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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) 2004-2012 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 <sched.h>
#include "sdpa_newton.h"
#include "sdpa_parts.h"
#include "sdpa_jordan.h"
#include "sdpa_linear.h"
#include "sdpa_algebra.h"
namespace sdpa {
pthread_mutex_t Newton::job_mutex = PTHREAD_MUTEX_INITIALIZER;
pthread_cond_t Newton::job_cond = PTHREAD_COND_INITIALIZER;
int Newton::Column_Number = 0;
bool Newton::mutex_flag = false;
int Newton::Calc_F1 = 0;
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;
diagonalIndex = NULL;
NUM_THREADS = 1;
NUM_GOTOBLAS = 1;
}
Newton::Newton(int m, BlockStruct& bs)
{
initialize(m, bs);
}
Newton::~Newton()
{
terminate();
}
void Newton::initialize(int m, BlockStruct& bs)
{
gVec.initialize(m);
SDP_nBlock = bs.SDP_nBlock;
SOCP_nBlock = bs.SOCP_nBlock;
LP_nBlock = bs.LP_nBlock;
DxMat.initialize(bs);
DyVec.initialize(m);
DzMat.initialize(bs);
r_zinvMat.initialize(bs);
x_rd_zinvMat.initialize(bs);
// Memory allocation of useFormula is moved to computeFormula_SDP
// NewArray(useFormula,FormulaType,m*SDP_nBlock);
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;
diagonalIndex = NULL;
}
void Newton::terminate()
{
if (bMat_type == SPARSE){
if (SDP_location_sparse_bMat && SDP_constraint1 && SDP_constraint2
&& SDP_blockIndex1 && SDP_blockIndex2) {
for (int l=0; l<SDP_nBlock; ++l) {
DeleteArray(SDP_location_sparse_bMat[l]);
DeleteArray(SDP_constraint1[l]);
DeleteArray(SDP_constraint2[l]);
DeleteArray(SDP_blockIndex1[l]);
DeleteArray(SDP_blockIndex2[l]);
}
DeleteArray(SDP_number);
DeleteArray(SDP_location_sparse_bMat);
DeleteArray(SDP_constraint1);
DeleteArray(SDP_constraint2);
DeleteArray(SDP_blockIndex1);
DeleteArray(SDP_blockIndex2);
}
#if 0
if (SOCP_location_sparse_bMat && SOCP_constraint1 && SOCP_constraint2
&& SOCP_blockIndex1 && SOCP_blockIndex2) {
for (int l=0; l<SOCP_nBlock; ++l) {
DeleteArray(SOCP_location_sparse_bMat[l]);
DeleteArray(SOCP_constraint1[l]);
DeleteArray(SOCP_constraint2[l]);
DeleteArray(SOCP_blockIndex1[l]);
DeleteArray(SOCP_blockIndex2[l]);
}
DeleteArray(SOCP_number);
DeleteArray(SOCP_location_sparse_bMat);
DeleteArray(SOCP_constraint1);
DeleteArray(SOCP_constraint2);
DeleteArray(SOCP_blockIndex1);
DeleteArray(SOCP_blockIndex2);
}
#endif
if (LP_location_sparse_bMat && LP_constraint1 && LP_constraint2
&& LP_blockIndex1 && LP_blockIndex2) {
for (int l=0; l<LP_nBlock; ++l) {
DeleteArray(LP_location_sparse_bMat[l]);
DeleteArray(LP_constraint1[l]);
DeleteArray(LP_constraint2[l]);
DeleteArray(LP_blockIndex1[l]);
DeleteArray(LP_blockIndex2[l]);
}
DeleteArray(LP_number);
DeleteArray(LP_location_sparse_bMat);
DeleteArray(LP_constraint1);
DeleteArray(LP_constraint2);
DeleteArray(LP_blockIndex1);
DeleteArray(LP_blockIndex2);
}
DeleteArray(diagonalIndex);
sparse_bMat.terminate();
} else { // bMat_type == DENSE
bMat.terminate();
}
int m = gVec.nDim;
gVec.terminate();
DxMat.terminate();
DyVec.terminate();
DzMat.terminate();
r_zinvMat.terminate();
x_rd_zinvMat.terminate();
if (useFormula) {
for (int j=0; j<m; ++j) {
DeleteArray(useFormula[j]);
}
DeleteArray(useFormula);
}
}
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)
{
// bMat_type = SPARSE;
// printf("SPARSE computation\n");
// initialize sparse_bMat by Chordal::makeGraph
// sparse_bMat.display();
bool isEmptyMatrix = false;
// make index of diagonalIndex
NewArray(diagonalIndex,int,m+1);
int k=0;
for (int index=0; index<sparse_bMat.NonZeroCount; index++){
if (sparse_bMat.row_index[index] == sparse_bMat.column_index[index]) {
diagonalIndex[k] = index;
if (sparse_bMat.row_index[index] != k+1) {
rMessage("The matrix [" << (sparse_bMat.row_index[index]-1)
<< "] is empty");
isEmptyMatrix = true;
diagonalIndex[k+1] = diagonalIndex[k];
k++;
}
k++;
}
}
if (isEmptyMatrix) {
rMessage("Input Data Error :: Some Input Matricies are Empty");
}
diagonalIndex[m] = sparse_bMat.NonZeroCount;
#if 0
rMessage("diagonalIndex = ");
for (int index=0; index <m; ++index) {
printf(" [%d:%d]",index,diagonalIndex[index]);
}
printf("\n");
#endif
}
// 2008/03/12 kazuhide nakata
void Newton::initialize_bMat(int m, Chordal& chordal,
InputData& inputData,
FILE* Display,
FILE* fpOut)
{
/* Create clique tree */
switch (chordal.best) {
case SELECT_DENSE:
bMat_type = DENSE;
if (Display) {
fprintf(Display,"Schur computation : DENSE \n");
}
if (fpOut) {
fprintf(fpOut,"Schur computation : DENSE \n");
}
initialize_dense_bMat(m);
// Here, we release MUMPS and sparse_bMat
chordal.terminate();
break;
case SELECT_MUMPS_BEST:
bMat_type = SPARSE;
if (Display) {
fprintf(Display,"Schur computation : SPARSE \n");
}
if (fpOut) {
fprintf(fpOut,"Schur computation : SPARSE \n");
}
initialize_sparse_bMat(m);
make_aggrigateIndex(inputData);
break;
default:
rError("Wrong Ordering Obtained");
break;
}
}
int Newton::binarySearchIndex(int i, int j)
{
// binary search for index of sparse_bMat
int t = -1;
// We store only the lower triangular
int ii = i, jj = j;
if (i<j) {
jj = i;
ii = j;
}
int begin = diagonalIndex[jj];
int end = diagonalIndex[jj+1]-1;
int target = (begin + end) / 2;
while (end - begin > 1){
if (sparse_bMat.row_index[target] < ii+1){
begin = target;
target = (begin + end) / 2;
} else if (sparse_bMat.row_index[target] > ii+1){
end = target;
target = (begin + end) / 2;
} else if (sparse_bMat.row_index[target] == ii+1) {
t = target;
break;
}
}
if (t == -1){
if (sparse_bMat.row_index[begin] == ii+1){
t = begin;
} else if (sparse_bMat.row_index[end] == ii+1){
t = end;
} else {
#if 0
int m = sparse_bMat.nRow;
rMessage("Trouble ii = " << ii << " jj = " << j << " m = " << m);
for (int k = 0; k<sparse_bMat.NonZeroCount; ++k) {
fprintf(stdout,"[%04d,%04d] at %04d\n",
sparse_bMat.row_index[k],
sparse_bMat.column_index[k],
k);
}
#endif
// rError("Newton::make_aggrigateIndex program bug");
}
}
return t;
}
void Newton::make_aggrigateIndex_SDP(InputData& inputData)
{
SDP_nBlock = inputData.SDP_nBlock;
NewArray(SDP_number,int,SDP_nBlock);
// memory allocate for aggrigateIndex
NewArray(SDP_constraint1,int*,SDP_nBlock);
NewArray(SDP_constraint2,int*,SDP_nBlock);
NewArray(SDP_blockIndex1,int*,SDP_nBlock);
NewArray(SDP_blockIndex2,int*,SDP_nBlock);
NewArray(SDP_location_sparse_bMat,int*,SDP_nBlock);
for (int l=0; l<SDP_nBlock; l++){
const int size = (inputData.SDP_nConstraint[l] + 1)
* inputData.SDP_nConstraint[l] / 2;
SDP_number[l] = size;
NewArray(SDP_constraint1[l],int,size);
NewArray(SDP_constraint2[l],int,size);
NewArray(SDP_blockIndex1[l],int,size);
NewArray(SDP_blockIndex2[l],int,size);
NewArray(SDP_location_sparse_bMat[l],int,size);
}
for (int l = 0; l<SDP_nBlock; l++){
int NonZeroCount = 0;
for (int k1=0; k1<inputData.SDP_nConstraint[l]; k1++){
int j = inputData.SDP_constraint[l][k1];
int jb = inputData.SDP_blockIndex[l][k1];
int jnz = inputData.A[j].SDP_sp_block[jb].NonZeroEffect;
for (int k2=0; k2<inputData.SDP_nConstraint[l]; k2++){
int i = inputData.SDP_constraint[l][k2];
int ib = inputData.SDP_blockIndex[l][k2];
int inz = inputData.A[i].SDP_sp_block[ib].NonZeroEffect;
if ((jnz < inz) || ((inz == jnz) && (i < j))){
continue;
}
// set index which A_i and A_j are not zero matrix
int target = binarySearchIndex(i,j);
if (target == -1) {
rMessage("("<<(i+1)<<","<<(j+1)<<") might have index");
SDP_number[l]--;
continue;
}
SDP_constraint1[l][NonZeroCount] = i;
SDP_constraint2[l][NonZeroCount] = j;
SDP_blockIndex1[l][NonZeroCount] = ib;
SDP_blockIndex2[l][NonZeroCount] = jb;
SDP_location_sparse_bMat[l][NonZeroCount] = target;
NonZeroCount++;
}
} // for k1
} //for l lth block
}
void Newton::make_aggrigateIndex_SOCP(InputData& inputData)
{
SOCP_nBlock = inputData.SOCP_nBlock;
NewArray(SOCP_number,int,SOCP_nBlock);
if (SOCP_number == NULL) {
rError("Newton::make_aggrigateIndex_SOCP memory exhausted ");
}
NewArray(SOCP_constraint1,int*,SOCP_nBlock);
NewArray(SOCP_constraint2,int*,SOCP_nBlock);
NewArray(SOCP_blockIndex1,int*,SOCP_nBlock);
NewArray(SOCP_blockIndex2,int*,SOCP_nBlock);
NewArray(SOCP_location_sparse_bMat,int*,SOCP_nBlock);
for (int l=0; l<SOCP_nBlock; l++){
int size = (inputData.SOCP_nConstraint[l]+1)
* inputData.SOCP_nConstraint[l]/2;
SOCP_number[l] = size;
NewArray(SOCP_constraint1[l],int,size);
NewArray(SOCP_constraint2[l],int,size);
NewArray(SOCP_blockIndex1[l],int,size);
NewArray(SOCP_blockIndex2[l],int,size);
NewArray(SOCP_location_sparse_bMat[l],int,size);
}
for (int l = 0; l<SOCP_nBlock; l++){
int NonZeroCount = 0;
for (int k1=0; k1<inputData.SOCP_nConstraint[l]; k1++){
int j = inputData.SOCP_constraint[l][k1];
int jb = inputData.SOCP_blockIndex[l][k1];
int jnz = inputData.A[j].SOCP_sp_block[jb].NonZeroEffect;
for (int k2=0; k2<inputData.SOCP_nConstraint[l]; k2++){
int i = inputData.SOCP_constraint[l][k2];
int ib = inputData.SOCP_blockIndex[l][k2];
int inz = inputData.A[i].SOCP_sp_block[ib].NonZeroEffect;
if ((jnz < inz) || ((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;
int target = binarySearchIndex(i,j);
SOCP_location_sparse_bMat[l][NonZeroCount] = target;
NonZeroCount++;
}
} // for k1
} //for l lth block
}
void Newton::make_aggrigateIndex_LP(InputData& inputData)
{
LP_nBlock = inputData.LP_nBlock;
NewArray(LP_number,int,LP_nBlock);
// memory allocate for aggrigateIndex
NewArray(LP_constraint1,int*,LP_nBlock);
NewArray(LP_constraint2,int*,LP_nBlock);
NewArray(LP_blockIndex1,int*,LP_nBlock);
NewArray(LP_blockIndex2,int*,LP_nBlock);
NewArray(LP_location_sparse_bMat,int*,LP_nBlock);
for (int l=0; l<LP_nBlock; l++){
int size = (inputData.LP_nConstraint[l]+1)
* inputData.LP_nConstraint[l]/2;
LP_number[l] = size;
NewArray(LP_constraint1[l],int,size);
NewArray(LP_constraint2[l],int,size);
NewArray(LP_blockIndex1[l],int,size);
NewArray(LP_blockIndex2[l],int,size);
NewArray(LP_location_sparse_bMat[l],int,size);
}
for (int l = 0; l<LP_nBlock; l++){
int NonZeroCount = 0;
for (int k1=0; k1<inputData.LP_nConstraint[l]; k1++){
int j = inputData.LP_constraint[l][k1];
int jb = inputData.LP_blockIndex[l][k1];
for (int k2=0; k2<inputData.LP_nConstraint[l]; k2++){
int i = inputData.LP_constraint[l][k2];
int ib = 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;
int target = binarySearchIndex(i,j);
LP_location_sparse_bMat[l][NonZeroCount] = target;
NonZeroCount++;
}
} // for k1
} //for l lth 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,
double DenseRatio, double Kappa)
{
int m = inputData.b.nDim;
int SDP_nBlock = inputData.SDP_nBlock;
NewArray(useFormula, FormulaType*, m);
for (int j=0; j<m; ++j) {
NewArray(useFormula[j], FormulaType, inputData.A[j].SDP_sp_nBlock);
}
// need not to initialize useFormula
int** upNonZeroCount;
NewArray(upNonZeroCount, int*, m);
for (int j=0; j<m; ++j) {
NewArray(upNonZeroCount[j], int, inputData.A[j].SDP_sp_nBlock);
}
for (int j=0; j<m; ++j) {
for (int jb=0; jb < inputData.A[j].SDP_sp_nBlock; ++jb) {
upNonZeroCount[j][jb] = 0;
}
}
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 l=0; l<SDP_nBlock; ++l) {
for (int k1=0; k1 < inputData.SDP_nConstraint[l];k1++){
int j = inputData.SDP_constraint[l][k1];
int jb = inputData.SDP_blockIndex[l][k1];
int jnz = A[j].SDP_sp_block[jb].NonZeroEffect;
int up = jnz;
// rMessage("up = " << up);
for (int k2=0; k2 < inputData.SDP_nConstraint[l];k2++){
int i = inputData.SDP_constraint[l][k2];
int ib = inputData.SDP_blockIndex[l][k2];
int inz = A[i].SDP_sp_block[ib].NonZeroEffect;
// printf("%d %d %d %d %d %d\n",i,ib,inz, j, jb,jnz);
if (inz < jnz) {
up += inz;
}
#if 1
else if ((jnz == inz) && i>j ) {
up += inz;
}
#endif
}
upNonZeroCount[j][jb] = up;
// rMessage("up = " << up);
}
}
// Determine which formula
double ff=0, ff1=0, ff2=0, ff3=0;
Calc_F1 = 0;
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 j = inputData.SDP_constraint[l][k];
int jb = inputData.SDP_blockIndex[l][k];
double jnz = inputData.A[j].SDP_sp_block[jb].NonZeroEffect;
double f1,f2,f3;
double n = inputData.A[j].SDP_sp_block[jb].nRow;
double up = upNonZeroCount[j][jb];
#if 1
f1 = Kappa*n*jnz + n*n*n + Kappa*up;
f2 = Kappa*n*jnz + Kappa*(n+1)*up;
#if 0
f3 = Kappa*(2*Kappa*jnz+1)*up/Kappa;
#else
f3 = Kappa*(2*Kappa*jnz+1)*up;
#endif
#endif
ff1 += f1;
ff2 += f2;
ff3 += f3;
//rMessage("up = " << up << " nonzero = " << nonzero);
//rMessage("f1=" << f1 << " f2=" << f2 << " f3=" << f3);
//printf("%d %d %lf %lf %lf\n",k,l,f1,f2,f3);
//printf("%d %d %le %le %le\n",k,l,f1,f2,f3);
if (inputData.A[j].SDP_sp_block[jb].type == SparseMatrix::DENSE) {
// if DENSE, we use only F1 or F2,
// that is we don't use F3
if (f1<f2) {
useFormula[j][jb] = F1;
countf1++;
ff += f1;
} else {
useFormula[j][jb] = F2;
countf2++;
ff += f2;
}
} else {
//printf("n = %d, inz = %d, n * n = %d, inz / (n * n) = %lf\n", (int)n, (int)inz, (int)(n*n), (double)inz/(n*n));
// this case is SPARSE
if (f1<f2 && f1<f3) {
if ((n <= 200) && (2.0 * n >= jnz)) {
//rMessage("line " << k << " is F3:1");
useFormula[j][jb] = F3;
countf3++;
ff += f3;
}
else {
//rMessage("line " << k << " is F1");
useFormula[j][jb] = F1;
countf1++;
ff += f1;
}
} else if (f2<f3) {
//rMessage("line " << k << " is F2");
useFormula[j][jb] = F2;
countf2++;
ff += f2;
} else {
//rMessage("line " << k << " is F3:2");
useFormula[j][jb] = F3;
countf3++;
ff += f3;
}
}
}
Calc_F1 += countf1;
// rMessage("Kappa = " << Kappa);
#if 0
rMessage("count f1 = " << countf1
<< ":: count f2 = " << countf2
<< ":: count f3 = " << countf3);
#endif
} // end of 'for (int l)'
for (int j=0; j<m; ++j) {
DeleteArray(upNonZeroCount[j]);
}
DeleteArray(upNonZeroCount);
return;
}
void Newton::compute_rMat(Newton::WHICH_DIRECTION direction,
AverageComplementarity& mu,
DirectionParameter& beta,
Solutions& currentPt,
WorkVariables& work)
{
// CORRECTOR :: r_zinv = (-XZ -dXdZ + mu I)Z^{-1}
// not CORRECTOR :: r_zinv = (-XZ + mu I)Z^{-1}
double target = beta.value*mu.current;
Lal::let(r_zinvMat,'=',currentPt.invzMat,'*',&target);
Lal::let(r_zinvMat,'=',r_zinvMat,'+',currentPt.xMat,&DMONE);
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,&DMONE);
}
// 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);
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,&DMONE);
} 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,&DMONE);
} // 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,'*',&DMONE);
// 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(double& ret, DenseMatrix& G,
SparseMatrix& Ai)
{
Lal::let(ret,'=',Ai,'.',G);
}
void Newton::calF2(double& ret,
DenseMatrix& F, DenseMatrix& G,
DenseMatrix& invZ, SparseMatrix& Ai,
bool& hasF2Gcal)
{
int alpha,beta;
double value1,value2;
int n = Ai.nRow;
// rMessage(" using F2 ");
switch (Ai.type) {
case SparseMatrix::SPARSE:
// rMessage("F2::SPARSE " << Ai.NonZeroCount);
ret = 0.0;
for (int index = 0; index < Ai.NonZeroCount; ++index) {
#if DATA_CAPSULE
alpha = Ai.DataS[index].vRow;
beta = Ai.DataS[index].vCol;
value1 = Ai.DataS[index].vEle;
#else
alpha = Ai.row_index[index];
beta = Ai.column_index[index];
value1 = Ai.sp_ele[index];
#endif
value2 = ddot_fc(&n, &F.de_ele[alpha+n*0], &n,
&invZ.de_ele[0+n*beta], &IONE);
ret += value1*value2;
if (alpha!=beta) {
value2 = ddot_fc(&n, &F.de_ele[beta+n*0], &n,
&invZ.de_ele[0+n*alpha], &IONE);
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,'=',F,'*',invZ);
hasF2Gcal = true;
}
Lal::let(ret,'=',Ai,'.',G);
break;
} // end of switch
}
inline void Newton::calF3(double& ret,
DenseMatrix& X, DenseMatrix& invZ,
SparseMatrix& Ai, SparseMatrix& Aj)
{
// Ai and Aj are SPARSE
ret = 0.0;
double sum;
const int nCol = X.nCol;
// rMessage("Aj.NonZeroCount = " << Aj.NonZeroCount);
for (int index1=0; index1<Aj.NonZeroCount; ++index1) {
#if DATA_CAPSULE
int alpha = Aj.DataS[index1].vRow;
int beta = Aj.DataS[index1].vCol;
double value1 = Aj.DataS[index1].vEle;
#else
int alpha = Aj.row_index[index1];
int beta = Aj.column_index[index1];
double value1 = Aj.sp_ele[index1];
#endif
sum = 0.0;
for (int index2=0; index2<Ai.NonZeroCount; ++index2) {
#if DATA_CAPSULE
int gamma = Ai.DataS[index2].vRow;
int delta = Ai.DataS[index2].vCol;
double value2 = Ai.DataS[index2].vEle;
#else
int gamma = Ai.row_index[index2];
int delta = Ai.column_index[index2];
double value2 = Ai.sp_ele[index2];
#endif
double plu = value2*invZ.de_ele[delta+nCol*beta]
* X.de_ele[gamma+nCol*alpha];
sum += plu;
if (gamma!=delta) {
double plu2 = value2*invZ.de_ele[gamma+nCol*beta]
* X.de_ele[delta+nCol*alpha];
sum += plu2;
}
}
ret += value1*sum;
if (alpha==beta) {
continue;
}
sum = 0.0;
for (int index2=0; index2<Ai.NonZeroCount; ++index2) {
#if DATA_CAPSULE
int gamma = Ai.DataS[index2].vRow;
int delta = Ai.DataS[index2].vCol;
double value2 = Ai.DataS[index2].vEle;
#else
int gamma = Ai.row_index[index2];
int delta = Ai.column_index[index2];
double value2 = Ai.sp_ele[index2];
#endif
double plu = value2*invZ.de_ele[delta+nCol*alpha]
* X.de_ele[gamma+nCol*beta];
sum += plu;
if (gamma!=delta) {
double plu2 = value2*invZ.de_ele[gamma+nCol*alpha]
* X.de_ele[delta+nCol*beta];
sum += plu2;
}
}
ret += value1*sum;
} // end of 'for (index1)'
return;
}
inline void Newton::calF1_thread(double& ret, DenseMatrix& G,
SparseMatrix& Ai)
{
Lal::let(ret,'=',Ai,'.',G);
}
void Newton::calF2_thread(double& ret,
DenseMatrix& F, DenseMatrix& G,
DenseMatrix& invZ, SparseMatrix& Ai,
bool& hasF2Gcal)
{
int alpha,beta;
double value1,value2;
int n = Ai.nRow;
// rMessage(" using F2 ");
switch (Ai.type) {
case SparseMatrix::SPARSE:
// rMessage("F2::SPARSE " << Aj.NonZeroCount);
ret = 0.0;
for (int index = 0; index < Ai.NonZeroCount; ++index) {
#if DATA_CAPSULE
alpha = Ai.DataS[index].vRow;
beta = Ai.DataS[index].vCol;
value1 = Ai.DataS[index].vEle;
#else
alpha = Ai.row_index[index];
beta = Ai.column_index[index];
value1 = Ai.sp_ele[index];
#endif
value2 = ddot_fc(&n, &F.de_ele[alpha+n*0], &n,
&invZ.de_ele[0+n*beta], &IONE);
ret += value1*value2;
if (alpha!=beta) {
value2 = ddot_fc(&n, &F.de_ele[beta+n*0], &n,
&invZ.de_ele[0+n*alpha], &IONE);
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,'=',F,'*',invZ);
hasF2Gcal = true;
}
Lal::let(ret,'=',Ai,'.',G);
break;
} // end of switch
}
#if 0
inline void Newton::calF3_thread(double& ret,
DenseMatrix& X, DenseMatrix& invZ,
SparseMatrix& Ai, SparseMatrix& Aj)
{
// Ai and Aj are SPARSE
ret = 0.0;
double sum;
const int nCol = X.nCol;
// rMessage("Aj.NonZeroCount = " << Aj.NonZeroCount);
for (int index1=0; index1<Aj.NonZeroCount; ++index1) {
#if DATA_CAPSULE
int alpha = Aj.DataS[index1].vRow;
int beta = Aj.DataS[index1].vCol;
double value1 = Aj.DataS[index1].vEle;
#else
int alpha = Aj.row_index[index1];
int beta = Aj.column_index[index1];
double value1 = Aj.sp_ele[index1];
#endif
sum = 0.0;
const int nCol = X.nCol;
double *Xalpha, *Xbeta, *invZalpha, *invZbeta;
Xalpha = &X.de_ele[nCol*alpha];
Xbeta = &X.de_ele[nCol*beta];
invZalpha = &invZ.de_ele[nCol*alpha];
invZbeta = &invZ.de_ele[nCol*beta];
for (int index2=0; index2<Ai.NonZeroCount; ++index2) {
#if DATA_CAPSULE
int gamma = Ai.DataS[index2].vRow;
int delta = Ai.DataS[index2].vCol;
double value2 = Ai.DataS[index2].vEle;
#else
int gamma = Ai.row_index[index2];
int delta = Ai.column_index[index2];
double value2 = Ai.sp_ele[index2];
#endif
double plu = value2*invZbeta[delta] * Xalpha[gamma];
sum += plu;
if (gamma!=delta) {
double plu2 = value2*invZbeta[gamma] * Xalpha[delta];
sum += plu2;
}
}
ret += value1*sum;
if (alpha==beta) {
continue;
}
sum = 0.0;
for (int index2=0; index2<Ai.NonZeroCount; ++index2) {
#if DATA_CAPSULE
int gamma = Ai.DataS[index2].vRow;
int delta = Ai.DataS[index2].vCol;
double value2 = Ai.DataS[index2].vEle;
#else
int gamma = Ai.row_index[index2];
int delta = Ai.column_index[index2];
double value2 = Ai.sp_ele[index2];
#endif
double plu = value2*invZalpha[delta] * Xbeta[gamma];
sum += plu;
if (gamma!=delta) {
double plu2 = value2*invZalpha[gamma] * Xbeta[delta];
sum += plu2;
}
}
ret += value1*sum;
} // end of 'for (index1)'
return;
}
#endif
inline void Newton::calF3_thread_1x1(double& ret,
DenseMatrix& X, DenseMatrix& invZ,
SparseMatrix& Ai, SparseMatrix& Aj)
{
// Ai and Aj are SPARSE
ret = 0.0;