Esempio n. 1
0
/**
 * Multiplica duas matrizes
 * @param  m1 [description]
 * @param  m2 [description]
 * @return    [description]
 */
SparseMatrix* multiply(SparseMatrix* m1, SparseMatrix* m2)
{
  SparseMatrix* result = newSparseMatrix();

  // Verifica se colunas1 == linhas2
  if (m1->_maximumX != m2->_maximumY)
  {
    printf("As matrizes a serem multiplicadas devem ter o mesmo número de colunas e linhas, respectivamente.\n");
    exit(1);
  }

  long x, y, k;

  // Quantidade de elementos de cada linha/coluna
  long max = m1->_maximumX;

  for (x = 1; x <= m1->_maximumY; x++)
  {
    for (y = 1; y <= m2->_maximumX; y++)
    {
      long cellValue = 0;

      // a11.b11 + a21.b12 + a31.b13
      for (k = 1; k <= max; k++)
      {
        cellValue += getCellValue(m1, k, x) * getCellValue(m2, y, k);
      }

      setCell(result, y, x, cellValue);
    }
  }

  return result;
}
Esempio n. 2
0
/**
 * Transpose a matrix; returns A = M'
 */
spmat* transposeSparseMatrix(spmat* M)
{	
	idxint j, i, k, q;    
	idxint* w = (idxint *)MALLOC(M->m*sizeof(idxint));
	spmat* A = newSparseMatrix(M->n, M->m, M->nnz);

	/* row count: how often does row k occur in M? */
	for( i=0; i < M->m; i++ ) { w[i] = 0; }
	for( k=0; k < M->nnz; k++ ) { w[M->ir[k]]++; }
	
	/* row pointers: cumulative sum of w gives A->jc */
	spla_cumsum(A->jc, w, M->m);

	/* now walk through M and copy data to right places and set row counter */
	for( j=0; j < M->n; j++ ){
		for( k = M->jc[j]; k < M->jc[j+1]; k++ ){
			q = w[M->ir[k]]++;
			A->ir[q] = j;
			A->pr[q] = M->pr[k];
		}
	}

	FREE(w);
	return A;
}
Esempio n. 3
0
/**
 * Subtrai duas matrizes, desde que com dimensões iguais
 * @param  m1 [description]
 * @param  m2 [description]
 * @return    [description]
 */
SparseMatrix* subtract(SparseMatrix* m1, SparseMatrix* m2)
{
  if (m1->_maximumX != m2->_maximumX || m1->_maximumY != m2->_maximumY)
  {
    printf("As matrizes a serem somadas devem ter o mesmo número de linhas e colunas.\n");
    exit(1);
  }

  SparseMatrix* matrix = newSparseMatrix();

  long x, y, diff;

  for (x = 1; x <= m1->_maximumX; x++)
  {
    for (y = 1; y <= m1->_maximumY; y++)
    {
      diff = getCellValue(m1, x, y) - getCellValue(m2, x, y);

      if (diff != 0)
      {
        setCell(matrix, x, y, diff);
      }
    }

  }

  return matrix;
}
Esempio n. 4
0
/*
 * Returns a copy of a sparse matrix A.
 */
spmat* copySparseMatrix(spmat* A)
{	
	idxint i;
	spmat* B = newSparseMatrix(A->m, A->n, A->nnz);

	/* copy over */
	for( i=0; i<=A->n; i++ ){ B->jc[i] = A->jc[i]; }
	for( i=0; i<A->nnz; i++ ){ B->ir[i] = A->ir[i]; }
	for( i=0; i<A->nnz; i++ ){ B->pr[i] = A->pr[i]; }

	return B;
}
Esempio n. 5
0
/**
 * Cria a transposta de uma matriz
 * @param  matrix [description]
 * @return        [description]
 */
SparseMatrix* transpose(SparseMatrix* matrix)
{
  SparseMatrix* m = newSparseMatrix();
  long x, y, cellValue;

  for (x = 1; x <= matrix->_maximumX; x++)
  {
    for (y = 1; y <= matrix->_maximumY; y++)
    {
      cellValue = getCellValue(matrix, x, y);

      if (cellValue != 0)
      {
        setCell(m, y, x, cellValue);
      }
    }
  }

  return m;
}
Esempio n. 6
0
/**
 * Multiplica uma matriz por um escalar
 * @param  matrix [description]
 * @param  scalar [description]
 * @return        [description]
 */
SparseMatrix* multiplyByScalar(SparseMatrix* matrix, long scalar)
{
  SparseMatrix* m = newSparseMatrix();
  long x, y, cellValue;

  for (x = 1; x <= matrix->_maximumX; x++)
  {
    for (y = 1; y <= matrix->_maximumY; y++)
    {
      cellValue = getCellValue(matrix, x, y);

      if (cellValue != 0)
      {
        cellValue *= scalar;
        setCell(m, x, y, cellValue);
      }
    }
  }

  return m;
}
Esempio n. 7
0
/*
 * Sets up all data structures needed.
 * Replace by codegen
 */
pwork* ECOS_setup(idxint n, idxint m, idxint p, idxint l, idxint ncones, idxint* q,
                   pfloat* Gpr, idxint* Gjc, idxint* Gir,
                   pfloat* Apr, idxint* Ajc, idxint* Air,
                   pfloat* c, pfloat* h, pfloat* b)
{
    idxint i, j, k, cidx, conesize, lnz, amd_result, nK, *Ljc, *Lir, *P, *Pinv, *Sign;
    pwork* mywork;
	double Control [AMD_CONTROL], Info [AMD_INFO];		
	pfloat rx, ry, rz, *Lpr;
	spmat *At, *Gt, *KU;

#if PROFILING > 0
	timer tsetup;
#endif

#if PROFILING > 1
	timer tcreatekkt;
	timer tmattranspose;
	timer tordering;
#endif

#if PROFILING > 0
	tic(&tsetup);
#endif
   
#if PRINTLEVEL > 2
	PRINTTEXT("\n");		
	PRINTTEXT("  *******************************************************************************\n");
	PRINTTEXT("  * ECOS: Embedded Conic Solver - Sparse Interior Point method for SOCPs        *\n");
	PRINTTEXT("  *                                                                             *\n");
	PRINTTEXT("  * NOTE: The solver is based on L. Vandenberghe's 'The CVXOPT linear and quad- *\n");
	PRINTTEXT("  *       ratic cone program solvers', March 20, 2010. Available online:        *\n");
	PRINTTEXT("  *       [http://abel.ee.ucla.edu/cvxopt/documentation/coneprog.pdf]           *\n");
	PRINTTEXT("  *                                                                             *\n");
	PRINTTEXT("  *       This code uses T.A. Davis' sparse LDL package and AMD code.           *\n");
	PRINTTEXT("  *       [http://www.cise.ufl.edu/research/sparse]                             *\n");
	PRINTTEXT("  *                                                                             *\n");
	PRINTTEXT("  *       Written during a summer visit at Stanford University with S. Boyd.    *\n");
	PRINTTEXT("  *                                                                             *\n");
	PRINTTEXT("  * (C) Alexander Domahidi, Automatic Control Laboratory, ETH Zurich, 2012-13.  *\n");
	PRINTTEXT("  *                     Email: [email protected]                      *\n");
	PRINTTEXT("  *******************************************************************************\n");
	PRINTTEXT("\n\n");
    PRINTTEXT("PROBLEM SUMMARY:\n");
    PRINTTEXT("    Primal variables (n): %d\n", (int)n);
	PRINTTEXT("Equality constraints (p): %d\n", (int)p);
	PRINTTEXT("     Conic variables (m): %d\n", (int)m);
	PRINTTEXT("- - - - - - - - - - - - - - -\n");
    PRINTTEXT("         Size of LP cone: %d\n", (int)l);
    PRINTTEXT("          Number of SOCs: %d\n", (int)ncones);
    for( i=0; i<ncones; i++ ){
        PRINTTEXT("    Size of SOC #%02d: %d\n", (int)(i+1), (int)q[i]);
    }
#endif
	
	/* get work data structure */
    mywork = (pwork *)MALLOC(sizeof(pwork));
#if PRINTLEVEL > 2
    PRINTTEXT("Memory allocated for WORK struct\n");
#endif

	/* dimensions */
	mywork->n = n;
	mywork->m = m;
	mywork->p = p;
    mywork->D = l + ncones;
#if PRINTLEVEL > 2
    PRINTTEXT("Set dimensions\n");
#endif

	/* variables */
    mywork->x = (pfloat *)MALLOC(n*sizeof(pfloat));
    mywork->y = (pfloat *)MALLOC(p*sizeof(pfloat));
    mywork->z = (pfloat *)MALLOC(m*sizeof(pfloat));
    mywork->s = (pfloat *)MALLOC(m*sizeof(pfloat));
  	mywork->lambda = (pfloat *)MALLOC(m*sizeof(pfloat));
	mywork->dsaff_by_W = (pfloat *)MALLOC(m*sizeof(pfloat));
    mywork->dsaff = (pfloat *)MALLOC(m*sizeof(pfloat));
    mywork->dzaff = (pfloat *)MALLOC(m*sizeof(pfloat));
    mywork->saff = (pfloat *)MALLOC(m*sizeof(pfloat));
    mywork->zaff = (pfloat *)MALLOC(m*sizeof(pfloat));
	mywork->W_times_dzaff = (pfloat *)MALLOC(m*sizeof(pfloat));
#if PRINTLEVEL > 2
    PRINTTEXT("Memory allocated for variables\n");
#endif
    
    /* best iterates so far */
    mywork->best_x = (pfloat *)MALLOC(n*sizeof(pfloat));
    mywork->best_y = (pfloat *)MALLOC(p*sizeof(pfloat));
    mywork->best_z = (pfloat *)MALLOC(m*sizeof(pfloat));
    mywork->best_s = (pfloat *)MALLOC(m*sizeof(pfloat));
    mywork->best_info = (stats *)MALLOC(sizeof(stats));

	/* cones */
	mywork->C = (cone *)MALLOC(sizeof(cone));
#if PRINTLEVEL > 2
    PRINTTEXT("Memory allocated for cone struct\n");
#endif

	/* LP cone */
	mywork->C->lpc = (lpcone *)MALLOC(sizeof(lpcone));
	mywork->C->lpc->p = l;
	if( l > 0 ){
		mywork->C->lpc->w = (pfloat *)MALLOC(l*sizeof(pfloat));
		mywork->C->lpc->v = (pfloat *)MALLOC(l*sizeof(pfloat));
		mywork->C->lpc->kkt_idx = (idxint *)MALLOC(l*sizeof(idxint));
#if PRINTLEVEL > 2
        PRINTTEXT("Memory allocated for LP cone\n");
#endif
	} else {
		mywork->C->lpc->w = NULL;
		mywork->C->lpc->v = NULL;
		mywork->C->lpc->kkt_idx = NULL;
#if PRINTLEVEL > 2
        PRINTTEXT("No LP cone present, pointers filled with NULL\n");
#endif
	}


	/* Second-order cones */
	mywork->C->soc = (socone *)MALLOC(ncones*sizeof(socone));
	mywork->C->nsoc = ncones;
    cidx = 0;
    for( i=0; i<ncones; i++ ){
        conesize = (idxint)q[i];
        mywork->C->soc[i].p = conesize;
        mywork->C->soc[i].a = 0;
		mywork->C->soc[i].eta = 0;
        mywork->C->soc[i].q = (pfloat *)MALLOC((conesize-1)*sizeof(pfloat));
		mywork->C->soc[i].skbar = (pfloat *)MALLOC((conesize)*sizeof(pfloat));
		mywork->C->soc[i].zkbar = (pfloat *)MALLOC((conesize)*sizeof(pfloat));
#if CONEMODE == 0
        mywork->C->soc[i].Didx = (idxint *)MALLOC((conesize)*sizeof(idxint));
#endif 
#if CONEMODE > 0
        mywork->C->soc[i].colstart = (idxint *)MALLOC((conesize)*sizeof(idxint));
#endif
        cidx += conesize;
    }
#if PRINTLEVEL > 2
    PRINTTEXT("Memory allocated for second-order cones\n");
#endif

	/* info struct */
    mywork->info = (stats *)MALLOC(sizeof(stats));
#if PROFILING > 1
	mywork->info->tfactor = 0;
	mywork->info->tkktsolve = 0;
    mywork->info->tfactor_t1 = 0;
    mywork->info->tfactor_t2 = 0;
#endif
#if PRINTLEVEL > 2
    PRINTTEXT("Memory allocated for info struct\n");
#endif

    
#if defined EQUILIBRATE && EQUILIBRATE > 0
    /* equilibration vector */
    mywork->xequil = (pfloat *)MALLOC(n*sizeof(pfloat));
    mywork->Aequil = (pfloat *)MALLOC(p*sizeof(pfloat));
    mywork->Gequil = (pfloat *)MALLOC(m*sizeof(pfloat));
    
#if PRINTLEVEL > 2
    PRINTTEXT("Memory allocated for equilibration vectors\n");
#endif
#endif

	/* settings */
	mywork->stgs = (settings *)MALLOC(sizeof(settings));
	mywork->stgs->maxit = MAXIT;
	mywork->stgs->gamma = GAMMA;	
	mywork->stgs->delta = DELTA;
    mywork->stgs->eps = EPS;
	mywork->stgs->nitref = NITREF;
	mywork->stgs->abstol = ABSTOL;	
	mywork->stgs->feastol = FEASTOL;
	mywork->stgs->reltol = RELTOL;
    mywork->stgs->abstol_inacc = ATOL_INACC;
	mywork->stgs->feastol_inacc = FTOL_INACC;
	mywork->stgs->reltol_inacc = RTOL_INACC;
    mywork->stgs->verbose = VERBOSE;
#if PRINTLEVEL > 2
    PRINTTEXT("Written settings\n");
#endif

    mywork->c = c;
    mywork->h = h;
    mywork->b = b;
#if PRINTLEVEL > 2
    PRINTTEXT("Hung pointers for c, h and b into WORK struct\n");
#endif

    /* Store problem data */
  if(Apr && Ajc && Air) {
    mywork->A = createSparseMatrix(p, n, Ajc[n], Ajc, Air, Apr);
  } else {
    mywork->A = NULL;
  }
  if (Gpr && Gjc && Gir) {
	  mywork->G = createSparseMatrix(m, n, Gjc[n], Gjc, Gir, Gpr);
  } else {
    /* create an empty sparse matrix */
	mywork->G = createSparseMatrix(m, n, 0, Gjc, Gir, Gpr);
  }

#if defined EQUILIBRATE && EQUILIBRATE > 0
    set_equilibration(mywork);
    #if PRINTLEVEL > 2
        PRINTTEXT("Done equilibrating\n");
    #endif
#endif

#if PROFILING > 1
	mywork->info->ttranspose = 0;
	tic(&tmattranspose);
#endif
  if(mywork->A)
	  At = transposeSparseMatrix(mywork->A);
  else
    At = NULL;
#if PROFILING > 1	
	mywork->info->ttranspose += toc(&tmattranspose);
#endif
#if PRINTLEVEL > 2
    PRINTTEXT("Transposed A\n");
#endif
    
    
#if PROFILING > 1	
	tic(&tmattranspose);
#endif
	Gt = transposeSparseMatrix(mywork->G);    	
#if PROFILING > 1	
	mywork->info->ttranspose += toc(&tmattranspose);
#endif
#if PRINTLEVEL > 2
    PRINTTEXT("Transposed G\n");
#endif
    


     
  
    /* set up KKT system */
#if PROFILING > 1
	tic(&tcreatekkt);
#endif
	createKKT_U(Gt, At, mywork->C, &Sign, &KU);
#if PROFILING > 1
	mywork->info->tkktcreate = toc(&tcreatekkt);
#endif
#if PRINTLEVEL > 2
    PRINTTEXT("Created upper part of KKT matrix K\n");
#endif
    
    
	/* 
     * Set up KKT system related data
     * (L comes later after symbolic factorization) 
     */
    nK = KU->n;
    
#if DEBUG > 0
    dumpSparseMatrix(KU, "KU0.txt");
#endif
#if PRINTLEVEL > 2
    PRINTTEXT("Dimension of KKT matrix: %d\n", (int)nK);
    PRINTTEXT("Non-zeros in KKT matrix: %d\n", (int)KU->nnz);
#endif
    
    
    
    /* allocate memory in KKT system */
	mywork->KKT = (kkt *)MALLOC(sizeof(kkt));
	mywork->KKT->D = (pfloat *)MALLOC(nK*sizeof(pfloat));
	mywork->KKT->Parent = (idxint *)MALLOC(nK*sizeof(idxint));
	mywork->KKT->Pinv = (idxint *)MALLOC(nK*sizeof(idxint));
	mywork->KKT->work1 = (pfloat *)MALLOC(nK*sizeof(pfloat));
	mywork->KKT->work2 = (pfloat *)MALLOC(nK*sizeof(pfloat));
    mywork->KKT->work3 = (pfloat *)MALLOC(nK*sizeof(pfloat));
    mywork->KKT->work4 = (pfloat *)MALLOC(nK*sizeof(pfloat));
    mywork->KKT->work5 = (pfloat *)MALLOC(nK*sizeof(pfloat));
    mywork->KKT->work6 = (pfloat *)MALLOC(nK*sizeof(pfloat));
	mywork->KKT->Flag = (idxint *)MALLOC(nK*sizeof(idxint));	
	mywork->KKT->Pattern = (idxint *)MALLOC(nK*sizeof(idxint));
	mywork->KKT->Lnz = (idxint *)MALLOC(nK*sizeof(idxint));	
	mywork->KKT->RHS1 = (pfloat *)MALLOC(nK*sizeof(pfloat));
	mywork->KKT->RHS2 = (pfloat *)MALLOC(nK*sizeof(pfloat));
	mywork->KKT->dx1 = (pfloat *)MALLOC(mywork->n*sizeof(pfloat));
	mywork->KKT->dx2 = (pfloat *)MALLOC(mywork->n*sizeof(pfloat));
	mywork->KKT->dy1 = (pfloat *)MALLOC(mywork->p*sizeof(pfloat));
	mywork->KKT->dy2 = (pfloat *)MALLOC(mywork->p*sizeof(pfloat));
	mywork->KKT->dz1 = (pfloat *)MALLOC(mywork->m*sizeof(pfloat));
	mywork->KKT->dz2 = (pfloat *)MALLOC(mywork->m*sizeof(pfloat));
    mywork->KKT->Sign = (idxint *)MALLOC(nK*sizeof(idxint));
    mywork->KKT->PKPt = newSparseMatrix(nK, nK, KU->nnz);
	mywork->KKT->PK = (idxint *)MALLOC(KU->nnz*sizeof(idxint));

#if PRINTLEVEL > 2
    PRINTTEXT("Created memory for KKT-related data\n");    
#endif
    
    
    /* calculate ordering of KKT matrix using AMD */
	P = (idxint *)MALLOC(nK*sizeof(idxint));
#if PROFILING > 1
	tic(&tordering);
#endif
	AMD_defaults(Control);	
	amd_result = AMD_order(nK, KU->jc, KU->ir, P, Control, Info);	
#if PROFILING > 1	
	mywork->info->torder = toc(&tordering);
#endif

	if( amd_result == AMD_OK ){
#if PRINTLEVEL > 2
		PRINTTEXT("AMD ordering successfully computed.\n");
		AMD_info(Info);
#endif
	} else {
#if PRINTLEVEL > 2
		PRINTTEXT("Problem in AMD ordering, exiting.\n");
        AMD_info(Info);
#endif
        return NULL;
	}
	
	/* calculate inverse permutation and permutation mapping of KKT matrix */
	pinv(nK, P, mywork->KKT->Pinv);		
	Pinv = mywork->KKT->Pinv;
#if DEBUG > 0
    dumpDenseMatrix_i(P, nK, 1, "P.txt");
    dumpDenseMatrix_i(mywork->KKT->Pinv, nK, 1, "PINV.txt");
#endif
	permuteSparseSymmetricMatrix(KU, mywork->KKT->Pinv, mywork->KKT->PKPt, mywork->KKT->PK);

	/* permute sign vector */
    for( i=0; i<nK; i++ ){ mywork->KKT->Sign[Pinv[i]] = Sign[i]; }
#if PRINTLEVEL > 3
    PRINTTEXT("P = [");
    for( i=0; i<nK; i++ ){ PRINTTEXT("%d ", (int)P[i]); }
    PRINTTEXT("];\n");
    PRINTTEXT("Pinv = [");
    for( i=0; i<nK; i++ ){ PRINTTEXT("%d ", (int)Pinv[i]); }
    PRINTTEXT("];\n");
    PRINTTEXT("Sign = [");
    for( i=0; i<nK; i++ ){ PRINTTEXT("%+d ", (int)Sign[i]); }
    PRINTTEXT("];\n");
    PRINTTEXT("SignP = [");
    for( i=0; i<nK; i++ ){ PRINTTEXT("%+d ", (int)mywork->KKT->Sign[i]); }
    PRINTTEXT("];\n");
#endif
	
    
	
	/* symbolic factorization */	
	Ljc = (idxint *)MALLOC((nK+1)*sizeof(idxint));
#if PRINTLEVEL > 2
    PRINTTEXT("Allocated memory for cholesky factor L\n");
#endif    
	LDL_symbolic2(
		mywork->KKT->PKPt->n,    /* A and L are n-by-n, where n >= 0 */
		mywork->KKT->PKPt->jc,   /* input of size n+1, not modified */
		mywork->KKT->PKPt->ir,	 /* input of size nz=Ap[n], not modified */
		Ljc,					 /* output of size n+1, not defined on input */
		mywork->KKT->Parent,	 /* output of size n, not defined on input */
		mywork->KKT->Lnz,		 /* output of size n, not defined on input */
		mywork->KKT->Flag		 /* workspace of size n, not defn. on input or output */
	);
	

	/* assign memory for L */
	lnz = Ljc[nK];
#if PRINTLEVEL > 2
	PRINTTEXT("Nonzeros in L, excluding diagonal: %d\n", (int)lnz) ;
#endif
	Lir = (idxint *)MALLOC(lnz*sizeof(idxint));
	Lpr = (pfloat *)MALLOC(lnz*sizeof(pfloat));
	mywork->KKT->L = createSparseMatrix(nK, nK, lnz, Ljc, Lir, Lpr);
#if PRINTLEVEL > 2
	PRINTTEXT("Created Cholesky factor of K in KKT struct\n");
#endif
    

	/* permute KKT matrix - we work on this one from now on */
	permuteSparseSymmetricMatrix(KU, mywork->KKT->Pinv, mywork->KKT->PKPt, NULL);
#if DEBUG > 0
    dumpSparseMatrix(mywork->KKT->PKPt, "PKPt.txt");
#endif
    
#if CONEMODE > 0
    /* zero any off-diagonal elements in (permuted) scalings in KKT matrix */
    for (i=0; i<mywork->C->nsoc; i++) {
        for (j=1; j<mywork->C->soc[i].p; j++) {
            for (k=0; k<j; k++) {
                mywork->KKT->PKPt->pr[mywork->KKT->PK[mywork->C->soc[i].colstart[j]+k]] = 0;
            }
        }
    }
#endif
#if DEBUG > 0
     dumpSparseMatrix(mywork->KKT->PKPt, "PKPt0.txt");
#endif

	/* set up RHSp for initialization */
	k = 0; j = 0;
	for( i=0; i<n; i++ ){ mywork->KKT->RHS1[Pinv[k++]] = 0; }
	for( i=0; i<p; i++ ){ mywork->KKT->RHS1[Pinv[k++]] = b[i]; }
	for( i=0; i<l; i++ ){ mywork->KKT->RHS1[Pinv[k++]] = h[i]; j++; }
	for( l=0; l<ncones; l++ ){ 
		for( i=0; i < mywork->C->soc[l].p; i++ ){ mywork->KKT->RHS1[Pinv[k++]] = h[j++]; }
#if CONEMODE == 0
		mywork->KKT->RHS1[Pinv[k++]] = 0;
        mywork->KKT->RHS1[Pinv[k++]] = 0;
#endif
	}
#if PRINTLEVEL > 2
    PRINTTEXT("Written %d entries of RHS1\n", (int)k);
#endif
	
	/* set up RHSd for initialization */
	for( i=0; i<n; i++ ){ mywork->KKT->RHS2[Pinv[i]] = -c[i]; }
	for( i=n; i<nK; i++ ){ mywork->KKT->RHS2[Pinv[i]] = 0; }

	/* get scalings of problem data */
	rx = norm2(c, n); mywork->resx0 = MAX(1, rx);
	ry = norm2(b, p); mywork->resy0 = MAX(1, ry);
	rz = norm2(h, m); mywork->resz0 = MAX(1, rz);

	/* get memory for residuals */
	mywork->rx = (pfloat *)MALLOC(n*sizeof(pfloat));
	mywork->ry = (pfloat *)MALLOC(p*sizeof(pfloat));
	mywork->rz = (pfloat *)MALLOC(m*sizeof(pfloat));
	
    /* clean up */
    mywork->KKT->P = P;
	FREE(Sign);
  if(At) freeSparseMatrix(At);
	freeSparseMatrix(Gt);
	freeSparseMatrix(KU);
    
#if PROFILING > 0
	mywork->info->tsetup = toc(&tsetup);
#endif

    return mywork;
}