mat nnls_solver(const mat & H, mat mu, const umat & mask, int max_iter, double rel_tol, int n_threads)
{
/****************************************************************************************************
* Description: sequential Coordinate-wise algorithm for non-negative least square regression problem
* A x = b, s.t. x[!m] >= 0, x[m] == 0
* Arguments:
* H : A^T * A
* mu : -A^T * b
* mask : a mask matrix (m) of same dim of x
* max_iter : maximum number of iterations
* rel_tol : stop criterion, minimum change on x between two successive iteration
* n_threads : number of threads
* Return:
* x : solution to argmin_{x, x>=0} ||Ax - b||_F^2
* Reference:
* http://cmp.felk.cvut.cz/ftp/articles/franc/Franc-TR-2005-06.pdf
* Author:
* Eric Xihui Lin <*****@*****.**>
* Version:
* 2015-11-16
****************************************************************************************************/
mat x(H.n_cols, mu.n_cols, fill::zeros);
if (n_threads < 0) n_threads = 0;
bool is_masked = !mask.empty();
#pragma omp parallel for num_threads(n_threads) schedule(dynamic)
for (int j = 0; j < mu.n_cols; j++)
{
if (is_masked && arma::all(mask.col(j)))
continue;
vec x0(H.n_cols);
// x0.fill(-9999);
double tmp;
int i = 0;
double err1, err2 = 9999;
do {
// break if all entries of col_j are masked
x0 = x.col(j);
err1 = err2;
err2 = 0;
for (int k = 0; k < H.n_cols; k++)
{
if (is_masked && mask(k,j) > 0) continue;
tmp = x(k,j) - mu(k,j) / H(k,k);
if (tmp < 0) tmp = 0;
if (tmp != x(k,j))
{
mu.col(j) += (tmp - x(k,j)) * H.col(k);
}
x(k,j) = tmp;
tmp = std::abs(x(k,j) - x0(k));
if (tmp > err2) err2 = tmp;
}
} while(++i < max_iter && std::abs(err1 - err2) / (err1 + 1e-9) > rel_tol);
}
return x;
}
void phi_design(mat& U, const uvec& sti, const int L, umat& vxicat, cube& wZ, const uvec& WTIME){
int P=U.n_cols, wT = WTIME.n_elem, t;
int Psq=P*P;
wZ.zeros();
for(int wt=0; wt<wT; wt++){
t = WTIME[wt];
for(int l=1;l<=L;l++){
if(sti[t-l]){
for(int p=0;p<P;p++){
uvec cond= (vxicat.col(p) >= l);
wZ.slice(wt).col(p).subvec((l-1)*Psq+ p*P,(l-1)*Psq+(p+1)*P-1) = (cond)%trans(U.row(t-l));
}
wZ.slice(wt).rows((L+l-1)*Psq,(L+l)*Psq-1).zeros();
}else{
wZ.slice(wt).rows((l-1)*Psq,l*Psq-1).zeros();
for(int p=0;p<P;p++){
uvec cond= (vxicat.col(p) >= l);
wZ.slice(wt).col(p).subvec((L+l-1)*Psq+p*P,(L+l-1)*Psq+(p+1)*P-1)=cond%trans(U.row(t-l));
}
}
}
}
}
void assignWinners(mat bids, rowvec prices, umat & assignments) {
uword
winnerIdx = 0,
nItems = prices.size();
double winningBid;
for(int item = 0; item < nItems; item++) {
vec winner = getMaxItemBid(item, bids);
winnerIdx = winner(0);
winningBid = winner(1);
if(winningBid < 0.0)
continue;
prices(item) += winningBid;
assignments.col(item).fill(0);
assignments(winnerIdx, item) = 1;
}
}
mat nnls_solver_with_missing(const mat & A, const mat & W, const mat & W1, const mat & H2, const umat & mask,
const double & eta, const double & beta, int max_iter, double rel_tol, int n_threads)
{
// A = [W, W1, W2] [H, H1, H2]^T.
// Where A may have missing values
// Note that here in the input W = [W, W2]
// compute x = [H, H1]^T given W, W2
// A0 = W2*H2 is empty when H2 is empty (no partial info in H)
// Return: x = [H, H1]
int n = A.n_rows, m = A.n_cols;
int k = W.n_cols - H2.n_cols;
int kW = W1.n_cols;
int nH = k+kW;
mat x(nH, m, fill::zeros);
if (n_threads < 0) n_threads = 0;
bool is_masked = !mask.empty();
#pragma omp parallel for num_threads(n_threads) schedule(dynamic)
for (int j = 0; j < m; j++)
{
// break if all entries of col_j are masked
if (is_masked && arma::all(mask.col(j)))
continue;
uvec non_missing = find_finite(A.col(j));
mat WtW(nH, nH); // WtW
update_WtW(WtW, W.rows(non_missing), W1.rows(non_missing), H2);
if (beta > 0) WtW += beta;
if (eta > 0) WtW.diag() += eta;
mat mu(nH, 1); // -WtA
uvec jv(1);
jv(0) = j;
//non_missing.t().print("non_missing = ");
//std::cout << "1.1" << std::endl;
if (H2.empty())
update_WtA(mu, W.rows(non_missing), W1.rows(non_missing), H2, A.submat(non_missing, jv));
else
update_WtA(mu, W.rows(non_missing), W1.rows(non_missing), H2.rows(j, j), A.submat(non_missing, jv));
//std::cout << "1.5" << std::endl;
vec x0(nH);
double tmp;
int i = 0;
double err1, err2 = 9999;
do {
x0 = x.col(j);
err1 = err2;
err2 = 0;
for (int l = 0; l < nH; l++)
{
if (is_masked && mask(l,j) > 0) continue;
tmp = x(l,j) - mu(l,0) / WtW(l,l);
if (tmp < 0) tmp = 0;
if (tmp != x(l,j))
{
mu.col(0) += (tmp - x(l,j)) * WtW.col(l);
}
x(l,j) = tmp;
tmp = std::abs(x(l,j) - x0(l));
if (tmp > err2) err2 = tmp;
}
} while(++i < max_iter && std::abs(err1 - err2) / (err1 + 1e-9) > rel_tol);
}
return x;
}
