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sdpa_newton.cpp
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sdpa_newton.cpp
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/* -------------------------------------------------------------
This file is a component of SDPA
Copyright (C) 2004 SDPA Project
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program; if not, write to the Free Software
Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
------------------------------------------------------------- */
#include <sdpa_newton.h>
#include <sdpa_parts.h>
namespace sdpa {
Newton::Newton() {
useFormula = NULL;
bMat_type = DENSE;
// Caution: if SDPA doesn't use sparse bMat,
// following variables are indefinite.
this->SDP_nBlock = -1;
SDP_number = NULL;
SDP_location_sparse_bMat = NULL;
SDP_constraint1 = NULL;
SDP_constraint2 = NULL;
SDP_blockIndex1 = NULL;
SDP_blockIndex2 = NULL;
this->SOCP_nBlock = -1;
SOCP_number = NULL;
SOCP_location_sparse_bMat = NULL;
SOCP_constraint1 = NULL;
SOCP_constraint2 = NULL;
SOCP_blockIndex1 = NULL;
SOCP_blockIndex2 = NULL;
this->LP_nBlock = -1;
LP_number = NULL;
LP_location_sparse_bMat = NULL;
LP_constraint1 = NULL;
LP_constraint2 = NULL;
LP_blockIndex1 = NULL;
LP_blockIndex2 = NULL;
ordering = NULL;
reverse_ordering = NULL;
diagonalIndex = NULL;
}
Newton::Newton(int m, int SDP_nBlock, int *SDP_blockStruct, int SOCP_nBlock, int *SOCP_blockStruct, int LP_nBlock) { initialize(m, SDP_nBlock, SDP_blockStruct, SOCP_nBlock, SOCP_blockStruct, LP_nBlock); }
Newton::~Newton() { terminate(); }
void Newton::initialize(int m, int SDP_nBlock, int *SDP_blockStruct, int SOCP_nBlock, int *SOCP_blockStruct, int LP_nBlock) {
gVec.initialize(m);
DxMat.initialize(SDP_nBlock, SDP_blockStruct, SOCP_nBlock, SOCP_blockStruct, LP_nBlock);
DyVec.initialize(m);
DzMat.initialize(SDP_nBlock, SDP_blockStruct, SOCP_nBlock, SOCP_blockStruct, LP_nBlock);
r_zinvMat.initialize(SDP_nBlock, SDP_blockStruct, SOCP_nBlock, SOCP_blockStruct, LP_nBlock);
x_rd_zinvMat.initialize(SDP_nBlock, SDP_blockStruct, SOCP_nBlock, SOCP_blockStruct, LP_nBlock);
rNewCheck();
useFormula = new FormulaType[m * SDP_nBlock];
if (useFormula == NULL) {
rError("Newton:: memory exhausted ");
}
bMat_type = DENSE;
// Caution: if SDPA doesn't use sparse bMat,
// following variables are indefinite.
this->SDP_nBlock = -1;
SDP_number = NULL;
SDP_location_sparse_bMat = NULL;
SDP_constraint1 = NULL;
SDP_constraint2 = NULL;
SDP_blockIndex1 = NULL;
SDP_blockIndex2 = NULL;
this->SOCP_nBlock = -1;
SOCP_number = NULL;
SOCP_location_sparse_bMat = NULL;
SOCP_constraint1 = NULL;
SOCP_constraint2 = NULL;
SOCP_blockIndex1 = NULL;
SOCP_blockIndex2 = NULL;
this->LP_nBlock = -1;
LP_number = NULL;
LP_location_sparse_bMat = NULL;
LP_constraint1 = NULL;
LP_constraint2 = NULL;
LP_blockIndex1 = NULL;
LP_blockIndex2 = NULL;
ordering = NULL;
reverse_ordering = NULL;
diagonalIndex = NULL;
}
void Newton::terminate() {
if (bMat_type == SPARSE) {
if (SDP_location_sparse_bMat && SDP_constraint1 && SDP_constraint2 && SDP_blockIndex1 && SDP_blockIndex2) {
for (int k = 0; k < SDP_nBlock; ++k) {
delete[] SDP_location_sparse_bMat[k];
delete[] SDP_constraint1[k];
delete[] SDP_constraint2[k];
delete[] SDP_blockIndex1[k];
delete[] SDP_blockIndex2[k];
SDP_location_sparse_bMat[k] = NULL;
SDP_constraint1[k] = NULL;
SDP_constraint2[k] = NULL;
SDP_blockIndex1[k] = NULL;
SDP_blockIndex2[k] = NULL;
}
delete[] SDP_number;
delete[] SDP_location_sparse_bMat;
delete[] SDP_constraint1;
delete[] SDP_constraint2;
delete[] SDP_blockIndex1;
delete[] SDP_blockIndex2;
SDP_number = NULL;
SDP_location_sparse_bMat = NULL;
SDP_constraint1 = NULL;
SDP_constraint2 = NULL;
SDP_blockIndex1 = NULL;
SDP_blockIndex2 = NULL;
}
#if 0
if (SOCP_location_sparse_bMat && SOCP_constraint1 && SOCP_constraint2
&& SOCP_blockIndex1 && SOCP_blockIndex2) {
for (int k=0; k<SOCP_nBlock; ++k) {
delete[] SOCP_location_sparse_bMat[k];
delete[] SOCP_constraint1[k]; delete[] SOCP_constraint2[k];
delete[] SOCP_blockIndex1[k]; delete[] SOCP_blockIndex2[k];
SOCP_location_sparse_bMat[k] = NULL;
SOCP_constraint1[k] = NULL; SOCP_constraint2[k] = NULL;
SOCP_blockIndex1[k] = NULL; SOCP_blockIndex2[k] = NULL;
}
delete[] SOCP_number; delete[] SOCP_location_sparse_bMat;
delete[] SOCP_constraint1; delete[] SOCP_constraint2;
delete[] SOCP_blockIndex1; delete[] SOCP_blockIndex2;
SOCP_number = NULL; SOCP_location_sparse_bMat = NULL;
SOCP_constraint1 = NULL; SOCP_constraint2 = NULL;
SOCP_blockIndex1 = NULL; SOCP_blockIndex2 =NULL;
}
#endif
if (LP_location_sparse_bMat && LP_constraint1 && LP_constraint2 && LP_blockIndex1 && LP_blockIndex2) {
for (int k = 0; k < LP_nBlock; ++k) {
delete[] LP_location_sparse_bMat[k];
delete[] LP_constraint1[k];
delete[] LP_constraint2[k];
delete[] LP_blockIndex1[k];
delete[] LP_blockIndex2[k];
LP_location_sparse_bMat[k] = NULL;
LP_constraint1[k] = NULL;
LP_constraint2[k] = NULL;
LP_blockIndex1[k] = NULL;
LP_blockIndex2[k] = NULL;
}
delete[] LP_number;
delete[] LP_location_sparse_bMat;
delete[] LP_constraint1;
delete[] LP_constraint2;
delete[] LP_blockIndex1;
delete[] LP_blockIndex2;
LP_number = NULL;
LP_location_sparse_bMat = NULL;
LP_constraint1 = NULL;
LP_constraint2 = NULL;
LP_blockIndex1 = NULL;
LP_blockIndex2 = NULL;
}
if (ordering) {
delete[] ordering;
ordering = NULL;
}
if (reverse_ordering) {
delete[] reverse_ordering;
reverse_ordering = NULL;
}
if (diagonalIndex) {
delete[] diagonalIndex;
diagonalIndex = NULL;
}
sparse_bMat.terminate();
} else { // bMat_type == DENSE
bMat.terminate();
}
gVec.terminate();
DxMat.terminate();
DyVec.terminate();
DzMat.terminate();
r_zinvMat.terminate();
x_rd_zinvMat.terminate();
if (useFormula != NULL) {
delete[] useFormula;
}
useFormula = NULL;
}
void Newton::initialize_dense_bMat(int m) {
// bMat_type = DENSE;
// printf("DENSE computations\n");
bMat.initialize(m, m, DenseMatrix::DENSE);
}
// 2008/03/12 kazuhide nakata
void Newton::initialize_sparse_bMat(int m, IV *newToOldIV, IVL *symbfacIVL) {
// bMat_type = SPARSE;
// printf("SPARSE computation\n");
int i, j, k;
int *newToOld;
newToOld = IV_entries(newToOldIV);
rNewCheck();
ordering = new int[m];
if (ordering == NULL) {
rError("Newton::initialize_sparse_bMat memory exhausted ");
}
for (i = 0; i < m; i++) {
ordering[i] = newToOld[i];
}
rNewCheck();
reverse_ordering = new int[m];
if (reverse_ordering == NULL) {
rError("Newton::initialize_sparse_bMat memory exhausted ");
}
for (i = 0; i < m; i++) {
reverse_ordering[ordering[i]] = i;
}
// separate front or back node
int *counter;
int nClique = IVL_nlist(symbfacIVL);
int psize;
int *pivec;
bool *bnode;
int *nFront;
rNewCheck();
counter = new int[m];
bnode = new bool[m];
nFront = new int[nClique];
if ((counter == NULL) || (bnode == NULL) || (nFront == NULL)) {
rError("Newton::initialize_sparse_bMat memory exhausted ");
}
for (i = 0; i < m; i++) {
bnode[i] = false;
counter[i] = -1;
}
// search number of front
for (int l = nClique - 1; l >= 0; l--) {
IVL_listAndSize(symbfacIVL, l, &psize, &pivec);
for (i = 0; i < psize; i++) {
int ii = reverse_ordering[pivec[i]];
if (bnode[ii] == false) {
counter[ii] = psize - i;
bnode[ii] = true;
} else {
nFront[l] = i;
break;
}
}
if (i == psize) {
nFront[l] = psize;
}
}
// error check
for (i = 0; i < m; i++) {
if (counter[i] == -1) {
rError("Newton::initialize_sparse_bMat: program bug");
}
}
// make index of diagonal
rNewCheck();
diagonalIndex = new int[m + 1];
if (diagonalIndex == NULL) {
rError("Newton::initialize_sparse_bMat memory exhausted ");
}
diagonalIndex[0] = 0;
for (i = 1; i < m + 1; i++) {
diagonalIndex[i] = diagonalIndex[i - 1] + counter[i - 1];
}
// initialize sparse_bMat
sparse_bMat.initialize(m, m, SparseMatrix::SPARSE, diagonalIndex[m]);
// initialize index of sparse_bmat
int nonzeros = 0;
for (int l = 0; l < nClique; l++) {
IVL_listAndSize(symbfacIVL, l, &psize, &pivec);
for (i = 0; i < nFront[l]; i++) {
int ii = reverse_ordering[pivec[i]];
for (j = i; j < psize; j++) {
int jj = reverse_ordering[pivec[j]];
int index = diagonalIndex[ii] + j - i;
sparse_bMat.row_index[index] = ii;
sparse_bMat.column_index[index] = jj;
nonzeros++;
}
}
}
// error check
if (nonzeros != sparse_bMat.NonZeroNumber) {
rError("Newton::initialize_sparse_bMat probram bug");
}
sparse_bMat.NonZeroCount = nonzeros;
// sparse_bMat.display();
delete[] counter;
delete[] bnode;
delete[] nFront;
}
// 2008/03/12 kazuhide nakata
void Newton::initialize_bMat(int m, Chordal &chordal, InputData &inputData, FILE *fpOut) {
/* Create clique tree */
switch (chordal.best) {
case -1: {
bMat_type = DENSE;
printf("DENSE computations\n");
fprintf(fpOut, "DENSE computation\n");
initialize_dense_bMat(m);
break;
}
case 0: {
rError("no support for METIS");
break;
}
case 1: {
bMat_type = SPARSE;
printf("SPARSE computation\n");
fprintf(fpOut, "SPARSE computation\n");
initialize_sparse_bMat(m, chordal.newToOldIV_MMD, chordal.symbfacIVL_MMD);
make_aggrigateIndex(inputData);
break;
}
case 2: {
bMat_type = SPARSE;
printf("SPARSE computation\n");
fprintf(fpOut, "SPARSE computation\n");
initialize_sparse_bMat(m, chordal.newToOldIV_ND, chordal.symbfacIVL_ND);
make_aggrigateIndex(inputData);
break;
}
case 3: {
bMat_type = SPARSE;
printf("SPARSE computation\n");
fprintf(fpOut, "SPARSE computation\n");
initialize_sparse_bMat(m, chordal.newToOldIV_MS, chordal.symbfacIVL_MS);
make_aggrigateIndex(inputData);
break;
}
case 4: {
bMat_type = SPARSE;
printf("SPARSE computation\n");
fprintf(fpOut, "SPARSE computation\n");
initialize_sparse_bMat(m, chordal.newToOldIV_NDMS, chordal.symbfacIVL_NDMS);
make_aggrigateIndex(inputData);
break;
}
}
}
void Newton::make_aggrigateIndex_SDP(InputData &inputData) {
int t, ii, jj;
SDP_nBlock = inputData.SDP_nBlock;
rNewCheck();
SDP_number = new int[SDP_nBlock];
if (SDP_number == NULL) {
rError("Newton::make_aggrigateIndex_SDP memory exhausted ");
}
// memory allocate for aggrigateIndex
rNewCheck();
SDP_constraint1 = new int *[SDP_nBlock];
SDP_constraint2 = new int *[SDP_nBlock];
SDP_blockIndex1 = new int *[SDP_nBlock];
SDP_blockIndex2 = new int *[SDP_nBlock];
SDP_location_sparse_bMat = new int *[SDP_nBlock];
if ((SDP_constraint1 == NULL) || (SDP_constraint2 == NULL) || (SDP_blockIndex1 == NULL) || (SDP_blockIndex2 == NULL) || (SDP_location_sparse_bMat == NULL)) {
rError("Newton::make_aggrigateIndex_SDP memory exhausted ");
}
for (int l = 0; l < SDP_nBlock; l++) {
int tmp = (inputData.SDP_nConstraint[l] + 1) * inputData.SDP_nConstraint[l] / 2;
rNewCheck();
SDP_number[l] = tmp;
SDP_constraint1[l] = new int[tmp];
SDP_constraint2[l] = new int[tmp];
SDP_blockIndex1[l] = new int[tmp];
SDP_blockIndex2[l] = new int[tmp];
SDP_location_sparse_bMat[l] = new int[tmp];
if ((SDP_constraint1[l] == NULL) || (SDP_constraint2[l] == NULL) || (SDP_blockIndex1[l] == NULL) || (SDP_blockIndex2[l] == NULL) || (SDP_location_sparse_bMat[l] == NULL)) {
rError("Newton::make_aggrigateIndex_SDP memory exhausted ");
}
}
for (int l = 0; l < SDP_nBlock; l++) {
int NonZeroCount = 0;
for (int k1 = 0; k1 < inputData.SDP_nConstraint[l]; k1++) {
int i = inputData.SDP_constraint[l][k1];
int ib = inputData.SDP_blockIndex[l][k1];
int inz = inputData.A[i].SDP_sp_block[ib].NonZeroEffect;
for (int k2 = 0; k2 < inputData.SDP_nConstraint[l]; k2++) {
int j = inputData.SDP_constraint[l][k2];
int jb = inputData.SDP_blockIndex[l][k2];
int jnz = inputData.A[j].SDP_sp_block[jb].NonZeroEffect;
if ((inz < jnz) || ((inz == jnz) && (i < j))) {
continue;
}
// set index which A_i and A_j are not zero matrix
SDP_constraint1[l][NonZeroCount] = i;
SDP_constraint2[l][NonZeroCount] = j;
SDP_blockIndex1[l][NonZeroCount] = ib;
SDP_blockIndex2[l][NonZeroCount] = jb;
if (reverse_ordering[i] < reverse_ordering[j]) {
ii = reverse_ordering[i];
jj = reverse_ordering[j];
} else {
jj = reverse_ordering[i];
ii = reverse_ordering[j];
}
// binary search for index of sparse_bMat
t = -1;
int begin = diagonalIndex[ii];
int end = diagonalIndex[ii + 1] - 1;
int target = (begin + end) / 2;
while (end - begin > 1) {
if (sparse_bMat.column_index[target] < jj) {
begin = target;
target = (begin + end) / 2;
} else if (sparse_bMat.column_index[target] > jj) {
end = target;
target = (begin + end) / 2;
} else if (sparse_bMat.column_index[target] == jj) {
t = target;
break;
}
}
if (t == -1) {
if (sparse_bMat.column_index[begin] == jj) {
t = begin;
} else if (sparse_bMat.column_index[end] == jj) {
t = end;
} else {
rError("Newton::make_aggrigateIndex_SDP program bug");
}
}
SDP_location_sparse_bMat[l][NonZeroCount] = t;
NonZeroCount++;
}
} // for k1
} // for k kth block
}
void Newton::make_aggrigateIndex_SOCP(InputData &inputData) {
int t, ii, jj;
SOCP_nBlock = inputData.SOCP_nBlock;
rNewCheck();
SOCP_number = new int[SOCP_nBlock];
if (SOCP_number == NULL) {
rError("Newton::make_aggrigateIndex_SOCP memory exhausted ");
}
// memory allocate for aggrigateIndex
rNewCheck();
SOCP_constraint1 = new int *[SOCP_nBlock];
SOCP_constraint2 = new int *[SOCP_nBlock];
SOCP_blockIndex1 = new int *[SOCP_nBlock];
SOCP_blockIndex2 = new int *[SOCP_nBlock];
SOCP_location_sparse_bMat = new int *[SOCP_nBlock];
if ((SOCP_constraint1 == NULL) || (SOCP_constraint2 == NULL) || (SOCP_blockIndex1 == NULL) || (SOCP_blockIndex2 == NULL) || (SOCP_location_sparse_bMat == NULL)) {
rError("Newton::make_aggrigateIndex_SOCP memory exhausted ");
}
for (int l = 0; l < SOCP_nBlock; l++) {
int tmp = (inputData.SOCP_nConstraint[l] + 1) * inputData.SOCP_nConstraint[l] / 2;
rNewCheck();
SOCP_number[l] = tmp;
SOCP_constraint1[l] = new int[tmp];
SOCP_constraint2[l] = new int[tmp];
SOCP_blockIndex1[l] = new int[tmp];
SOCP_blockIndex2[l] = new int[tmp];
SOCP_location_sparse_bMat[l] = new int[tmp];
if ((SOCP_constraint1[l] == NULL) || (SOCP_constraint2[l] == NULL) || (SOCP_blockIndex1[l] == NULL) || (SOCP_blockIndex2[l] == NULL) || (SOCP_location_sparse_bMat[l] == NULL)) {
rError("Newton::make_aggrigateIndex_SOCP memory exhausted ");
}
}
for (int l = 0; l < SOCP_nBlock; l++) {
int NonZeroCount = 0;
for (int k1 = 0; k1 < inputData.SOCP_nConstraint[l]; k1++) {
int i = inputData.SOCP_constraint[l][k1];
int ib = inputData.SOCP_blockIndex[l][k1];
int inz = inputData.A[i].SOCP_sp_block[ib].NonZeroEffect;
for (int k2 = 0; k2 < inputData.SOCP_nConstraint[l]; k2++) {
int j = inputData.SOCP_constraint[l][k2];
int jb = inputData.SOCP_blockIndex[l][k2];
int jnz = inputData.A[j].SOCP_sp_block[jb].NonZeroEffect;
if ((inz < jnz) || ((inz == jnz) && (i < j))) {
continue;
}
// set index which A_i and A_j are not zero matrix
SOCP_constraint1[l][NonZeroCount] = i;
SOCP_constraint2[l][NonZeroCount] = j;
SOCP_blockIndex1[l][NonZeroCount] = ib;
SOCP_blockIndex2[l][NonZeroCount] = jb;
if (reverse_ordering[i] < reverse_ordering[j]) {
ii = reverse_ordering[i];
jj = reverse_ordering[j];
} else {
jj = reverse_ordering[i];
ii = reverse_ordering[j];
}
// binary search for index of sparse_bMat
t = -1;
int begin = diagonalIndex[ii];
int end = diagonalIndex[ii + 1] - 1;
int target = (begin + end) / 2;
while (end - begin > 1) {
if (sparse_bMat.column_index[target] < jj) {
begin = target;
target = (begin + end) / 2;
} else if (sparse_bMat.column_index[target] > jj) {
end = target;
target = (begin + end) / 2;
} else if (sparse_bMat.column_index[target] == jj) {
t = target;
break;
}
}
if (t == -1) {
if (sparse_bMat.column_index[begin] == jj) {
t = begin;
} else if (sparse_bMat.column_index[end] == jj) {
t = end;
} else {
rError("Newton::make_aggrigateIndex_SDP program bug");
}
}
SOCP_location_sparse_bMat[l][NonZeroCount] = t;
NonZeroCount++;
}
} // for k1
} // for k kth block
}
void Newton::make_aggrigateIndex_LP(InputData &inputData) {
int t, ii, jj;
LP_nBlock = inputData.LP_nBlock;
rNewCheck();
LP_number = new int[LP_nBlock];
if (LP_number == NULL) {
rError("Newton::make_aggrigateIndex_LP memory exhausted ");
}
// memory allocate for aggrigateIndex
rNewCheck();
LP_constraint1 = new int *[LP_nBlock];
LP_constraint2 = new int *[LP_nBlock];
LP_blockIndex1 = new int *[LP_nBlock];
LP_blockIndex2 = new int *[LP_nBlock];
LP_location_sparse_bMat = new int *[LP_nBlock];
if ((LP_constraint1 == NULL) || (LP_constraint2 == NULL) || (LP_blockIndex1 == NULL) || (LP_blockIndex2 == NULL) || (LP_location_sparse_bMat == NULL)) {
rError("Newton::make_aggrigateIndex_LP memory exhausted ");
}
for (int l = 0; l < LP_nBlock; l++) {
int tmp = (inputData.LP_nConstraint[l] + 1) * inputData.LP_nConstraint[l] / 2;
rNewCheck();
LP_number[l] = tmp;
LP_constraint1[l] = new int[tmp];
LP_constraint2[l] = new int[tmp];
LP_blockIndex1[l] = new int[tmp];
LP_blockIndex2[l] = new int[tmp];
LP_location_sparse_bMat[l] = new int[tmp];
if ((LP_constraint1[l] == NULL) || (LP_constraint2[l] == NULL) || (LP_blockIndex1[l] == NULL) || (LP_blockIndex2[l] == NULL) || (LP_location_sparse_bMat[l] == NULL)) {
rError("Newton::make_aggrigateIndex_LP memory exhausted ");
}
}
for (int l = 0; l < LP_nBlock; l++) {
int NonZeroCount = 0;
for (int k1 = 0; k1 < inputData.LP_nConstraint[l]; k1++) {
int i = inputData.LP_constraint[l][k1];
int ib = inputData.LP_blockIndex[l][k1];
for (int k2 = 0; k2 < inputData.LP_nConstraint[l]; k2++) {
int j = inputData.LP_constraint[l][k2];
int jb = inputData.LP_blockIndex[l][k2];
if (i < j) {
continue;
}
// set index which A_i and A_j are not zero matrix
LP_constraint1[l][NonZeroCount] = i;
LP_constraint2[l][NonZeroCount] = j;
LP_blockIndex1[l][NonZeroCount] = ib;
LP_blockIndex2[l][NonZeroCount] = jb;
if (reverse_ordering[i] < reverse_ordering[j]) {
ii = reverse_ordering[i];
jj = reverse_ordering[j];
} else {
jj = reverse_ordering[i];
ii = reverse_ordering[j];
}
// binary search for index of sparse_bMat
t = -1;
int begin = diagonalIndex[ii];
int end = diagonalIndex[ii + 1] - 1;
int target = (begin + end) / 2;
while (end - begin > 1) {
if (sparse_bMat.column_index[target] < jj) {
begin = target;
target = (begin + end) / 2;
} else if (sparse_bMat.column_index[target] > jj) {
end = target;
target = (begin + end) / 2;
} else if (sparse_bMat.column_index[target] == jj) {
t = target;
break;
}
}
if (t == -1) {
if (sparse_bMat.column_index[begin] == jj) {
t = begin;
} else if (sparse_bMat.column_index[end] == jj) {
t = end;
} else {
rError("Newton::make_aggrigateIndex_SDP program bug");
}
}
LP_location_sparse_bMat[l][NonZeroCount] = t;
NonZeroCount++;
}
} // for k1
} // for k kth block
}
void Newton::make_aggrigateIndex(InputData &inputData) {
make_aggrigateIndex_SDP(inputData);
// make_aggrigateIndex_SOCP(inputData);
make_aggrigateIndex_LP(inputData);
}
void Newton::computeFormula_SDP(InputData &inputData, mpf_class DenseRatio, mpf_class Kappa) {
int m = inputData.b.nDim;
int SDP_nBlock = inputData.SDP_nBlock;
int *upNonZeroCount;
rNewCheck();
upNonZeroCount = new int[m * SDP_nBlock];
if (upNonZeroCount == NULL) {
rError("Newton:: memory exhausted ");
}
// We have no chance to use DenseRatio
if (upNonZeroCount == NULL || useFormula == NULL) {
rError("Newton:: failed initialization");
}
SparseLinearSpace *A = inputData.A;
#if 0
for (int k=0; k<m; ++k) {
for (int l=0; l<inputData.A[0].nBlock; ++l) {
rMessage("A[" << k << "].ele[" << l << "] ="
<< inputData.A[k].ele[l].NonZeroEffect);
}
}
#endif
// Count sum of number of elements
// that each number of elements are less than own.
for (int iter = 0; iter < m * SDP_nBlock; iter++) {
upNonZeroCount[iter] = 0;
}
for (int l = 0; l < SDP_nBlock; ++l) {
for (int k1 = 0; k1 < inputData.SDP_nConstraint[l]; k1++) {
int i = inputData.SDP_constraint[l][k1];
int ib = inputData.SDP_blockIndex[l][k1];
int inz = A[i].SDP_sp_block[ib].NonZeroEffect;
int up = inz;
// rMessage("up = " << up);
for (int k2 = 0; k2 < inputData.SDP_nConstraint[l]; k2++) {
int j = inputData.SDP_constraint[l][k2];
int jb = inputData.SDP_blockIndex[l][k2];
int jnz = A[j].SDP_sp_block[jb].NonZeroEffect;
// printf("%d %d %d %d %d %d\n",i,ib,inz, j, jb,jnz);
if (jnz < inz) {
up += jnz;
}
#if 1
else if ((jnz == inz) && (j < i)) {
up += jnz;
}
#endif
}
upNonZeroCount[i * SDP_nBlock + l] = up;
// rMessage("up = " << up);
}
}
// Determine which formula
for (int l = 0; l < SDP_nBlock; ++l) {
int countf1, countf2, countf3;
countf1 = countf2 = countf3 = 0;
for (int k = 0; k < inputData.SDP_nConstraint[l]; k++) {
int i = inputData.SDP_constraint[l][k];
int ib = inputData.SDP_blockIndex[l][k];
mpf_class inz = inputData.A[i].SDP_sp_block[ib].NonZeroEffect;
mpf_class f1, f2, f3;
mpf_class n = inputData.A[i].SDP_sp_block[ib].nRow;
mpf_class up = upNonZeroCount[i * SDP_nBlock + l];
f1 = Kappa * n * inz + n * n * n + Kappa * up;
f2 = Kappa * n * inz + Kappa * (n + 1) * up;
#if 1
f3 = Kappa * (2 * Kappa * inz + 1) * up / Kappa;
#else
f3 = Kappa * (2 * Kappa * inz + 1) * up;
#endif
// rMessage("up = " << up << " nonzero = " << nonzero);
// rMessage("f1=" << f1 << " f2=" << f2 << " f3=" << f3);
// printf("%d %d %lf %lf %lf %lf\n",k,l,nonzero,f1,f2,f3);
if (inputData.A[i].SDP_sp_block[ib].type == SparseMatrix::DENSE) {
// if DENSE, we use only F1 or F2,
// that is we don't use F3
if (f1 < f2) {
useFormula[i * SDP_nBlock + l] = F1;
countf1++;
} else {
useFormula[i * SDP_nBlock + l] = F2;
countf2++;
}
} else {
// this case is SPARSE
if (f1 < f2 && f1 < f3) {
// rMessage("line " << k << " is F1");
useFormula[i * SDP_nBlock + l] = F1;
countf1++;
} else if (f2 < f3) {
// rMessage("line " << k << " is F2");
useFormula[i * SDP_nBlock + l] = F2;
countf2++;
} else {
// rMessage("line " << k << " is F3");
useFormula[i * SDP_nBlock + l] = F3;
countf3++;
}
}
}
// rMessage("Kappa = " << Kappa);
#if 0
rMessage("count f1 = " << countf1
<< ":: count f2 = " << countf2
<< ":: count f3 = " << countf3);
#endif
} // end of 'for (int l)'
if (upNonZeroCount != NULL) {
delete[] upNonZeroCount;
}
upNonZeroCount = NULL;
return;
}
void Newton::compute_rMat(Newton::WHICH_DIRECTION direction, AverageComplementarity &mu, DirectionParameter &beta, Solutions ¤tPt, WorkVariables &work) {
mpf_class MMONE = -1.0;
// CORRECTOR :: r_zinv = (-XZ -dXdZ + mu I)Z^{-1}
// not CORRECTOR :: r_zinv = (-XZ + mu I)Z^{-1}
mpf_class target = beta.value * mu.current;
Lal::let(r_zinvMat, '=', currentPt.invzMat, '*', &target);
Lal::let(r_zinvMat, '=', r_zinvMat, '+', currentPt.xMat, &MMONE);
if (direction == CORRECTOR) {
// work.DLS1 = Dx Dz Z^{-1}
Jal::ns_jordan_triple_product(work.DLS1, DxMat, DzMat, currentPt.invzMat, work.DLS2);
Lal::let(r_zinvMat, '=', r_zinvMat, '+', work.DLS1, &MMONE);
}
// rMessage("r_zinvMat = ");
// r_zinvMat.display();
}
void Newton::Make_gVec(Newton::WHICH_DIRECTION direction, InputData &inputData, Solutions ¤tPt, Residuals ¤tRes, AverageComplementarity &mu, DirectionParameter &beta, Phase &phase, WorkVariables &work, ComputeTime &com) {
TimeStart(START1);
// rMessage("mu = " << mu.current);
// rMessage("beta = " << beta.value);
mpf_class MMONE = -1.0;
compute_rMat(direction, mu, beta, currentPt, work);
TimeEnd(END1);
com.makerMat += TimeCal(START1, END1);
TimeStart(START2);
TimeStart(START_GVEC_MUL);
// work.DLS1 = R Z^{-1} - X D Z^{-1} = r_zinv - X D Z^{-1}
if (phase.value == SolveInfo::pFEAS || phase.value == SolveInfo::noINFO) {
if (direction == CORRECTOR) {
// x_rd_zinvMat is computed in PREDICTOR step
Lal::let(work.DLS1, '=', r_zinvMat, '+', x_rd_zinvMat, &MMONE);
} else {
// currentPt is infeasilbe, that is the residual
// dualMat is not 0.
// x_rd_zinvMat = X D Z^{-1}
Jal::ns_jordan_triple_product(x_rd_zinvMat, currentPt.xMat, currentRes.dualMat, currentPt.invzMat, work.DLS2);
Lal::let(work.DLS1, '=', r_zinvMat, '+', x_rd_zinvMat, &MMONE);
} // if (direction == CORRECTOR)
} else {
// dualMat == 0
work.DLS1.copyFrom(r_zinvMat);
}
// rMessage("work.DLS1");
// work.DLS1.display();
TimeEnd(END_GVEC_MUL);
com.makegVecMul += TimeCal(START_GVEC_MUL, END_GVEC_MUL);
inputData.multi_InnerProductToA(work.DLS1, gVec);
Lal::let(gVec, '=', gVec, '*', &MMONE);
// rMessage("gVec = ");
// gVec.display();
#if 0
if (phase.value == SolveInfo:: dFEAS
|| phase.value == SolveInfo::noINFO) {
#endif
Lal::let(gVec, '=', gVec, '+', currentRes.primalVec);
#if 0
}
#endif
TimeEnd(END2);
com.makegVec += TimeCal(START2, END2);
}
void Newton::calF1(mpf_class &ret, DenseMatrix &G, SparseMatrix &Aj) { Lal::let(ret, '=', Aj, '.', G); }
void Newton::calF2(mpf_class &ret, DenseMatrix &F, DenseMatrix &G, DenseMatrix &X, SparseMatrix &Aj, bool &hasF2Gcal) {
int alpha, beta;
mpf_class value1, value2;
int n = Aj.nRow;
// rMessage(" using F2 ");
switch (Aj.type) {
case SparseMatrix::SPARSE:
// rMessage("F2::SPARSE " << Aj.NonZeroCount);
ret = 0.0;
for (int index = 0; index < Aj.NonZeroCount; ++index) {
alpha = Aj.row_index[index];
beta = Aj.column_index[index];
value1 = Aj.sp_ele[index];
// value2 = F77_FUNC (ddot, DDOT)(&n, &X.de_ele[alpha+n*0], &n,
// &F.de_ele[0+n*beta], &IONE);
value2 = Rdot(n, X.de_ele + alpha, n, F.de_ele + (n * beta), 1);
ret += value1 * value2;
if (alpha != beta) {
// value2 = F77_FUNC (ddot, DDOT)(&n, &X.de_ele[beta+n*0], &n,
// &F.de_ele[0+n*alpha], &IONE);
value2 = Rdot(n, X.de_ele + beta, n, F.de_ele + (n * alpha), 1);
ret += value1 * value2;
}
}
break;
case SparseMatrix::DENSE:
// G is temporary matrix
// rMessage("F2::DENSE");
if (hasF2Gcal == false) {
// rMessage(" using F2 changing to F1");
Lal::let(G, '=', X, '*', F);
hasF2Gcal = true;
}
Lal::let(ret, '=', Aj, '.', G);
break;
} // end of switch
}
void Newton::calF3(mpf_class &ret, DenseMatrix &F, DenseMatrix &G, DenseMatrix &X, DenseMatrix &invZ, SparseMatrix &Ai, SparseMatrix &Aj) {
ret = 0.0;
//#pragma omp parallel
{
mpf_class local_ret = 0.0;
mpf_class sum, value1, value2, plu;
//#pragma omp for nowait
for (int index1 = 0; index1 < Aj.NonZeroCount; ++index1) {
int alpha = Aj.row_index[index1];
int beta = Aj.column_index[index1];
value1 = Aj.sp_ele[index1];
sum = 0.0;
for (int index2 = 0; index2 < Ai.NonZeroCount; ++index2) {
int gamma = Ai.row_index[index2];
int delta = Ai.column_index[index2];
value2 = Ai.sp_ele[index2];
plu = value2;
plu *= invZ.de_ele[delta + invZ.nCol * beta];
plu *= X.de_ele[alpha + X.nCol * gamma];
sum += plu;
if (gamma != delta) {
plu = value2;
plu *= invZ.de_ele[gamma + invZ.nCol * beta];
plu *= X.de_ele[alpha + X.nCol * delta];
sum += plu;
}
}
plu = value1;
plu *= sum;
local_ret += plu;
if (alpha == beta) {
continue;
}
sum = 0.0;
for (int index2 = 0; index2 < Ai.NonZeroCount; ++index2) {
int gamma = Ai.row_index[index2];
int delta = Ai.column_index[index2];
value2 = Ai.sp_ele[index2];
plu = value2;
plu *= invZ.de_ele[delta + invZ.nCol * alpha];
plu *= X.de_ele[beta + X.nCol * gamma];
sum += plu;
if (gamma != delta) {
plu = value2;
plu *= invZ.de_ele[gamma + invZ.nCol * alpha];
plu *= X.de_ele[beta + X.nCol * delta];
sum += plu;
}
}
plu = value1;
plu *= sum;
local_ret += plu;