max)
double sum_resp = arma::sum(this->responsibilities); //sum += r_nk
this->responsibilities /= sum_resp; //normalization of responsibilities, NOT in log space => r_nk /= sum;
//save neg likelihood of current pair
double f = log(sum_resp) + a_max;
if(! std::isnormal(f)) {
std::cout << "ERROR: likelihood cannot be computed for protein " << protein_id << ", i " << i << ", j " << j << " ("<< contact <<"): " << f << std::endl;
std::cout << "Nij: " << N_ij << ", sum_resp: " << sum_resp << ", a_max: " << a_max << std::endl;
for(int k = 0; k < this->nr_components; k++){
std::cout << "component: " << k << ", sum_precMat(k)diag: "<< arma::sum(this->parameters.get_precMat(k).diag()) << ", responsibilty:" << this->responsibilities(k) << ", log_density: " << log_density(k) << ", log_det_lambda_ij_k: " << log_det_lambda_ij_k(k) << std::endl;
}
continue;
} else log_likelihood(pair) = f;
// Compute ALL gradients for ALL components
for(int k = 0; k < this->nr_components; k++){
//weights (either for contact or non_contact)
grad_weight(k, contact) += gradient_weight_comp(k, contact);
//mu
grad_mu.col(k) += gradient_mu_comp(k);
//precMat - diagonal
arma::mat grad_precMat_protein = gradient_precisionMatrix_comp(k);
grad_precMat.col(k) += grad_precMat_protein.diag();
}//end components
}//end loop over ij pairs
}
double ChLcpInteriorPoint::Solve(
ChLcpSystemDescriptor& sysd ///< system description with constraints and variables
)
{
std::cout << "-------using interior point solver!!------" << std::endl;
std::vector<ChLcpConstraint*>& mconstraints = sysd.GetConstraintsList();
std::vector<ChLcpVariables*>& mvariables = sysd.GetVariablesList();
ChMatrixDynamic <double> mv0;
ChSparseMatrix mM;
ChSparseMatrix mCq;
ChSparseMatrix mE;
ChMatrixDynamic <double> mf;
ChMatrixDynamic <double> mb;
ChMatrixDynamic <double> mfric;
sysd.ConvertToMatrixForm(&mCq, &mM, &mE, &mf, &mb, &mfric);
sysd.FromVariablesToVector(mv0);
ChStreamOutAsciiFile file_V0( "dump_V_old.dat" ) ;
mv0.StreamOUTdenseMatlabFormat(file_V0) ;
ChStreamOutAsciiFile file_M ( "dump_M.dat" ) ;
mM.StreamOUTsparseMatlabFormat ( file_M ) ;
ChStreamOutAsciiFile file_Cq ( "dump_Cq.dat" ) ;
mCq.StreamOUTsparseMatlabFormat ( file_Cq ) ;
ChStreamOutAsciiFile file_E ( "dump_E.dat" ) ;
mE.StreamOUTsparseMatlabFormat ( file_E ) ;
ChStreamOutAsciiFile file_f ( "dump_f.dat" ) ;
mf.StreamOUTdenseMatlabFormat ( file_f ) ;
ChStreamOutAsciiFile file_b ( "dump_b.dat" ) ;
mb.StreamOUTdenseMatlabFormat ( file_b ) ;
ChStreamOutAsciiFile file_fric ( "dump_fric.dat" ) ;
mfric.StreamOUTdenseMatlabFormat ( file_fric ) ;
printf("Successfully writing chickenbutt files!\n");
/* file_f.GetFstream().close();
file_fric.GetFstream().close();
file_V0.GetFstream().close();
file_M.GetFstream().close();
file_Cq.GetFstream().close();
file_b.GetFstream().close();
*/
int nBodies = mM.GetColumns()/6;
size_t nVariables = mvariables.size();
size_t nConstraints = sysd.CountActiveConstraints();
int numContacts = nConstraints/3;
size_t nOfConstraints = mconstraints.size();
/* ALWYAS DO THIS IN THE LCP SOLVER!!!*/
for (unsigned int ic = 0; ic < nConstraints; ic++)
mconstraints[ic]->Update_auxiliary();
//Get sparse info for contact jacobian and Minv matrix to pass on to Ang's solver
std::vector<int> index_i_Cq;
std::vector<int> index_j_Cq;
std::vector<double> val_Cq;
double val;
// fprintf(stderr, "------------Cq(from C::E)----------\n");
for (int ii = 0; ii < mCq.GetRows(); ii++){
for (int jj = 0; jj < mCq.GetColumns(); jj++){
val = mCq.GetElement(ii,jj);
if (val){
index_i_Cq.push_back(jj);
index_j_Cq.push_back(ii);
val_Cq.push_back(val);
// fprintf(stderr, "%d %d %.20g\n", ii, jj, val);
}
}
}
/* for (int iv = 0; iv < mvariables.size(); iv++)
if (mvariables[iv]->IsActive())
mvariables[iv]->Compute_invMb_v(mvariables[iv]->Get_qb(), mvariables[iv]->Get_fb());
*/
// int count = 0;
// for (std::vector<int>::iterator it = index_i_Cq.begin(); it != index_i_Cq.end(); it ++){
// std::cout << "(" << index_i_Cq[count] <<"," << index_j_Cq[count] <<"):" << val_Cq[count] << std::endl;
// count ++;
// }
// Minv matrix
std::vector<int> index_i_Minv;
std::vector<int> index_j_Minv;
std::vector<double> val_Minv;
for (int i = 0; i < nBodies*6; i++){
index_i_Minv.push_back(i);
index_j_Minv.push_back(i);
val_Minv.push_back(1.0/mM.GetElement(i,i));
}
// create reference to pass on to SPIKE
int *Cq_i = &index_i_Cq[0];
int *Cq_j = &index_j_Cq[0];
int Cq_nnz = val_Cq.size();
double *Cq_val = &val_Cq[0];
int *Minv_i = &index_i_Minv[0];
int *Minv_j = &index_j_Minv[0];
double *Minv_val = &val_Minv[0];
// formulate rhs of optimization problem f(x) = 1/2 *x'*N*x + r'*x
ChMatrixDynamic <double> opt_r_tmp(nConstraints,1);
// assemble r vector
/** 1. put [M^-1]*k in q sparse vector of each variable **/
for (unsigned int iv = 0; iv < nVariables; iv ++)
if (mvariables[iv]->IsActive()){
mvariables[iv]->Compute_invMb_v(mvariables[iv]->Get_qb(), mvariables[iv]->Get_fb());
ChMatrix<double> k = mvariables[iv]->Get_fb();
ChMatrix<double> Mk = mvariables[iv]->Get_qb();
// fprintf(stderr, "Body %d k: %.12f %.12f %.12f\n", iv, k(0,0), k(1,0), k(2,0));
// fprintf(stderr, "Body %d M^[-1]*k: %.12f %.12f %.12f\n", iv, Mk(0,0), Mk(1,0), Mk(2,0));
}
/** 2. now do rhs = D'*q = D'*(M^-1)*k **/
int s_i = 0;
opt_r.Resize(nConstraints,1);
for (unsigned int ic = 0; ic < nConstraints; ic ++)
if (mconstraints[ic]->IsActive()){
opt_r(s_i,0) = mconstraints[ic]->Compute_Cq_q();
++s_i;
}
// fprintf(stderr, "------D'*M^(-1)*k-------\n");
// for (int i = 0; i < opt_r.GetRows(); i++)
// fprintf(stderr, "%.16f\n", opt_r(i,0));
/** 3. rhs = rhs + c **/
sysd.BuildBiVector(opt_r_tmp);
opt_r.MatrInc(opt_r_tmp);
// fprintf(stderr, "------opt_r-------\n");
// for (int i = 0; i < opt_r.GetRows(); i++)
// fprintf(stderr, "%.12f\n", opt_r(i,0));
///////////////////
//velocity update//
///////////////////
ChMatrixDynamic<> mq;
sysd.FromVariablesToVector(mq, true);
for (int i = 0; i < mq.GetRows(); i++){
// mq.SetElementN(i, mf.GetElementN(i)/mM.GetElement(i,i) + mv0.GetElementN(i));
// mq.SetElementN(i, mf.GetElementN(i)/mM.GetElement(i,i));
// fprintf(stderr, "%d: %g / %g + %g = %g\n",i, mf.GetElementN(i), mM.GetElement(i,i), mv0.GetElementN(i), mq.GetElementN(i));
// fprintf(stderr, "%g\n", mq.GetElementN(i));
}
////////////////////////////
//assign solver parameters//
////////////////////////////
double barrier_t = 1;
double eta_hat;
int numStages = 500;
int mu1 = 10;
double b1 = 0.5;
double a1 = 0.01;
// assign vectors here
ff.Resize(numContacts*2,1);
lambda_k.Resize(numContacts*2,1); /*initialize lambda_k*/
xk.Resize(numContacts*3,1);
r_dual.Resize(numContacts*3,1);
r_cent.Resize(numContacts*2,1);
d_x.Resize(numContacts*3,1);
d_lambda.Resize(numContacts*2,1);
Schur_rhs.Resize(3*numContacts,1);
grad_f.Resize(3*numContacts,1);
if (mconstraints.size() == 0){
sysd.FromVectorToConstraints(xk);
sysd.FromVectorToVariables(mq);
for (size_t ic = 0; ic < mconstraints.size(); ic ++){
if (mconstraints[ic]->IsActive())
mconstraints[ic]->Increment_q(mconstraints[ic]->Get_l_i());
}
return 1e-8;
}
double *BlockDiagonal_val = new double[9*numContacts];
int *BlockDiagonal_i = new int[9*numContacts];
int *BlockDiagonal_j = new int[9*numContacts];
double *spike_rhs = new double[3*numContacts];
int tmp0, tmp1, tmp2;
for (int i = 0; i < numContacts; i ++){
tmp0 = 3*i;
tmp1 = 3*i+1;
tmp2 = 3*i+2;
*(BlockDiagonal_i + 9*i) = tmp0;
*(BlockDiagonal_i + 9*i+1) = tmp0;
*(BlockDiagonal_i + 9*i+2) = tmp0;
*(BlockDiagonal_i + 9*i+3) = tmp1;
*(BlockDiagonal_i + 9*i+4) = tmp1;
*(BlockDiagonal_i + 9*i+5) = tmp1;
*(BlockDiagonal_i + 9*i+6) = tmp2;
*(BlockDiagonal_i + 9*i+7) = tmp2;
*(BlockDiagonal_i + 9*i+8) = tmp2;
*(BlockDiagonal_j + 9*i) = tmp0;
*(BlockDiagonal_j + 9*i+1) = tmp1;
*(BlockDiagonal_j + 9*i+2) = tmp2;
*(BlockDiagonal_j + 9*i+3) = tmp0;
*(BlockDiagonal_j + 9*i+4) = tmp1;
*(BlockDiagonal_j + 9*i+5) = tmp2;
*(BlockDiagonal_j + 9*i+6) = tmp0;
*(BlockDiagonal_j + 9*i+7) = tmp1;
*(BlockDiagonal_j + 9*i+8) = tmp2;
}
// initialize xk
for (int i = 0; i < numContacts; i ++){
xk(3*i, 0) = 1;
xk(3*i+1, 0) = 0;
xk(3*i+2, 0) = 0;
}
evaluateConstraints(mfric.GetAddress(), numContacts, false);
//initialize lambda
for (int i = 0; i < lambda_k.GetRows(); i++)
lambda_k(i,0) = -1/(barrier_t * ff(i,0));
/////////////////////////////
////GO THROUGH EACH STAGE////
/////////////////////////////
for (int stage = 0; stage < numStages; stage++){
eta_hat = - lambda_k.MatrDot(&lambda_k, &ff);
barrier_t = mu1 * (2*numContacts)/eta_hat;
// assemble grad_f = N*x + r
sysd.ShurComplementProduct(grad_f, &xk, 0);
// fprintf(stderr, "----------N*x----------\n");
// for (int i = 0; i < grad_f.GetRows(); i++)
// fprintf(stderr, "%.20f\n", grad_f.GetElementN(i));
grad_f.MatrInc(opt_r);
// fprintf(stderr, "----------grad_f----------\n");
// for (int i = 0; i < grad_f.GetRows(); i++)
// fprintf(stderr, "%.20f\n", grad_f.GetElementN(i));
// compute r_d and r_c for schur implementation
computeSchurRHS(grad_f.GetAddress(), mfric.GetAddress(), numContacts, barrier_t);
// fprintf(stderr, "----------r_dual----------\n");
// for (int i = 0; i < r_dual.GetRows(); i++)
// fprintf(stderr, "%.16f\n", r_dual.GetElementN(i));
// fprintf(stderr, "----------r_cent----------\n");
// for (int i = 0; i < r_cent.GetRows(); i++)
// fprintf(stderr, "%.16f\n", r_cent.GetElementN(i));
// assemble block diagonal matrix
computeBlockDiagonal(BlockDiagonal_val, mfric.GetAddress(), numContacts, barrier_t);
// assemble rhs vector for spike solver
computeSpikeRHS(spike_rhs, mfric.GetAddress(), numContacts, barrier_t);
// fprintf(stderr, "----------spike_rhs----------\n");
// for (int i = 0; i < 3*numContacts; i++){
// fprintf(stderr, "%.16f\n", *(spike_rhs + i));
// }
double *spike_dx = new double [3*numContacts];
//call ang's solver here....
bool solveSuc = solveSPIKE(nBodies, numContacts, Cq_i, Cq_j, Cq_nnz, Cq_val, Minv_i, Minv_j, Minv_val, BlockDiagonal_i, BlockDiagonal_j, BlockDiagonal_val, spike_dx, spike_rhs);
if (solveSuc == false)
std::cerr << "Solve Failed!" << std::endl;
// assume d_x is calculated perfectly!
for (int i = 0; i < numContacts; i++){
d_x(3*i,0) = *(spike_dx + 3*i);
d_x(3*i+1,0) = *(spike_dx + 3*i + 1);
d_x(3*i+2,0) = *(spike_dx + 3*i + 2);
}
/* fprintf(stderr, "-------d_x---------\n");
for (int i = 0; i < d_x.GetRows(); i++){
fprintf(stderr, "%.20f\n", d_x(i,0));
}
*/
// free the heap!
delete [] spike_dx;
// evaluate d_lambda
for (int i = 0; i < numContacts; i++){
d_lambda(i) = lambda_k(i,0)/ff(i,0) * (pow(mfric(3*i,0),2)*xk(3*i,0)*d_x(3*i,0) - xk(3*i+1,0)*d_x(3*i+1,0) -xk(3*i+2,0)*d_x(3*i+2,0) - r_cent(i,0) );
d_lambda(i + numContacts) = lambda_k(i+numContacts,0)/ff(i+numContacts)*(d_x(3*i) - r_cent(i + numContacts));
}
/* fprintf(stderr, "----------d_lambda----------\n");
for (int i = 0; i < 2*numContacts; i++){
fprintf(stderr, "%.16f\n", d_lambda(i,0));
}
*/
///////////////
//LINE SEARCH//
///////////////
double s_max = 1;
double tmp;
for (int i = 0; i < 2*numContacts; i ++){
if (d_lambda(i,0) < 0){
tmp = -lambda_k(i,0)/d_lambda(i,0);
// fprintf(stderr, "i = %d, tmp = %.20f\n", i, tmp);
if (tmp < s_max){
s_max = tmp;
}
}
}
double bla = 0.99;
double ss = bla * s_max;
// fprintf(stderr, "s_max = %.20g\n", s_max);
ff_tmp.Resize(2*numContacts,1);
lambda_k_tmp.Resize(2*numContacts,1);
xk_tmp.Resize(3*numContacts,1);;
r_dual_tmp.Resize(3*numContacts,1);;
r_cent_tmp.Resize(3*numContacts,1);;
bool DO = true;
int count = 0;
// fprintf(stderr, "----line search----\n");
while (DO){
xk_tmp = d_x;
// fprintf(stderr, "-----d_x----\n");
// for (int i = 0; i < 3*numContacts; i ++)
// fprintf(stderr, "%.20g\n", xk_tmp(i,0));
xk_tmp.MatrScale(ss);
// fprintf(stderr, "-----ss*d_x----\n");
// for (int i = 0; i < 3*numContacts; i ++)
// fprintf(stderr, "%.20g\n", xk_tmp(i,0));
xk_tmp.MatrAdd(xk,xk_tmp);
// fprintf(stderr, "-----xk+ss*d_x----\n");
// for (int i = 0; i < 3*numContacts; i ++)
// fprintf(stderr, "%.20g\n", xk_tmp(i,0));
evaluateConstraints(mfric.GetAddress(), numContacts, true);
// fprintf(stderr, "-----tmp_ff----\n");
// for (int i = 0; i < 2*numContacts; i ++)
// fprintf(stderr, "%.20g\n", ff_tmp(i,0));
// fprintf(stderr, "max_ff = %.20g\n", ff_tmp.Max());
if (ff_tmp.Max()<0){
DO = false;
}
else{
count++;
ss = b1 * ss;
// fprintf(stderr,"ss[%d] = %.20g\n", count, ss);
}
}
DO = true;
double norm_r_t = sqrt(pow(r_dual.NormTwo(),2) + pow(r_cent.NormTwo(),2));
double norm_r_t_ss;
count = 0;
while (DO){
xk_tmp = d_x;
xk_tmp.MatrScale(ss);
xk_tmp.MatrAdd(xk,xk_tmp);
lambda_k_tmp = d_lambda;
lambda_k_tmp.MatrScale(ss);
lambda_k_tmp.MatrAdd(lambda_k, lambda_k_tmp);
evaluateConstraints(mfric.GetAddress(),numContacts,true);
sysd.ShurComplementProduct(grad_f, &xk_tmp, 0);
grad_f.MatrInc(opt_r);
computeSchurKKT(grad_f.GetAddress(), mfric.GetAddress(), numContacts, barrier_t, true);
norm_r_t_ss = sqrt(pow(r_dual_tmp.NormTwo(),2) + pow(r_cent_tmp.NormTwo(),2));
if (norm_r_t_ss < (1 - a1*ss)*norm_r_t)
DO = false;
else{
count ++;
ss = b1*ss;
// fprintf(stderr,"ss[%d] = %.20g\n", count, ss);
}
}
// upadate xk and lambda_k
d_x.MatrScale(ss);
// fprintf(stderr, "-------ss*d_x---------\n");
// for (int i = 0; i < d_x.GetRows(); i++)
// fprintf(stderr, "%.20f\n", d_x(i,0));
// fprintf(stderr, "----------xk = xk + ss*d_x--------\n");
xk.MatrInc(d_x);
// for (int i = 0; i < xk.GetRows(); i++)
// fprintf(stderr, "%.20f\n", xk(i,0));
d_lambda.MatrScale(ss);
lambda_k.MatrInc(d_lambda);
// fprintf(stderr, "-------lambda_k------\n");
// for (int i = 0; i < lambda_k.GetRows(); i++)
// fprintf(stderr, "%.20f\n", lambda_k(i,0));
sysd.ShurComplementProduct(grad_f, &xk, 0);
grad_f.MatrInc(opt_r);
evaluateConstraints(mfric.GetAddress(), numContacts, false);
computeSchurKKT(grad_f.GetAddress(), mfric.GetAddress(), numContacts, barrier_t, false);
// std::cout << "----r_dual-----" << std::endl;
// for (int i = 0; i < r_dual.GetRows(); i++)
// std::cout << r_dual(i,0) << std::endl;
// std::cout << "-----r_cent-----" << std::endl;
// for (int i = 0; i < r_cent.GetRows(); i++)
// std::cout << r_cent(i,0) << std::endl;
fprintf(stderr, "stage[%d], rd = %e, rg = %e, s = %f, t = %f\n", stage+1, r_dual.NormInf(), r_cent.NormInf(), ss, barrier_t);
if (r_cent.NormInf() < 1e-10 ||stage == (numStages - 1)){
fprintf(stderr, "solution found after %d stages!\n", stage+1);
// fprintf(stderr, "stage[%d], rd = %e, rg = %e, s = %f, t = %f\n", stage+1, r_dual.NormInf(), r_cent.NormInf(), ss, barrier_t);
delete [] BlockDiagonal_val;
delete [] BlockDiagonal_i;
delete [] BlockDiagonal_j;
delete [] spike_rhs;
/////////////////////////////////////////////
//set small-magnitude contact force to zero//
/////////////////////////////////////////////
// for (int i = 0; i < numContacts; i++){
// if (sqrt(pow(xk(3*i,0),2) + pow(xk(3*i+1,0),2) + pow(xk(3*i+2,0),2)) < 1e-6){
/// xk(3*i,0) = 0;
// xk(3*i+1,0) = 0;
// xk(3*i+2, 0) = 0;
// }
// }
sysd.FromVectorToConstraints(xk);
sysd.FromVectorToVariables(mq);
for (size_t ic = 0; ic < mconstraints.size(); ic ++){
if (mconstraints[ic]->IsActive())
mconstraints[ic]->Increment_q(mconstraints[ic]->Get_l_i());
}
// return r_cent.NormInf();
return 1e-8;
}
evaluateConstraints(mfric.GetAddress(), numContacts, false);
}
}
