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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& currentPt,
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& currentPt,
Residuals& currentRes,
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)
{
// Ai and Aj are SPARSE
ret = 0.0;
mpf_class sum;
// rMessage("Aj.NonZeroCount = " << Aj.NonZeroCount);
for (int index1=0; index1<Aj.NonZeroCount; ++index1) {
int alpha = Aj.row_index[index1];
int beta = Aj.column_index[index1];
mpf_class 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];
mpf_class value2 = Ai.sp_ele[index2];
mpf_class plu = value2*invZ.de_ele[delta+invZ.nCol*beta]