SPMAT *comp_AAT(SPMAT *A)
#endif
{
SPMAT *AAT;
SPROW *r, *r2;
row_elt *elts, *elts2;
int i, idx, idx2, j, m, minim, n, num_scan, tmp1;
Real ip;
if ( ! A )
error(E_NULL,"comp_AAT");
m = A->m; n = A->n;
/* set up column access paths */
if ( ! A->flag_col )
sp_col_access(A);
AAT = sp_get(m,m,10);
for ( i = 0; i < m; i++ )
{
/* initialisation */
r = &(A->row[i]);
elts = r->elt;
/* set up scan lists for this row */
if ( r->len > scan_len )
set_scan(r->len);
for ( j = 0; j < r->len; j++ )
{
col_list[j] = elts[j].col;
scan_row[j] = elts[j].nxt_row;
scan_idx[j] = elts[j].nxt_idx;
}
num_scan = r->len;
/* scan down the rows for next non-zero not
associated with a diagonal entry */
for ( ; ; )
{
minim = m;
for ( idx = 0; idx < num_scan; idx++ )
{
tmp1 = scan_row[idx];
minim = ( tmp1 >= 0 && tmp1 < minim ) ? tmp1 : minim;
}
if ( minim >= m )
break;
r2 = &(A->row[minim]);
if ( minim > i )
{
ip = sprow_ip(r,r2,n);
sp_set_val(AAT,minim,i,ip);
sp_set_val(AAT,i,minim,ip);
}
/* update scan entries */
elts2 = r2->elt;
for ( idx = 0; idx < num_scan; idx++ )
{
if ( scan_row[idx] != minim || scan_idx[idx] < 0 )
continue;
idx2 = scan_idx[idx];
scan_row[idx] = elts2[idx2].nxt_row;
scan_idx[idx] = elts2[idx2].nxt_idx;
}
}
/* set the diagonal entry */
sp_set_val(AAT,i,i,sprow_sqr(r,n));
}
return AAT;
}
SPMAT *spCHsymb(SPMAT *A)
#endif
{
register int i;
int idx, k, m, minim, n, num_scan, diag_idx, tmp1;
SPROW *r_piv, *r_op;
row_elt *elt_piv, *elt_op, *old_elt;
if ( A == SMNULL )
error(E_NULL,"spCHsymb");
if ( A->m != A->n )
error(E_SQUARE,"spCHsymb");
/* set up access paths if not already done so */
if ( ! A->flag_col )
sp_col_access(A);
if ( ! A->flag_diag )
sp_diag_access(A);
/* printf("spCHsymb() -- checkpoint 1\n"); */
m = A->m; n = A->n;
for ( k = 0; k < m; k++ )
{
r_piv = &(A->row[k]);
if ( r_piv->len > scan_len )
set_scan(r_piv->len);
elt_piv = r_piv->elt;
diag_idx = sprow_idx2(r_piv,k,r_piv->diag);
if ( diag_idx < 0 )
error(E_POSDEF,"spCHsymb");
old_elt = &(elt_piv[diag_idx]);
for ( i = 0; i < r_piv->len; i++ )
{
if ( elt_piv[i].col > k )
break;
col_list[i] = elt_piv[i].col;
scan_row[i] = elt_piv[i].nxt_row;
scan_idx[i] = elt_piv[i].nxt_idx;
}
/* printf("spCHsymb() -- checkpoint 2\n"); */
num_scan = i; /* number of actual entries in scan_row etc. */
/* printf("num_scan = %d\n",num_scan); */
/* now set the k-th column of the Cholesky factors */
/* printf("k = %d\n",k); */
for ( ; ; ) /* forever do... */
{
/* printf("spCHsymb() -- checkpoint 3\n"); */
/* find next row where something (non-trivial) happens
i.e. find min(scan_row) */
minim = n;
for ( i = 0; i < num_scan; i++ )
{
tmp1 = scan_row[i];
/* printf("%d ",tmp1); */
minim = ( tmp1 >= 0 && tmp1 < minim ) ? tmp1 : minim;
}
if ( minim >= n )
break; /* nothing more to do for this column */
r_op = &(A->row[minim]);
elt_op = r_op->elt;
/* set next entry in column k of Cholesky factors */
idx = sprow_idx2(r_op,k,scan_idx[num_scan-1]);
if ( idx < 0 )
{ /* fill-in */
sp_set_val(A,minim,k,0.0);
/* in case a realloc() has occurred... */
elt_op = r_op->elt;
/* now set up column access path again */
idx = sprow_idx2(r_op,k,-(idx+2));
tmp1 = old_elt->nxt_row;
old_elt->nxt_row = minim;
r_op->elt[idx].nxt_row = tmp1;
tmp1 = old_elt->nxt_idx;
old_elt->nxt_idx = idx;
r_op->elt[idx].nxt_idx = tmp1;
}
/* printf("spCHsymb() -- checkpoint 4\n"); */
/* remember current element in column k for column chain */
idx = sprow_idx2(r_op,k,idx);
old_elt = &(r_op->elt[idx]);
/* update scan_row */
/* printf("spCHsymb() -- checkpoint 5\n"); */
/* printf("minim = %d\n",minim); */
for ( i = 0; i < num_scan; i++ )
{
if ( scan_row[i] != minim )
continue;
idx = sprow_idx2(r_op,col_list[i],scan_idx[i]);
if ( idx < 0 )
{ scan_row[i] = -1; continue; }
scan_row[i] = elt_op[idx].nxt_row;
scan_idx[i] = elt_op[idx].nxt_idx;
/* printf("scan_row[%d] = %d\n",i,scan_row[i]); */
/* printf("scan_idx[%d] = %d\n",i,scan_idx[i]); */
}
}
/* printf("spCHsymb() -- checkpoint 6\n"); */
}
return A;
}
//--------------------------------------------------------------------------
void Hqp_IpRedSpBKP::init(const Hqp_Program *qp)
{
IVEC *degree, *neigh_start, *neighs;
SPMAT *QCTC;
SPROW *r1, *r2;
int i, j;
int len, dim;
Real sum;
_n = qp->c->dim;
_me = qp->b->dim;
_m = qp->d->dim;
dim = _n + _me;
// reallocations
_pivot = px_resize(_pivot, dim);
_blocks = px_resize(_blocks, dim);
_zw = v_resize(_zw, _m);
_scale = v_resize(_scale, _n);
_r12 = v_resize(_r12, dim);
_xy = v_resize(_xy, dim);
// store C' for further computations
// analyze structure of C'*C
_CT = sp_transp(qp->C, _CT);
sp_ones(_CT);
v_ones(_zw);
QCTC = sp_get(_n, _n, 10);
r1 = _CT->row;
for (i=0; i<_n; i++, r1++) {
r2 = r1;
for (j=i; j<_n; j++, r2++) {
sum = sprow_inprod(r1, _zw, r2);
if (sum != 0.0) {
sp_set_val(QCTC, i, j, sum);
if (i != j)
sp_set_val(QCTC, j, i, sum);
}
}
}
_CTC_degree = iv_resize(_CTC_degree, _n);
_CTC_neigh_start = iv_resize(_CTC_neigh_start, _n + 1);
_CTC_neighs = sp_rcm_scan(QCTC, SMNULL, SMNULL,
_CTC_degree, _CTC_neigh_start, _CTC_neighs);
// initialize structure of reduced qp
QCTC = sp_add(qp->Q, QCTC, QCTC);
// determine RCM ordering
degree = iv_get(dim);
neigh_start = iv_get(dim + 1);
neighs = sp_rcm_scan(QCTC, qp->A, SMNULL, degree, neigh_start, IVNULL);
_QP2J = sp_rcm_order(degree, neigh_start, neighs, _QP2J);
_sbw = sp_rcm_sbw(neigh_start, neighs, _QP2J);
_J2QP = px_inv(_QP2J, _J2QP);
iv_free(degree);
iv_free(neigh_start);
iv_free(neighs);
len = 1 + (int)(log((double)dim) / log(2.0));
sp_free(_J);
sp_free(_J_raw);
_J_raw = sp_get(dim, dim, len);
_J = SMNULL;
// fill up data (to allocate _J_raw)
sp_into_symsp(QCTC, -1.0, _J_raw, _QP2J, 0, 0);
spT_into_symsp(qp->A, 1.0, _J_raw, _QP2J, 0, _n);
sp_into_symsp(qp->A, 1.0, _J_raw, _QP2J, _n, 0);
sp_free(QCTC);
// prepare iterations
update(qp);
}
SPMAT *spLUfactor(SPMAT *A, PERM *px, double alpha)
#endif
{
int i, best_i, k, idx, len, best_len, m, n;
SPROW *r, *r_piv, tmp_row;
STATIC SPROW *merge = (SPROW *)NULL;
Real max_val, tmp;
STATIC VEC *col_vals=VNULL;
if ( ! A || ! px )
error(E_NULL,"spLUfctr");
if ( alpha <= 0.0 || alpha > 1.0 )
error(E_RANGE,"alpha in spLUfctr");
if ( px->size <= A->m )
px = px_resize(px,A->m);
px_ident(px);
col_vals = v_resize(col_vals,A->m);
MEM_STAT_REG(col_vals,TYPE_VEC);
m = A->m; n = A->n;
if ( ! A->flag_col )
sp_col_access(A);
if ( ! A->flag_diag )
sp_diag_access(A);
A->flag_col = A->flag_diag = FALSE;
if ( ! merge ) {
merge = sprow_get(20);
MEM_STAT_REG(merge,TYPE_SPROW);
}
for ( k = 0; k < n; k++ )
{
/* find pivot row/element for partial pivoting */
/* get first row with a non-zero entry in the k-th column */
max_val = 0.0;
for ( i = k; i < m; i++ )
{
r = &(A->row[i]);
idx = sprow_idx(r,k);
if ( idx < 0 )
tmp = 0.0;
else
tmp = r->elt[idx].val;
if ( fabs(tmp) > max_val )
max_val = fabs(tmp);
col_vals->ve[i] = tmp;
}
if ( max_val == 0.0 )
continue;
best_len = n+1; /* only if no possibilities */
best_i = -1;
for ( i = k; i < m; i++ )
{
tmp = fabs(col_vals->ve[i]);
if ( tmp == 0.0 )
continue;
if ( tmp >= alpha*max_val )
{
r = &(A->row[i]);
idx = sprow_idx(r,k);
len = (r->len) - idx;
if ( len < best_len )
{
best_len = len;
best_i = i;
}
}
}
/* swap row #best_i with row #k */
MEM_COPY(&(A->row[best_i]),&tmp_row,sizeof(SPROW));
MEM_COPY(&(A->row[k]),&(A->row[best_i]),sizeof(SPROW));
MEM_COPY(&tmp_row,&(A->row[k]),sizeof(SPROW));
/* swap col_vals entries */
tmp = col_vals->ve[best_i];
col_vals->ve[best_i] = col_vals->ve[k];
col_vals->ve[k] = tmp;
px_transp(px,k,best_i);
r_piv = &(A->row[k]);
for ( i = k+1; i < n; i++ )
{
/* compute and set multiplier */
tmp = col_vals->ve[i]/col_vals->ve[k];
if ( tmp != 0.0 )
sp_set_val(A,i,k,tmp);
else
continue;
/* perform row operations */
merge->len = 0;
r = &(A->row[i]);
sprow_mltadd(r,r_piv,-tmp,k+1,merge,TYPE_SPROW);
idx = sprow_idx(r,k+1);
if ( idx < 0 )
idx = -(idx+2);
/* see if r needs expanding */
if ( r->maxlen < idx + merge->len )
sprow_xpd(r,idx+merge->len,TYPE_SPMAT);
r->len = idx+merge->len;
MEM_COPY((char *)(merge->elt),(char *)&(r->elt[idx]),
merge->len*sizeof(row_elt));
}
}
#ifdef THREADSAFE
sprow_free(merge); V_FREE(col_vals);
#endif
return A;
}
/*
* n_vars is the number of variables to be considered,
* d is the data array of variables d[0],...,d[n_vars-1],
* pred determines which estimate is required: BLUE, BLUP, or BLP
*/
void gls(DATA **d /* pointer to DATA array */,
int n_vars, /* length of DATA array (to consider) */
enum GLS_WHAT pred, /* what type of prediction is requested */
DPOINT *where, /* prediction location */
double *est /* output: array that holds the predicted values and variances */)
{
GLM *glm = NULL; /* to be copied to/from d */
static MAT *X0 = MNULL, *C0 = MNULL, *MSPE = MNULL, *CinvC0 = MNULL,
*Tmp1 = MNULL, *Tmp2 = MNULL, *Tmp3, *R = MNULL;
static VEC *blup = VNULL, *tmpa = VNULL, *tmpb = VNULL;
volatile unsigned int i, rows_C;
unsigned int j, k, l = 0, row, col, start_i, start_j, start_X, global;
VARIOGRAM *v = NULL;
static enum GLS_WHAT last_pred = GLS_INIT; /* the initial value */
double c_value, *X_ori;
if (d == NULL) { /* clean up */
if (X0 != MNULL) M_FREE(X0);
if (C0 != MNULL) M_FREE(C0);
if (MSPE != MNULL) M_FREE(MSPE);
if (CinvC0 != MNULL) M_FREE(CinvC0);
if (Tmp1 != MNULL) M_FREE(Tmp1);
if (Tmp2 != MNULL) M_FREE(Tmp2);
if (Tmp3 != MNULL) M_FREE(Tmp3);
if (R != MNULL) M_FREE(R);
if (blup != VNULL) V_FREE(blup);
if (tmpa != VNULL) V_FREE(tmpa);
if (tmpb != VNULL) V_FREE(tmpb);
last_pred = GLS_INIT;
return;
}
#ifndef HAVE_SPARSE
if (gl_sparse) {
pr_warning("sparse matrices not supported: compile with --with-sparse");
gl_sparse = 0;
}
#endif
if (DEBUG_COV) {
printlog("we're at %s X: %g Y: %g Z: %g\n",
IS_BLOCK(where) ? "block" : "point",
where->x, where->y, where->z);
}
if (pred != UPDATE) /* it right away: */
last_pred = pred;
assert(last_pred != GLS_INIT);
if (d[0]->glm == NULL) { /* allocate and initialize: */
glm = new_glm();
d[0]->glm = (void *) glm;
} else
glm = (GLM *) d[0]->glm;
glm->mu0 = v_resize(glm->mu0, n_vars);
MSPE = m_resize(MSPE, n_vars, n_vars);
if (pred == GLS_BLP || UPDATE_BLP) {
X_ori = where->X;
for (i = 0; i < n_vars; i++) { /* mu(0) */
glm->mu0->ve[i] = calc_mu(d[i], where);
blup = v_copy(glm->mu0, v_resize(blup, glm->mu0->dim));
where->X += d[i]->n_X; /* shift to next x0 entry */
}
where->X = X_ori; /* ... and set back */
for (i = 0; i < n_vars; i++) { /* Cij(0,0): */
for (j = 0; j <= i; j++) {
v = get_vgm(LTI(d[i]->id,d[j]->id));
MSPE->me[i][j] = MSPE->me[j][i] = COVARIANCE0(v, where, where, d[j]->pp_norm2);
}
}
fill_est(NULL, blup, MSPE, n_vars, est); /* in case of empty neighbourhood */
}
/* xxx */
/*
logprint_variogram(v, 1);
*/
/*
* selection dependent problem dimensions:
*/
for (i = rows_C = 0; i < n_vars; i++)
rows_C += d[i]->n_sel;
if (rows_C == 0) { /* empty selection list(s) */
if (pred == GLS_BLP || UPDATE_BLP)
debug_result(blup, MSPE, pred);
return;
}
for (i = 0, global = 1; i < n_vars && global; i++)
global = (d[i]->sel == d[i]->list && d[i]->n_list == d[i]->n_original);
/*
* global things: enter whenever (a) first time, (b) local selections or
* (c) the size of the problem grew since the last call (e.g. simulation)
*/
if ((glm->C == NULL && glm->spC == NULL) || !global || rows_C > glm->C->m) {
/*
* fill y:
*/
glm->y = get_y(d, glm->y, n_vars);
if (pred != UPDATE) {
if (! gl_sparse) {
glm->C = m_resize(glm->C, rows_C, rows_C);
m_zero(glm->C);
}
#ifdef HAVE_SPARSE
else {
if (glm->C == NULL) {
glm->spC = sp_get(rows_C, rows_C, gl_sparse);
/* d->spLLT = spLLT = sp_get(rows_C, rows_C, gl_sparse); */
} else {
glm->spC = sp_resize(glm->spC, rows_C, rows_C);
/* d->spLLT = spLLT = sp_resize(spLLT, rows_C, rows_C); */
}
sp_zero(glm->spC);
}
#endif
glm->X = get_X(d, glm->X, n_vars);
M_DEBUG(glm->X, "X");
glm->CinvX = m_resize(glm->CinvX, rows_C, glm->X->n);
glm->XCinvX = m_resize(glm->XCinvX, glm->X->n, glm->X->n);
glm->beta = v_resize(glm->beta, glm->X->n);
for (i = start_X = start_i = 0; i < n_vars; i++) { /* row var */
/* fill C, mu: */
for (j = start_j = 0; j <= i; j++) { /* col var */
v = get_vgm(LTI(d[i]->id,d[j]->id));
for (k = 0; k < d[i]->n_sel; k++) { /* rows */
row = start_i + k;
for (l = 0, col = start_j; col <= row && l < d[j]->n_sel; l++, col++) {
if (pred == GLS_BLUP)
c_value = GCV(v, d[i]->sel[k], d[j]->sel[l]);
else
c_value = COVARIANCE(v, d[i]->sel[k], d[j]->sel[l]);
/* on the diagonal, if necessary, add measurement error variance */
if (d[i]->colnvariance && i == j && k == l)
c_value += d[i]->sel[k]->variance;
if (! gl_sparse)
glm->C->me[row][col] = c_value;
#ifdef HAVE_SPARSE
else {
if (c_value != 0.0)
sp_set_val(glm->spC, row, col, c_value);
}
#endif
} /* for l */
} /* for k */
start_j += d[j]->n_sel;
} /* for j */
start_i += d[i]->n_sel;
if (d[i]->n_sel > 0)
start_X += d[i]->n_X - d[i]->n_merge;
} /* for i */
/*
if (d[0]->colnvmu)
glm->C = convert_vmuC(glm->C, d[0]);
*/
if (d[0]->variance_fn) {
glm->mu = get_mu(glm->mu, glm->y, d, n_vars);
convert_C(glm->C, glm->mu, d[0]->variance_fn);
}
if (DEBUG_COV && pred == GLS_BLUP)
printlog("[using generalized covariances: max_val - semivariance()]");
if (! gl_sparse) {
M_DEBUG(glm->C, "Covariances (x_i, x_j) matrix C (lower triangle only)");
}
#ifdef HAVE_SPARSE
else {
SM_DEBUG(glm->spC, "Covariances (x_i, x_j) sparse matrix C (lower triangle only)")
}
#endif
/* check for singular C: */
if (! gl_sparse && gl_cn_max > 0.0) {
for (i = 0; i < rows_C; i++) /* row */
for (j = i+1; j < rows_C; j++) /* col > row */
glm->C->me[i][j] = glm->C->me[j][i]; /* fill symmetric */
if (is_singular(glm->C, gl_cn_max)) {
pr_warning("Covariance matrix (nearly) singular at location [%g,%g,%g]: skipping...",
where->x, where->y, where->z);
m_free(glm->C); glm->C = MNULL; /* assure re-entrance if global */
return;
}
}
/*
* factorize C:
*/
if (! gl_sparse)
LDLfactor(glm->C);
#ifdef HAVE_SPARSE
else {
sp_compact(glm->spC, 0.0);
spCHfactor(glm->spC);
}
#endif
} /* if (pred != UPDATE) */
if (pred != GLS_BLP && !UPDATE_BLP) { /* C-1 X and X'C-1 X, beta */
/*
* calculate CinvX:
*/
tmpa = v_resize(tmpa, rows_C);
for (i = 0; i < glm->X->n; i++) {
tmpa = get_col(glm->X, i, tmpa);
if (! gl_sparse)
tmpb = LDLsolve(glm->C, tmpa, tmpb);
#ifdef HAVE_SPARSE
else
tmpb = spCHsolve(glm->spC, tmpa, tmpb);
#endif
set_col(glm->CinvX, i, tmpb);
}
/*
* calculate X'C-1 X:
*/
glm->XCinvX = mtrm_mlt(glm->X, glm->CinvX, glm->XCinvX); /* X'C-1 X */
M_DEBUG(glm->XCinvX, "X'C-1 X");
if (gl_cn_max > 0.0 && is_singular(glm->XCinvX, gl_cn_max)) {
pr_warning("X'C-1 X matrix (nearly) singular at location [%g,%g,%g]: skipping...",
where->x, where->y, where->z);
m_free(glm->C); glm->C = MNULL; /* assure re-entrance if global */
return;
}
m_inverse(glm->XCinvX, glm->XCinvX);
/*
* calculate beta:
*/
tmpa = vm_mlt(glm->CinvX, glm->y, tmpa); /* X'C-1 y */
glm->beta = vm_mlt(glm->XCinvX, tmpa, glm->beta); /* (X'C-1 X)-1 X'C-1 y */
V_DEBUG(glm->beta, "beta");
M_DEBUG(glm->XCinvX, "Cov(beta), (X'C-1 X)-1");
M_DEBUG(R = get_corr_mat(glm->XCinvX, R), "Corr(beta)");
} /* if pred != GLS_BLP */
} /* if redo the heavy part */
//-------------------------------------------------------------------------
void Prg_ASCEND::setup()
{
int n, me, m;
int i, j, row_idx, idx;
SPMAT *J;
int nincidences;
const struct var_variable **incidences;
// obtain ASCEND system
// todo: should check that system can be solved with HQP (e.g. no integers)
_nvars = slv_get_num_solvers_vars(_slv_system);
_vars = slv_get_solvers_var_list(_slv_system);
_nrels = slv_get_num_solvers_rels(_slv_system);
_rels = slv_get_solvers_rel_list(_slv_system);
_obj = slv_get_obj_relation(_slv_system);
// count number of optimization variables and bounds
_var_lb = v_resize(_var_lb, _nvars);
_var_ub = v_resize(_var_ub, _nvars);
_var_asc2hqp = iv_resize(_var_asc2hqp, _nvars);
_derivatives = v_resize(_derivatives, _nvars);
_var_master_idxs = iv_resize(_var_master_idxs, _nvars);
_var_solver_idxs = iv_resize(_var_solver_idxs, _nvars);
n = 0;
me = 0;
m = 0;
for (i = 0; i < _nvars; i++) {
_var_lb[i] = var_lower_bound(_vars[i]);
_var_ub[i] = var_upper_bound(_vars[i]);
/*
var_write_name(_slv_system, _vars[i], stderr);
fprintf(stderr, ":\t%i,\t%g,\t%g\n", var_fixed(_vars[i]),
_var_lb[i], _var_ub[i]);
*/
if (var_fixed(_vars[i])) {
_var_asc2hqp[i] = -1;
}
else {
_var_asc2hqp[i] = n++;
if (_var_lb[i] == _var_ub[i])
++me;
else {
if (_var_lb[i] > -_Inf)
++m;
if (_var_ub[i] < _Inf)
++m;
}
}
}
// consider bounds as linear constraints (i.e. no Jacobian update)
_me_bounds = me;
_m_bounds = m;
// count number of HQP constraints
for (i = 0; i < _nrels; i++) {
if (rel_equal(_rels[i]))
++me; // equality constraint
else
++m; // inequality constraint
}
// allocate QP approximation and optimization variables vector
_qp->resize(n, me, m);
_x = v_resize(_x, n);
// allocate sparse structure for bounds
// (write constant elements in Jacobians)
me = m = 0;
for (i = 0; i < _nvars; i++) {
idx = _var_asc2hqp[i];
if (idx < 0)
continue;
if (_var_lb[i] == _var_ub[i]) {
row_idx = me++;
sp_set_val(_qp->A, row_idx, idx, 1.0);
}
else {
if (_var_lb[i] > -_Inf) {
row_idx = m++;
sp_set_val(_qp->C, row_idx, idx, 1.0);
}
if (_var_ub[i] < _Inf) {
row_idx = m++;
sp_set_val(_qp->C, row_idx, idx, -1.0);
}
}
}
// allocate sparse structure for general constraints
// (just insert dummy values; actual values are set in update method)
for (i = 0; i < _nrels; i++) {
if (rel_equal(_rels[i])) {
row_idx = me++;
J = _qp->A;
}
else {
row_idx = m++;
J = _qp->C;
}
nincidences = rel_n_incidences(_rels[i]);
incidences = rel_incidence_list(_rels[i]);
for (j = 0; j < nincidences; j++) {
idx = _var_asc2hqp[var_sindex(incidences[j])];
if (idx >= 0)
sp_set_val(J, row_idx, idx, 1.0);
}
}
// todo: setup sparse structure of Hessian
// for now initialize something resulting in dense BFGS update
for (j = 0; j < n-1; j++) {
sp_set_val(_qp->Q, j, j, 0.0);
sp_set_val(_qp->Q, j, j+1, 0.0);
}
sp_set_val(_qp->Q, j, j, 0.0);
}
