/* Returns reduced gradient.Returns an offset (to be added to current one).
*/
double
ClpLinearObjective::reducedGradient(ClpSimplex * model, double * region,
bool /*useFeasibleCosts*/)
{
int numberRows = model->numberRows();
//work space
CoinIndexedVector * workSpace = model->rowArray(0);
CoinIndexedVector arrayVector;
arrayVector.reserve(numberRows + 1);
int iRow;
#ifdef CLP_DEBUG
workSpace->checkClear();
#endif
double * array = arrayVector.denseVector();
int * index = arrayVector.getIndices();
int number = 0;
const double * cost = model->costRegion();
//assert (!useFeasibleCosts);
const int * pivotVariable = model->pivotVariable();
for (iRow = 0; iRow < numberRows; iRow++) {
int iPivot = pivotVariable[iRow];
double value = cost[iPivot];
if (value) {
array[iRow] = value;
index[number++] = iRow;
}
}
arrayVector.setNumElements(number);
int numberColumns = model->numberColumns();
// Btran basic costs
double * work = workSpace->denseVector();
model->factorization()->updateColumnTranspose(workSpace, &arrayVector);
ClpFillN(work, numberRows, 0.0);
// now look at dual solution
double * rowReducedCost = region + numberColumns;
double * dual = rowReducedCost;
double * rowCost = model->costRegion(0);
for (iRow = 0; iRow < numberRows; iRow++) {
dual[iRow] = array[iRow];
}
double * dj = region;
ClpDisjointCopyN(model->costRegion(1), numberColumns, dj);
model->transposeTimes(-1.0, dual, dj);
for (iRow = 0; iRow < numberRows; iRow++) {
// slack
double value = dual[iRow];
value += rowCost[iRow];
rowReducedCost[iRow] = value;
}
return 0.0;
}
//--------------------------------------------------------------------------
void
CoinIndexedVectorUnitTest()
{
int i;
// Test default constructor
{
CoinIndexedVector r;
assert( r.indices_==NULL );
assert( r.elements_==NULL );
assert( r.getNumElements()==0 );
assert( r.capacity_==0);
}
// Test set and get methods
const int ne = 4;
int inx[ne] = { 1, 3, 4, 7 };
double el[ne] = { 1.2, 3.4, 5.6, 7.8 };
{
CoinIndexedVector r;
assert( r.getNumElements()==0 );
// Test setting/getting elements with int* & float* vectors
r.setVector( ne, inx, el );
assert( r.getNumElements()==ne );
for ( i=0; i<ne; i++ ) {
assert( r.getIndices()[i] == inx[i] );
assert( r[inx[i]] == el[i] );
}
// Test setting/getting elements with indices out of order
const int ne2 = 5;
int inx2[ne2] = { 2, 4, 8, 14, 3 };
double el2[ne2] = { 2.2, 4.4, 6.6, 8.8, 3.3 };
r.setVector(ne2,inx2,el2);
assert( r.getNumElements()==ne2 );
assert( r.getIndices()[0]==inx2[0] );
assert( r.getIndices()[1]==inx2[1] );
assert( r.getIndices()[2]==inx2[2] );
assert( r.getIndices()[3]==inx2[3] );
assert( r.getIndices()[4]==inx2[4] );
CoinIndexedVector r1(ne2,inx2,el2);
assert( r == r1 );
}
CoinIndexedVector r;
{
CoinIndexedVector r;
const int ne = 3;
int inx[ne] = { 1, 2, 3 };
double el[ne] = { 2.2, 4.4, 8.8};
r.setVector(ne,inx,el);
int c = r.capacity();
// Test swap function
r.swap(0,2);
assert( r.getIndices()[0]==3 );
assert( r.getIndices()[1]==2 );
assert( r.getIndices()[2]==1 );
assert( r.capacity() == c );
// Test the append function
CoinIndexedVector s;
const int nes = 4;
int inxs[nes] = { 11, 12, 13, 14 };
double els[nes] = { .122, 14.4, 18.8, 19.9};
s.setVector(nes,inxs,els);
r.append(s);
assert( r.getNumElements()==7 );
assert( r.getIndices()[0]==3 );
assert( r.getIndices()[1]==2 );
assert( r.getIndices()[2]==1 );
assert( r.getIndices()[3]==11 );
assert( r.getIndices()[4]==12 );
assert( r.getIndices()[5]==13 );
assert( r.getIndices()[6]==14 );
// Test the resize function
c = r.capacity();
r.truncate(4);
// we will lose 11
assert( r.getNumElements()==3 );
assert( r.getIndices()[0]==3 );
assert( r.getIndices()[1]==2 );
assert( r.getIndices()[2]==1 );
assert( r.getMaxIndex() == 3 );
assert( r.getMinIndex() == 1 );
assert( r.capacity() == c );
}
// Test borrow and return vector
{
CoinIndexedVector r,r2;
const int ne = 3;
int inx[ne] = { 1, 2, 3 };
double el[ne] = { 2.2, 4.4, 8.8};
double els[4] = { 0.0,2.2, 4.4, 8.8};
r.setVector(ne,inx,el);
r2.borrowVector(4,ne,inx,els);
assert (r==r2);
r2.returnVector();
assert (!r2.capacity());
assert (!r2.getNumElements());
assert (!r2.denseVector());
assert (!r2.getIndices());
}
// Test copy constructor and assignment operator
{
CoinIndexedVector rhs;
{
CoinIndexedVector r;
{
CoinIndexedVector rC1(r);
assert( 0==r.getNumElements() );
assert( 0==rC1.getNumElements() );
r.setVector( ne, inx, el );
assert( ne==r.getNumElements() );
assert( 0==rC1.getNumElements() );
}
CoinIndexedVector rC2(r);
assert( ne==r.getNumElements() );
assert( ne==rC2.getNumElements() );
for ( i=0; i<ne; i++ ) {
assert( r.getIndices()[i] == rC2.getIndices()[i] );
}
rhs=rC2;
}
// Test that rhs has correct values even though lhs has gone out of scope
assert( rhs.getNumElements()==ne );
for ( i=0; i<ne; i++ ) {
assert( inx[i] == rhs.getIndices()[i] );
}
}
// Test operator==
{
CoinIndexedVector v1,v2;
assert( v1==v2 );
assert( v2==v1 );
assert( v1==v1 );
assert( !(v1!=v2) );
v1.setVector( ne, inx, el );
assert ( !(v1==v2) );
assert ( v1!=v2 );
CoinIndexedVector v3(v1);
assert( v3==v1 );
assert( v3!=v2 );
CoinIndexedVector v4(v2);
assert( v4!=v1 );
assert( v4==v2 );
}
{
// Test sorting of indexed vectors
const int ne = 4;
int inx[ne] = { 1, 4, 0, 2 };
double el[ne] = { 10., 40., 1., 20. };
CoinIndexedVector r;
r.setVector(ne,inx,el);
// Test that indices are in increasing order
r.sort();
for ( i=1; i<ne; i++ ) assert( r.getIndices()[i-1] < r.getIndices()[i] );
}
{
// Test operator[] and indexExists()
const int ne = 4;
int inx[ne] = { 1, 4, 0, 2 };
double el[ne] = { 10., 40., 1., 50. };
CoinIndexedVector r;
bool errorThrown = false;
try {
assert( r[1]==0. );
}
catch (CoinError& e) {
errorThrown = true;
}
assert( errorThrown );
r.setVector(ne,inx,el);
errorThrown = false;
try {
assert( r[-1]==0. );
}
catch (CoinError& e) {
errorThrown = true;
}
assert( errorThrown );
assert( r[ 0]==1. );
assert( r[ 1]==10.);
assert( r[ 2]==50.);
assert( r[ 3]==0. );
assert( r[ 4]==40.);
errorThrown = false;
try {
assert( r[5]==0. );
}
catch (CoinError& e) {
errorThrown = true;
}
assert( errorThrown );
assert ( r.getMaxIndex()==4 );
assert ( r.getMinIndex()==0 );
}
// Test that attemping to get min/max index of a 0,
// length vector
{
CoinIndexedVector nullVec;
assert( nullVec.getMaxIndex() == -COIN_INT_MAX/*0*/ );
assert( nullVec.getMinIndex() == COIN_INT_MAX/*0*/ );
}
// Test CoinFltEq with equivalent method
{
const int ne = 4;
int inx1[ne] = { 1, 3, 4, 7 };
double el1[ne] = { 1.2, 3.4, 5.6, 7.8 };
int inx2[ne] = { 7, 4, 3, 1 };
double el2[ne] = { 7.8+.5, 5.6+.5, 3.4+.5, 1.2+.5 };
CoinIndexedVector v1,v2;
v1.setVector(ne,inx1,el1);
v2.setVector(ne,inx2,el2);
}
{
// Test reserve
CoinIndexedVector v1,v2;
assert( v1.capacity()==0 );
v1.reserve(6);
assert( v1.capacity()==6 );
assert( v1.getNumElements()==0 );
v2=v1;
assert( v2.capacity() == 6 );
assert( v2.getNumElements()==0 );
assert( v2==v1 );
v1.setVector(0,NULL,NULL);
assert( v1.capacity()==6 );
assert( v1.getNumElements()==0 );
assert( v2==v1 );
v2=v1;
assert( v2.capacity() == 6 );
assert( v2.getNumElements()==0 );
assert( v2==v1 );
const int ne = 2;
int inx[ne] = { 1, 3 };
double el[ne] = { 1.2, 3.4 };
v1.setVector(ne,inx,el);
assert( v1.capacity()==6 );
assert( v1.getNumElements()==2 );
v2=v1;
assert( v2.capacity()==6 );
assert( v2.getNumElements()==2 );
assert( v2==v1 );
const int ne1 = 5;
int inx1[ne1] = { 1, 3, 4, 5, 6 };
double el1[ne1] = { 1.2, 3.4, 5., 6., 7. };
v1.setVector(ne1,inx1,el1);
assert( v1.capacity()==7 );
assert( v1.getNumElements()==5 );
v2=v1;
assert( v2.capacity()==7 );
assert( v2.getNumElements()==5 );
assert( v2==v1 );
const int ne2 = 8;
int inx2[ne2] = { 1, 3, 4, 5, 6, 7, 8, 9 };
double el2[ne2] = { 1.2, 3.4, 5., 6., 7., 8., 9., 10. };
v1.setVector(ne2,inx2,el2);
assert( v1.capacity()==10 );
assert( v1.getNumElements()==8 );
v2=v1;
assert( v2.getNumElements()==8 );
assert( v2==v1 );
v1.setVector(ne1,inx1,el1);
assert( v1.capacity()==10 );
assert( v1.getNumElements()==5 );
v2=v1;
assert( v2.capacity()==10 );
assert( v2.getNumElements()==5 );
assert( v2==v1 );
v1.reserve(7);
assert( v1.capacity()==10 );
assert( v1.getNumElements()==5 );
v2=v1;
assert( v2.capacity()==10 );
assert( v2.getNumElements()==5 );
assert( v2==v1 );
}
// Test the insert method
{
CoinIndexedVector v1;
assert( v1.getNumElements()==0 );
assert( v1.capacity()==0 );
v1.insert(1,1.);
assert( v1.getNumElements()==1 );
assert( v1.capacity()==2 );
assert( v1.getIndices()[0] == 1 );
v1.insert(10,10.);
assert( v1.getNumElements()==2 );
assert( v1.capacity()==11 );
assert( v1.getIndices()[1] == 10 );
v1.insert(20,20.);
assert( v1.getNumElements()==3 );
assert( v1.capacity()==21 );
assert( v1.getIndices()[2] == 20 );
v1.insert(30,30.);
assert( v1.getNumElements()==4 );
assert( v1.capacity()==31 );
assert( v1.getIndices()[3] == 30 );
v1.insert(40,40.);
assert( v1.getNumElements()==5 );
assert( v1.capacity()==41 );
assert( v1.getIndices()[4] == 40 );
v1.insert(50,50.);
assert( v1.getNumElements()==6 );
assert( v1.capacity()==51 );
assert( v1.getIndices()[5] == 50 );
CoinIndexedVector v2;
const int ne1 = 3;
int inx1[ne1] = { 1, 3, 4 };
double el1[ne1] = { 1.2, 3.4, 5. };
v2.setVector(ne1,inx1,el1);
assert( v2.getNumElements()==3 );
assert( v2.capacity()==5 );
// Test clean method - get rid of 1.2
assert(v2.clean(3.0)==2);
assert(v2.denseVector()[1]==0.0);
// Below are purely for debug - so use assert
// so we won't try with false
// Test checkClean
#ifndef NO_CHECK_CL
v2.checkClean();
assert( v2.getNumElements()==2 );
// Get rid of all
int numberRemaining = v2.clean(10.0);
assert(numberRemaining==0);
v2.checkClear();
#endif
}
{
//Test setConstant and setElement
CoinIndexedVector v2;
const int ne1 = 3;
int inx1[ne1] = { 1, 3, 4 };
v2.setConstant(ne1,inx1,3.14);
assert( v2.getNumElements()==3 );
assert( v2.capacity()==5 );
assert( v2.getIndices()[0]==1 );
assert( v2.getIndices()[1]==3 );
assert( v2.getIndices()[2]==4 );
assert( v2[3] == 3.14 );
CoinIndexedVector v2X(ne1,inx1,3.14);
assert( v2 == v2X );
}
{
//Test setFull
CoinIndexedVector v2;
const int ne2 = 3;
double el2[ne2] = { 1., 3., 4. };
v2.setFull(ne2,el2);
assert( v2.getNumElements()==3 );
assert( v2.capacity()==3 );
assert( v2.getIndices()[0]==0 );
assert( v2.getIndices()[1]==1 );
assert( v2.getIndices()[2]==2 );
assert( v2[1] == 3. );
CoinIndexedVector v2X(ne2,el2);
assert( v2 == v2X );
v2.setFull(0,el2);
assert( v2[2] == 0. );
}
#if 0
// what happens when someone sets
// the number of elements to be a negative number
{
const int ne = 4;
int inx1[ne] = { 1, 3, 4, 7 };
double el1[ne] = { 1.2, 3.4, 5.6, 7.8 };
CoinIndexedVector v1;
v1.set(-ne,inx1,el1);
}
#endif
// Test copy constructor from ShallowPackedVector
{
const int ne = 4;
int inx[ne] = { 1, 4, 0, 2 };
double el[ne] = { 10., 40., 1., 50. };
CoinIndexedVector std(ne,inx,el);
CoinShallowPackedVector * spvP = new CoinShallowPackedVector(ne,inx,el);
CoinIndexedVector pv(*spvP);
assert( pv == std );
delete spvP;
assert( pv == std );
}
// Test assignment from ShallowPackedVector
{
const int ne = 4;
int inx[ne] = { 1, 4, 0, 2 };
double el[ne] = { 10., 40., 1., 50. };
CoinIndexedVector std(ne,inx,el);
CoinShallowPackedVector * spvP = new CoinShallowPackedVector(ne,inx,el);
CoinIndexedVector pv;
pv = *spvP;
assert( pv == std );
delete spvP;
assert( pv == std );
}
{
// Test that sample usage works
const int ne = 4;
int inx[ne] = { 1, 4, 0, 2 };
double el[ne] = { 10., 40., 1., 50. };
CoinIndexedVector r(ne,inx,el);
assert( r.getIndices()[0]== 1 );
assert( r.getIndices()[1]== 4 );
assert( r.getIndices()[2]== 0 );
assert( r.getIndices()[3]== 2 );
assert( r[ 0]==1. );
assert( r[ 1]==10.);
assert( r[ 2]==50.);
assert( r[ 3]==0. );
assert( r[ 4]==40.);
r.sortIncrElement();
assert( r.getIndices()[0]== 0 );
assert( r.getIndices()[1]== 1 );
assert( r.getIndices()[2]== 4 );
assert( r.getIndices()[3]== 2 );
assert( r[ 0]==1. );
assert( r[ 1]==10.);
assert( r[ 2]==50.);
assert( r[ 3]==0. );
assert( r[ 4]==40.);
CoinIndexedVector r1;
r1=r;
assert( r==r1 );
CoinIndexedVector add = r + r1;
assert( add[0] == 1.+ 1. );
assert( add[1] == 10.+10. );
assert( add[2] == 50.+50. );
assert( add[3] == 0.+ 0. );
assert( add[4] == 40.+40. );
}
}
/* Return <code>x *A</code> in <code>z</code> but
just for number indices in y.
Default cheats with fake CoinIndexedVector and
then calls subsetTransposeTimes */
void
ClpMatrixBase::listTransposeTimes(const ClpSimplex * model,
double * x,
int * y,
int number,
double * z) const
{
CoinIndexedVector pi;
CoinIndexedVector list;
CoinIndexedVector output;
int * saveIndices = list.getIndices();
list.setNumElements(number);
list.setIndexVector(y);
double * savePi = pi.denseVector();
pi.setDenseVector(x);
double * saveOutput = output.denseVector();
output.setDenseVector(z);
output.setPacked();
subsetTransposeTimes(model, &pi, &list, &output);
// restore settings
list.setIndexVector(saveIndices);
pi.setDenseVector(savePi);
output.setDenseVector(saveOutput);
}
/* Saves any weights round factorization as pivot rows may change
1) before factorization
2) after factorization
3) just redo infeasibilities
4) restore weights
*/
void
ClpDualRowSteepest::saveWeights(ClpSimplex * model, int mode)
{
// alternateWeights_ is defined as indexed but is treated oddly
model_ = model;
int numberRows = model_->numberRows();
int numberColumns = model_->numberColumns();
const int * pivotVariable = model_->pivotVariable();
int i;
if (mode == 1) {
if(weights_) {
// Check if size has changed
if (infeasible_->capacity() == numberRows) {
alternateWeights_->clear();
// change from row numbers to sequence numbers
int * which = alternateWeights_->getIndices();
for (i = 0; i < numberRows; i++) {
int iPivot = pivotVariable[i];
which[i] = iPivot;
}
state_ = 1;
} else {
// size has changed - clear everything
delete [] weights_;
weights_ = NULL;
delete [] dubiousWeights_;
dubiousWeights_ = NULL;
delete infeasible_;
infeasible_ = NULL;
delete alternateWeights_;
alternateWeights_ = NULL;
delete savedWeights_;
savedWeights_ = NULL;
state_ = -1;
}
}
} else if (mode == 2 || mode == 4 || mode >= 5) {
// restore
if (!weights_ || state_ == -1 || mode == 5) {
// initialize weights
delete [] weights_;
delete alternateWeights_;
weights_ = new double[numberRows];
alternateWeights_ = new CoinIndexedVector();
// enough space so can use it for factorization
alternateWeights_->reserve(numberRows +
model_->factorization()->maximumPivots());
if (mode_ != 1 || mode == 5) {
// initialize to 1.0 (can we do better?)
for (i = 0; i < numberRows; i++) {
weights_[i] = 1.0;
}
} else {
CoinIndexedVector * temp = new CoinIndexedVector();
temp->reserve(numberRows +
model_->factorization()->maximumPivots());
double * array = alternateWeights_->denseVector();
int * which = alternateWeights_->getIndices();
for (i = 0; i < numberRows; i++) {
double value = 0.0;
array[0] = 1.0;
which[0] = i;
alternateWeights_->setNumElements(1);
alternateWeights_->setPackedMode(true);
model_->factorization()->updateColumnTranspose(temp,
alternateWeights_);
int number = alternateWeights_->getNumElements();
int j;
for (j = 0; j < number; j++) {
value += array[j] * array[j];
array[j] = 0.0;
}
alternateWeights_->setNumElements(0);
weights_[i] = value;
}
delete temp;
}
// create saved weights (not really indexedvector)
savedWeights_ = new CoinIndexedVector();
savedWeights_->reserve(numberRows);
double * array = savedWeights_->denseVector();
int * which = savedWeights_->getIndices();
for (i = 0; i < numberRows; i++) {
array[i] = weights_[i];
which[i] = pivotVariable[i];
}
} else if (mode != 6) {
int * which = alternateWeights_->getIndices();
CoinIndexedVector * rowArray3 = model_->rowArray(3);
rowArray3->clear();
int * back = rowArray3->getIndices();
// In case something went wrong
for (i = 0; i < numberRows + numberColumns; i++)
back[i] = -1;
if (mode != 4) {
// save
CoinMemcpyN(which, numberRows, savedWeights_->getIndices());
CoinMemcpyN(weights_, numberRows, savedWeights_->denseVector());
} else {
// restore
//memcpy(which,savedWeights_->getIndices(),
// numberRows*sizeof(int));
//memcpy(weights_,savedWeights_->denseVector(),
// numberRows*sizeof(double));
which = savedWeights_->getIndices();
}
// restore (a bit slow - but only every re-factorization)
double * array = savedWeights_->denseVector();
for (i = 0; i < numberRows; i++) {
int iSeq = which[i];
back[iSeq] = i;
}
for (i = 0; i < numberRows; i++) {
int iPivot = pivotVariable[i];
iPivot = back[iPivot];
if (iPivot >= 0) {
weights_[i] = array[iPivot];
if (weights_[i] < DEVEX_TRY_NORM)
weights_[i] = DEVEX_TRY_NORM; // may need to check more
} else {
// odd
weights_[i] = 1.0;
}
}
} else {
// mode 6 - scale back weights as primal errors
double primalError = model_->largestPrimalError();
double allowed ;
if (primalError > 1.0e3)
allowed = 10.0;
else if (primalError > 1.0e2)
allowed = 50.0;
else if (primalError > 1.0e1)
allowed = 100.0;
else
allowed = 1000.0;
double allowedInv = 1.0 / allowed;
for (i = 0; i < numberRows; i++) {
double value = weights_[i];
if (value < allowedInv)
value = allowedInv;
else if (value > allowed)
value = allowed;
weights_[i] = allowed;
}
}
state_ = 0;
// set up infeasibilities
if (!infeasible_) {
infeasible_ = new CoinIndexedVector();
infeasible_->reserve(numberRows);
}
}
if (mode >= 2) {
// Get dubious weights
//if (!dubiousWeights_)
//dubiousWeights_=new int[numberRows];
//model_->factorization()->getWeights(dubiousWeights_);
infeasible_->clear();
int iRow;
const int * pivotVariable = model_->pivotVariable();
double tolerance = model_->currentPrimalTolerance();
for (iRow = 0; iRow < numberRows; iRow++) {
int iPivot = pivotVariable[iRow];
double value = model_->solution(iPivot);
double lower = model_->lower(iPivot);
double upper = model_->upper(iPivot);
if (value < lower - tolerance) {
value -= lower;
value *= value;
#ifdef CLP_DUAL_COLUMN_MULTIPLIER
if (iPivot < numberColumns)
value *= CLP_DUAL_COLUMN_MULTIPLIER; // bias towards columns
#endif
#ifdef CLP_DUAL_FIXED_COLUMN_MULTIPLIER
if (lower == upper)
value *= CLP_DUAL_FIXED_COLUMN_MULTIPLIER; // bias towards taking out fixed variables
#endif
// store square in list
infeasible_->quickAdd(iRow, value);
} else if (value > upper + tolerance) {
value -= upper;
value *= value;
#ifdef CLP_DUAL_COLUMN_MULTIPLIER
if (iPivot < numberColumns)
value *= CLP_DUAL_COLUMN_MULTIPLIER; // bias towards columns
#endif
#ifdef CLP_DUAL_FIXED_COLUMN_MULTIPLIER
if (lower == upper)
value *= CLP_DUAL_FIXED_COLUMN_MULTIPLIER; // bias towards taking out fixed variables
#endif
// store square in list
infeasible_->quickAdd(iRow, value);
}
}
}
}
/* Updates weights and returns pivot alpha.
Also does FT update */
double
ClpDualRowSteepest::updateWeights(CoinIndexedVector * input,
CoinIndexedVector * spare,
CoinIndexedVector * spare2,
CoinIndexedVector * updatedColumn)
{
//#define CLP_DEBUG 3
#if CLP_DEBUG>2
// Very expensive debug
{
int numberRows = model_->numberRows();
CoinIndexedVector * temp = new CoinIndexedVector();
temp->reserve(numberRows +
model_->factorization()->maximumPivots());
double * array = alternateWeights_->denseVector();
int * which = alternateWeights_->getIndices();
alternateWeights_->clear();
int i;
for (i = 0; i < numberRows; i++) {
double value = 0.0;
array[i] = 1.0;
which[0] = i;
alternateWeights_->setNumElements(1);
model_->factorization()->updateColumnTranspose(temp,
alternateWeights_);
int number = alternateWeights_->getNumElements();
int j;
for (j = 0; j < number; j++) {
int iRow = which[j];
value += array[iRow] * array[iRow];
array[iRow] = 0.0;
}
alternateWeights_->setNumElements(0);
double w = CoinMax(weights_[i], value) * .1;
if (fabs(weights_[i] - value) > w) {
printf("%d old %g, true %g\n", i, weights_[i], value);
weights_[i] = value; // to reduce printout
}
//else
//printf("%d matches %g\n",i,value);
}
delete temp;
}
#endif
assert (input->packedMode());
if (!updatedColumn->packedMode()) {
// I think this means empty
#ifdef COIN_DEVELOP
printf("updatedColumn not packed mode ClpDualRowSteepest::updateWeights\n");
#endif
return 0.0;
}
double alpha = 0.0;
if (!model_->factorization()->networkBasis()) {
// clear other region
alternateWeights_->clear();
double norm = 0.0;
int i;
double * work = input->denseVector();
int numberNonZero = input->getNumElements();
int * which = input->getIndices();
double * work2 = spare->denseVector();
int * which2 = spare->getIndices();
// ftran
//permute and move indices into index array
//also compute norm
//int *regionIndex = alternateWeights_->getIndices ( );
const int *permute = model_->factorization()->permute();
//double * region = alternateWeights_->denseVector();
if (permute) {
for ( i = 0; i < numberNonZero; i ++ ) {
int iRow = which[i];
double value = work[i];
norm += value * value;
iRow = permute[iRow];
work2[iRow] = value;
which2[i] = iRow;
}
} else {
for ( i = 0; i < numberNonZero; i ++ ) {
int iRow = which[i];
double value = work[i];
norm += value * value;
//iRow = permute[iRow];
work2[iRow] = value;
which2[i] = iRow;
}
}
spare->setNumElements ( numberNonZero );
// Do FT update
#if 0
ft_count_in += updatedColumn->getNumElements();
up_count_in += spare->getNumElements();
#endif
if (permute || true) {
#if CLP_DEBUG>2
printf("REGION before %d els\n", spare->getNumElements());
spare->print();
#endif
model_->factorization()->updateTwoColumnsFT(spare2, updatedColumn,
spare, permute != NULL);
#if CLP_DEBUG>2
printf("REGION after %d els\n", spare->getNumElements());
spare->print();
#endif
} else {
// Leave as old way
model_->factorization()->updateColumnFT(spare2, updatedColumn);
model_->factorization()->updateColumn(spare2, spare, false);
}
#undef CLP_DEBUG
#if 0
ft_count += updatedColumn->getNumElements();
up_count += spare->getNumElements();
xx_count++;
if ((xx_count % 1000) == 0)
printf("zz %d ft %g up %g (in %g %g)\n", xx_count, ft_count, up_count,
ft_count_in, up_count_in);
#endif
numberNonZero = spare->getNumElements();
// alternateWeights_ should still be empty
int pivotRow = model_->pivotRow();
#ifdef CLP_DEBUG
if ( model_->logLevel ( ) > 4 &&
fabs(norm - weights_[pivotRow]) > 1.0e-3 * (1.0 + norm))
printf("on row %d, true weight %g, old %g\n",
pivotRow, sqrt(norm), sqrt(weights_[pivotRow]));
#endif
// could re-initialize here (could be expensive)
norm /= model_->alpha() * model_->alpha();
assert(model_->alpha());
assert(norm);
// pivot element
alpha = 0.0;
double multiplier = 2.0 / model_->alpha();
// look at updated column
work = updatedColumn->denseVector();
numberNonZero = updatedColumn->getNumElements();
which = updatedColumn->getIndices();
int nSave = 0;
double * work3 = alternateWeights_->denseVector();
int * which3 = alternateWeights_->getIndices();
const int * pivotColumn = model_->factorization()->pivotColumn();
for (i = 0; i < numberNonZero; i++) {
int iRow = which[i];
double theta = work[i];
if (iRow == pivotRow)
alpha = theta;
double devex = weights_[iRow];
work3[nSave] = devex; // save old
which3[nSave++] = iRow;
// transform to match spare
int jRow = permute ? pivotColumn[iRow] : iRow;
double value = work2[jRow];
devex += theta * (theta * norm + value * multiplier);
if (devex < DEVEX_TRY_NORM)
devex = DEVEX_TRY_NORM;
weights_[iRow] = devex;
}
alternateWeights_->setPackedMode(true);
alternateWeights_->setNumElements(nSave);
if (norm < DEVEX_TRY_NORM)
norm = DEVEX_TRY_NORM;
// Try this to make less likely will happen again and stop cycling
//norm *= 1.02;
weights_[pivotRow] = norm;
spare->clear();
#ifdef CLP_DEBUG
spare->checkClear();
#endif
} else {
// Do FT update
model_->factorization()->updateColumnFT(spare, updatedColumn);
// clear other region
alternateWeights_->clear();
double norm = 0.0;
int i;
double * work = input->denseVector();
int number = input->getNumElements();
int * which = input->getIndices();
double * work2 = spare->denseVector();
int * which2 = spare->getIndices();
for (i = 0; i < number; i++) {
int iRow = which[i];
double value = work[i];
norm += value * value;
work2[iRow] = value;
which2[i] = iRow;
}
spare->setNumElements(number);
// ftran
#ifndef NDEBUG
alternateWeights_->checkClear();
#endif
model_->factorization()->updateColumn(alternateWeights_, spare);
// alternateWeights_ should still be empty
#ifndef NDEBUG
alternateWeights_->checkClear();
#endif
int pivotRow = model_->pivotRow();
#ifdef CLP_DEBUG
if ( model_->logLevel ( ) > 4 &&
fabs(norm - weights_[pivotRow]) > 1.0e-3 * (1.0 + norm))
printf("on row %d, true weight %g, old %g\n",
pivotRow, sqrt(norm), sqrt(weights_[pivotRow]));
#endif
// could re-initialize here (could be expensive)
norm /= model_->alpha() * model_->alpha();
assert(norm);
//if (norm < DEVEX_TRY_NORM)
//norm = DEVEX_TRY_NORM;
// pivot element
alpha = 0.0;
double multiplier = 2.0 / model_->alpha();
// look at updated column
work = updatedColumn->denseVector();
number = updatedColumn->getNumElements();
which = updatedColumn->getIndices();
int nSave = 0;
double * work3 = alternateWeights_->denseVector();
int * which3 = alternateWeights_->getIndices();
for (i = 0; i < number; i++) {
int iRow = which[i];
double theta = work[i];
if (iRow == pivotRow)
alpha = theta;
double devex = weights_[iRow];
work3[nSave] = devex; // save old
which3[nSave++] = iRow;
double value = work2[iRow];
devex += theta * (theta * norm + value * multiplier);
if (devex < DEVEX_TRY_NORM)
devex = DEVEX_TRY_NORM;
weights_[iRow] = devex;
}
if (!alpha) {
// error - but carry on
alpha = 1.0e-50;
}
alternateWeights_->setPackedMode(true);
alternateWeights_->setNumElements(nSave);
if (norm < DEVEX_TRY_NORM)
norm = DEVEX_TRY_NORM;
weights_[pivotRow] = norm;
spare->clear();
}
#ifdef CLP_DEBUG
spare->checkClear();
#endif
return alpha;
}
void CglGMI::generateCuts(OsiCuts &cs)
{
isInteger = new bool[ncol];
computeIsInteger();
cstat = new int[ncol];
rstat = new int[nrow];
solver->getBasisStatus(cstat, rstat); // 0: free 1: basic
// 2: upper 3: lower
#if defined GMI_TRACETAB
printvecINT("cstat", cstat, ncol);
printvecINT("rstat", rstat, nrow);
#endif
// list of basic integer fractional variables
int *listFracBasic = new int[nrow];
int numFracBasic = 0;
for (int i = 0; i < ncol; ++i) {
// j is the variable which is basic in row i
if ((cstat[i] == 1) && (isInteger[i])) {
if (CoinMin(aboveInteger(xlp[i]),
1-aboveInteger(xlp[i])) > param.getAway()) {
listFracBasic[numFracBasic] = i;
numFracBasic++;
}
#if defined TRACK_REJECT || defined TRACK_REJECT_SIMPLE
else if (trackRejection) {
// Say that we tried to generate a cut, but it was discarded
// because of small fractionality
if (!isIntegerValue(xlp[i])) {
fracFail++;
numGeneratedCuts++;
}
}
#endif
}
}
#if defined GMI_TRACE
printf("CglGMI::generateCuts() : %d fractional rows\n", numFracBasic);
#endif
if (numFracBasic == 0) {
delete[] listFracBasic;
delete[] cstat;
delete[] rstat;
delete[] isInteger;
return;
}
// there are rows with basic integer fractional variables, so we can
// generate cuts
// Basis index for columns and rows; each element is -1 if corresponding
// variable is nonbasic, and contains the basis index if basic.
// The basis index is the row in which the variable is basic.
int* colBasisIndex = new int[ncol];
int* rowBasisIndex = new int[nrow];
#if defined OSI_TABLEAU
memset(colBasisIndex, -1, ncol*sizeof(int));
memset(rowBasisIndex, -1, nrow*sizeof(int));
solver->enableFactorization();
int* basicVars = new int[nrow];
solver->getBasics(basicVars);
for (int i = 0; i < nrow; ++i) {
if (basicVars[i] < ncol) {
colBasisIndex[basicVars[i]] = i;
}
else {
rowBasisIndex[basicVars[i] - ncol] = i;
}
}
#else
CoinFactorization factorization;
if (factorize(factorization, colBasisIndex, rowBasisIndex)) {
printf("### WARNING: CglGMI::generateCuts(): error during factorization!\n");
return;
}
#endif
// cut in sparse form
double* cutElem = new double[ncol];
int* cutIndex = new int[ncol];
int cutNz = 0;
double cutRhs;
// cut in dense form
double* cut = new double[ncol];
double *slackVal = new double[nrow];
for (int i = 0; i < nrow; ++i) {
slackVal[i] = rowRhs[i] - rowActivity[i];
}
#if defined OSI_TABLEAU
// Column part and row part of a row of the simplex tableau
double* tableauColPart = new double[ncol];
double* tableauRowPart = new double[nrow];
#else
// Need some more data for simplex tableau computation
const int * row = byCol->getIndices();
const CoinBigIndex * columnStart = byCol->getVectorStarts();
const int * columnLength = byCol->getVectorLengths();
const double * columnElements = byCol->getElements();
// Create work arrays for factorization
// two vectors for updating: the first one is needed to do the computations
// but we do not use it, the second one contains a row of the basis inverse
CoinIndexedVector work;
CoinIndexedVector array;
// Make sure they large enough
work.reserve(nrow);
array.reserve(nrow);
int * arrayRows = array.getIndices();
double * arrayElements = array.denseVector();
// End of code to create work arrays
double one = 1.0;
#endif
// Matrix elements by row for slack substitution
const double *elements = byRow->getElements();
const int *rowStart = byRow->getVectorStarts();
const int *indices = byRow->getIndices();
const int *rowLength = byRow->getVectorLengths();
// Indices of basic and slack variables, and cut elements
int iBasic, slackIndex;
double cutCoeff;
double rowElem;
// Now generate the cuts: obtain a row of the simplex tableau
// where an integer variable is basic and fractional, and compute the cut
for (int i = 0; i < numFracBasic; ++i) {
if (!computeCutFractionality(xlp[listFracBasic[i]], cutRhs)) {
// cut is discarded because of the small fractionalities involved
#if defined TRACK_REJECT || defined TRACK_REJECT_SIMPLE
if (trackRejection) {
// Say that we tried to generate a cut, but it was discarded
// because of small fractionality
fracFail++;
numGeneratedCuts++;
}
#endif
continue;
}
// the variable listFracBasic[i] is basic in row iBasic
iBasic = colBasisIndex[listFracBasic[i]];
#if defined GMI_TRACE
printf("Row %d with var %d basic, f0 = %f\n", i, listFracBasic[i], f0);
#endif
#if defined OSI_TABLEAU
solver->getBInvARow(iBasic, tableauColPart, tableauRowPart);
#else
array.clear();
array.setVector(1, &iBasic, &one);
factorization.updateColumnTranspose (&work, &array);
int numberInArray=array.getNumElements();
#endif
// reset the cut
memset(cut, 0, ncol*sizeof(double));
// columns
for (int j = 0; j < ncol; ++j) {
if ((colBasisIndex[j] >= 0) ||
(areEqual(colLower[j], colUpper[j],
param.getEPS(), param.getEPS()))) {
// Basic or fixed variable -- skip
continue;
}
#ifdef OSI_TABLEAU
rowElem = tableauColPart[j];
#else
rowElem = 0.0;
// add in row of tableau
for (int h = columnStart[j]; h < columnStart[j]+columnLength[j]; ++h) {
rowElem += columnElements[h]*arrayElements[row[h]];
}
#endif
if (!isZero(fabs(rowElem))) {
// compute cut coefficient
flip(rowElem, j);
cutCoeff = computeCutCoefficient(rowElem, j);
if (isZero(cutCoeff)) {
continue;
}
unflipOrig(cutCoeff, j, cutRhs);
cut[j] = cutCoeff;
#if defined GMI_TRACE
printf("var %d, row %f, cut %f\n", j, rowElem, cutCoeff);
#endif
}
}
// now do slacks part
#if defined OSI_TABLEAU
for (int j = 0 ; j < nrow; ++j) {
// index of the row corresponding to the slack variable
slackIndex = j;
if (rowBasisIndex[j] >= 0) {
// Basic variable -- skip it
continue;
}
rowElem = tableauRowPart[j];
#else
for (int j = 0 ; j < numberInArray ; ++j) {
// index of the row corresponding to the slack variable
slackIndex = arrayRows[j];
rowElem = arrayElements[slackIndex];
#endif
if (!isZero(fabs(rowElem))) {
slackIndex += ncol;
// compute cut coefficient
flip(rowElem, slackIndex);
cutCoeff = computeCutCoefficient(rowElem, slackIndex);
if (isZero(fabs(cutCoeff))) {
continue;
}
unflipSlack(cutCoeff, slackIndex, cutRhs, slackVal);
eliminateSlack(cutCoeff, slackIndex, cut, cutRhs,
elements, rowStart, indices, rowLength, rowRhs);
#if defined GMI_TRACE
printf("var %d, row %f, cut %f\n", slackIndex, rowElem, cutCoeff);
#endif
}
}
packRow(cut, cutElem, cutIndex, cutNz);
if (cutNz == 0)
continue;
#if defined GMI_TRACE
printvecDBL("final cut:", cutElem, cutIndex, cutNz);
printf("cutRhs: %f\n", cutRhs);
#endif
#if defined TRACK_REJECT || defined TRACK_REJECT_SIMPLE
if (trackRejection) {
numGeneratedCuts++;
}
#endif
if (cleanCut(cutElem, cutIndex, cutNz, cutRhs, xlp) && cutNz > 0) {
OsiRowCut rc;
rc.setRow(cutNz, cutIndex, cutElem);
rc.setLb(-param.getINFINIT());
rc.setUb(cutRhs);
if (!param.getCHECK_DUPLICATES()) {
cs.insert(rc);
}
else{
cs.insertIfNotDuplicate(rc, CoinAbsFltEq(param.getEPS_COEFF()));
}
}
}
#if defined GMI_TRACE
printf("CglGMI::generateCuts() : number of cuts : %d\n", cs.sizeRowCuts());
#endif
#if defined OSI_TABLEAU
solver->disableFactorization();
delete[] basicVars;
delete[] tableauColPart;
delete[] tableauRowPart;
#endif
delete[] colBasisIndex;
delete[] rowBasisIndex;
delete[] cut;
delete[] slackVal;
delete[] cutElem;
delete[] cutIndex;
delete[] listFracBasic;
delete[] cstat;
delete[] rstat;
delete[] isInteger;
} /* generateCuts */
/***********************************************************************/
void CglGMI::setParam(const CglGMIParam &source) {
param = source;
} /* setParam */
/***********************************************************************/
void CglGMI::computeIsInteger() {
for (int i = 0; i < ncol; ++i) {
if(solver->isInteger(i)) {
isInteger[i] = true;
}
else {
if((areEqual(colLower[i], colUpper[i],
param.getEPS(), param.getEPS()))
&& (isIntegerValue(colUpper[i]))) {
// continuous variable fixed to an integer value
isInteger[i] = true;
}
else {
isInteger[i] = false;
}
}
}
} /* computeIsInteger */
/***********************************************************************/
void CglGMI::printOptTab(OsiSolverInterface *lclSolver) const
{
int *cstat = new int[ncol];
int *rstat = new int[nrow];
lclSolver->enableFactorization();
lclSolver->getBasisStatus(cstat, rstat); // 0: free 1: basic
// 2: upper 3: lower
int *basisIndex = new int[nrow]; // basisIndex[i] =
// index of pivot var in row i
// (slack if number >= ncol)
lclSolver->getBasics(basisIndex);
double *z = new double[ncol]; // workspace to get row of the tableau
double *slack = new double[nrow]; // workspace to get row of the tableau
double *slackVal = new double[nrow];
for (int i = 0; i < nrow; i++) {
slackVal[i] = rowRhs[i] - rowActivity[i];
}
const double *rc = lclSolver->getReducedCost();
const double *dual = lclSolver->getRowPrice();
const double *solution = lclSolver->getColSolution();
printvecINT("cstat", cstat, ncol);
printvecINT("rstat", rstat, nrow);
printvecINT("basisIndex", basisIndex, nrow);
printvecDBL("solution", solution, ncol);
printvecDBL("slackVal", slackVal, nrow);
printvecDBL("reduced_costs", rc, ncol);
printvecDBL("dual solution", dual, nrow);
printf("Optimal Tableau:\n");
for (int i = 0; i < nrow; i++) {
lclSolver->getBInvARow(i, z, slack);
for (int ii = 0; ii < ncol; ++ii) {
printf("%5.2f ", z[ii]);
}
printf(" | ");
for (int ii = 0; ii < nrow; ++ii) {
printf("%5.2f ", slack[ii]);
}
printf(" | ");
if(basisIndex[i] < ncol) {
printf("%5.2f ", solution[basisIndex[i]]);
}
else {
printf("%5.2f ", slackVal[basisIndex[i]-ncol]);
}
printf("\n");
}
for (int ii = 0; ii < 7*(ncol+nrow+1); ++ii) {
printf("-");
}
printf("\n");
for (int ii = 0; ii < ncol; ++ii) {
printf("%5.2f ", rc[ii]);
}
printf(" | ");
for (int ii = 0; ii < nrow; ++ii) {
printf("%5.2f ", -dual[ii]);
}
printf(" | ");
printf("%5.2f\n", -lclSolver->getObjValue());
lclSolver->disableFactorization();
delete[] cstat;
delete[] rstat;
delete[] basisIndex;
delete[] slack;
delete[] z;
delete[] slackVal;
} /* printOptTab */