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ClpSimplexDual.cpp
8065 lines (7839 loc) · 347 KB
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ClpSimplexDual.cpp
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/* $Id$ */
// Copyright (C) 2002, International Business Machines
// Corporation and others. All Rights Reserved.
// This code is licensed under the terms of the Eclipse Public License (EPL).
/* Notes on implementation of dual simplex algorithm.
When dual feasible:
If primal feasible, we are optimal. Otherwise choose an infeasible
basic variable to leave basis (normally going to nearest bound) (B). We
now need to find an incoming variable which will leave problem
dual feasible so we get the row of the tableau corresponding to
the basic variable (with the correct sign depending if basic variable
above or below feasibility region - as that affects whether reduced
cost on outgoing variable has to be positive or negative).
We now perform a ratio test to determine which incoming variable will
preserve dual feasibility (C). If no variable found then problem
is infeasible (in primal sense). If there is a variable, we then
perform pivot and repeat. Trivial?
-------------------------------------------
A) How do we get dual feasible? If all variables have bounds then
it is trivial to get feasible by putting non-basic variables to
correct bounds. OSL did not have a phase 1/phase 2 approach but
instead effectively put fake bounds on variables and this is the
approach here, although I had hoped to make it cleaner.
If there is a weight of X on getting dual feasible:
Non-basic variables with negative reduced costs are put to
lesser of their upper bound and their lower bound + X.
Similarly, mutatis mutandis, for positive reduced costs.
Free variables should normally be in basis, otherwise I have
coding which may be able to come out (and may not be correct).
In OSL, this weight was changed heuristically, here at present
it is only increased if problem looks finished. If problem is
feasible I check for unboundedness. If not unbounded we
could play with going into primal. As long as weights increase
any algorithm would be finite.
B) Which outgoing variable to choose is a virtual base class.
For difficult problems steepest edge is preferred while for
very easy (large) problems we will need partial scan.
C) Sounds easy, but this is hardest part of algorithm.
1) Instead of stopping at first choice, we may be able
to flip that variable to other bound and if objective
still improving choose again. These mini iterations can
increase speed by orders of magnitude but we may need to
go to more of a bucket choice of variable rather than looking
at them one by one (for speed).
2) Accuracy. Reduced costs may be of wrong sign but less than
tolerance. Pivoting on these makes objective go backwards.
OSL modified cost so a zero move was made, Gill et al
(in primal analogue) modified so a strictly positive move was
made. It is not quite as neat in dual but that is what we
try and do. The two problems are that re-factorizations can
change reduced costs above and below tolerances and that when
finished we need to reset costs and try again.
3) Degeneracy. Gill et al helps but may not be enough. We
may need more. Also it can improve speed a lot if we perturb
the costs significantly.
References:
Forrest and Goldfarb, Steepest-edge simplex algorithms for
linear programming - Mathematical Programming 1992
Forrest and Tomlin, Implementing the simplex method for
the Optimization Subroutine Library - IBM Systems Journal 1992
Gill, Murray, Saunders, Wright A Practical Anti-Cycling
Procedure for Linear and Nonlinear Programming SOL report 1988
TODO:
a) Better recovery procedures. At present I never check on forward
progress. There is checkpoint/restart with reducing
re-factorization frequency, but this is only on singular
factorizations.
b) Fast methods for large easy problems (and also the option for
the code to automatically choose which method).
c) We need to be able to stop in various ways for OSI - this
is fairly easy.
*/
#ifdef COIN_DEVELOP
#undef COIN_DEVELOP
#define COIN_DEVELOP 2
#endif
#include "CoinPragma.hpp"
#include <math.h>
#include "CoinHelperFunctions.hpp"
#include "ClpHelperFunctions.hpp"
#include "ClpSimplexDual.hpp"
#include "ClpEventHandler.hpp"
#include "ClpFactorization.hpp"
#include "CoinPackedMatrix.hpp"
#include "CoinIndexedVector.hpp"
#include "CoinFloatEqual.hpp"
#include "ClpDualRowDantzig.hpp"
#include "ClpMessage.hpp"
#include "ClpLinearObjective.hpp"
#include <cfloat>
#include <cassert>
#include <string>
#include <stdio.h>
#include <iostream>
//#define CLP_DEBUG 1
// To force to follow another run put logfile name here and define
//#define FORCE_FOLLOW
#ifdef FORCE_FOLLOW
static FILE * fpFollow = NULL;
static char * forceFile = "old.log";
static int force_in = -1;
static int force_out = -1;
static int force_iteration = 0;
#endif
//#define VUB
#ifdef VUB
extern int * vub;
extern int * toVub;
extern int * nextDescendent;
#endif
#ifdef NDEBUG
#define NDEBUG_CLP
#endif
#ifndef CLP_INVESTIGATE
#define NDEBUG_CLP
#endif
// dual
/* *** Method
This is a vanilla version of dual simplex.
It tries to be a single phase approach with a weight of 1.0 being
given to getting optimal and a weight of dualBound_ being
given to getting dual feasible. In this version I have used the
idea that this weight can be thought of as a fake bound. If the
distance between the lower and upper bounds on a variable is less
than the feasibility weight then we are always better off flipping
to other bound to make dual feasible. If the distance is greater
then we make up a fake bound dualBound_ away from one bound.
If we end up optimal or primal infeasible, we check to see if
bounds okay. If so we have finished, if not we increase dualBound_
and continue (after checking if unbounded). I am undecided about
free variables - there is coding but I am not sure about it. At
present I put them in basis anyway.
The code is designed to take advantage of sparsity so arrays are
seldom zeroed out from scratch or gone over in their entirety.
The only exception is a full scan to find outgoing variable. This
will be changed to keep an updated list of infeasibilities (or squares
if steepest edge). Also on easy problems we don't need full scan - just
pick first reasonable.
One problem is how to tackle degeneracy and accuracy. At present
I am using the modification of costs which I put in OSL and which was
extended by Gill et al. I am still not sure of the exact details.
The flow of dual is three while loops as follows:
while (not finished) {
while (not clean solution) {
Factorize and/or clean up solution by flipping variables so
dual feasible. If looks finished check fake dual bounds.
Repeat until status is iterating (-1) or finished (0,1,2)
}
while (status==-1) {
Iterate until no pivot in or out or time to re-factorize.
Flow is:
choose pivot row (outgoing variable). if none then
we are primal feasible so looks as if done but we need to
break and check bounds etc.
Get pivot row in tableau
Choose incoming column. If we don't find one then we look
primal infeasible so break and check bounds etc. (Also the
pivot tolerance is larger after any iterations so that may be
reason)
If we do find incoming column, we may have to adjust costs to
keep going forwards (anti-degeneracy). Check pivot will be stable
and if unstable throw away iteration (we will need to implement
flagging of basic variables sometime) and break to re-factorize.
If minor error re-factorize after iteration.
Update everything (this may involve flipping variables to stay
dual feasible.
}
}
At present we never check we are going forwards. I overdid that in
OSL so will try and make a last resort.
Needs partial scan pivot out option.
Needs dantzig, uninitialized and full steepest edge options (can still
use partial scan)
May need other anti-degeneracy measures, especially if we try and use
loose tolerances as a way to solve in fewer iterations.
I like idea of dynamic scaling. This gives opportunity to decouple
different implications of scaling for accuracy, iteration count and
feasibility tolerance.
*/
#define CLEAN_FIXED 0
// Startup part of dual (may be extended to other algorithms)
int
ClpSimplexDual::startupSolve(int ifValuesPass, double * saveDuals, int startFinishOptions)
{
// If values pass then save given duals round check solution
// sanity check
// initialize - no values pass and algorithm_ is -1
// put in standard form (and make row copy)
// create modifiable copies of model rim and do optional scaling
// If problem looks okay
// Do initial factorization
// If user asked for perturbation - do it
numberFake_ = 0; // Number of variables at fake bounds
numberChanged_ = 0; // Number of variables with changed costs
if (!startup(0, startFinishOptions)) {
int usePrimal = 0;
// looks okay
// Superbasic variables not allowed
// If values pass then scale pi
if (ifValuesPass) {
if (problemStatus_ && perturbation_ < 100)
usePrimal = perturb();
int i;
if (scalingFlag_ > 0) {
for (i = 0; i < numberRows_; i++) {
dual_[i] = saveDuals[i] * inverseRowScale_[i];
}
} else {
CoinMemcpyN(saveDuals, numberRows_, dual_);
}
// now create my duals
for (i = 0; i < numberRows_; i++) {
// slack
double value = dual_[i];
value += rowObjectiveWork_[i];
saveDuals[i+numberColumns_] = value;
}
CoinMemcpyN(objectiveWork_, numberColumns_, saveDuals);
transposeTimes(-1.0, dual_, saveDuals);
// make reduced costs okay
for (i = 0; i < numberColumns_; i++) {
if (getStatus(i) == atLowerBound) {
if (saveDuals[i] < 0.0) {
//if (saveDuals[i]<-1.0e-3)
//printf("bad dj at lb %d %g\n",i,saveDuals[i]);
saveDuals[i] = 0.0;
}
} else if (getStatus(i) == atUpperBound) {
if (saveDuals[i] > 0.0) {
//if (saveDuals[i]>1.0e-3)
//printf("bad dj at ub %d %g\n",i,saveDuals[i]);
saveDuals[i] = 0.0;
}
}
}
CoinMemcpyN(saveDuals, (numberColumns_ + numberRows_), dj_);
// set up possible ones
for (i = 0; i < numberRows_ + numberColumns_; i++)
clearPivoted(i);
int iRow;
for (iRow = 0; iRow < numberRows_; iRow++) {
int iPivot = pivotVariable_[iRow];
if (fabs(saveDuals[iPivot]) > dualTolerance_) {
if (getStatus(iPivot) != isFree)
setPivoted(iPivot);
}
}
} else if ((specialOptions_ & 1024) != 0 && CLEAN_FIXED) {
// set up possible ones
for (int i = 0; i < numberRows_ + numberColumns_; i++)
clearPivoted(i);
int iRow;
for (iRow = 0; iRow < numberRows_; iRow++) {
int iPivot = pivotVariable_[iRow];
if (iPivot < numberColumns_ && lower_[iPivot] == upper_[iPivot]) {
setPivoted(iPivot);
}
}
}
double objectiveChange;
assert (!numberFake_);
assert (numberChanged_ == 0);
if (!numberFake_) // if nonzero then adjust
changeBounds(1, NULL, objectiveChange);
if (!ifValuesPass) {
// Check optimal
if (!numberDualInfeasibilities_ && !numberPrimalInfeasibilities_)
problemStatus_ = 0;
}
if (problemStatus_ < 0 && perturbation_ < 100) {
bool inCbcOrOther = (specialOptions_ & 0x03000000) != 0;
if (!inCbcOrOther)
usePrimal = perturb();
// Can't get here if values pass
gutsOfSolution(NULL, NULL);
#ifdef CLP_INVESTIGATE
if (numberDualInfeasibilities_)
printf("ZZZ %d primal %d dual - sumdinf %g\n",
numberPrimalInfeasibilities_,
numberDualInfeasibilities_, sumDualInfeasibilities_);
#endif
if (handler_->logLevel() > 2) {
handler_->message(CLP_SIMPLEX_STATUS, messages_)
<< numberIterations_ << objectiveValue();
handler_->printing(sumPrimalInfeasibilities_ > 0.0)
<< sumPrimalInfeasibilities_ << numberPrimalInfeasibilities_;
handler_->printing(sumDualInfeasibilities_ > 0.0)
<< sumDualInfeasibilities_ << numberDualInfeasibilities_;
handler_->printing(numberDualInfeasibilitiesWithoutFree_
< numberDualInfeasibilities_)
<< numberDualInfeasibilitiesWithoutFree_;
handler_->message() << CoinMessageEol;
}
if (inCbcOrOther) {
if (numberPrimalInfeasibilities_) {
usePrimal = perturb();
if (perturbation_ >= 101) {
computeDuals(NULL);
//gutsOfSolution(NULL,NULL);
checkDualSolution(); // recompute objective
}
} else if (numberDualInfeasibilities_) {
problemStatus_ = 10;
if ((moreSpecialOptions_ & 32) != 0 && false)
problemStatus_ = 0; // say optimal!!
#if COIN_DEVELOP>2
printf("returning at %d\n", __LINE__);
#endif
return 1; // to primal
}
}
} else if (!ifValuesPass) {
gutsOfSolution(NULL, NULL);
// double check
if (numberDualInfeasibilities_ || numberPrimalInfeasibilities_)
problemStatus_ = -1;
}
if (usePrimal) {
problemStatus_ = 10;
#if COIN_DEVELOP>2
printf("returning to use primal (no obj) at %d\n", __LINE__);
#endif
}
return usePrimal;
} else {
return 1;
}
}
void
ClpSimplexDual::finishSolve(int startFinishOptions)
{
assert (problemStatus_ || !sumPrimalInfeasibilities_);
// clean up
finish(startFinishOptions);
}
//#define CLP_REPORT_PROGRESS
#ifdef CLP_REPORT_PROGRESS
static int ixxxxxx = 0;
static int ixxyyyy = 90;
#endif
#ifdef CLP_INVESTIGATE_SERIAL
static int z_reason[7] = {0, 0, 0, 0, 0, 0, 0};
static int z_thinks = -1;
#endif
void
ClpSimplexDual::gutsOfDual(int ifValuesPass, double * & saveDuals, int initialStatus,
ClpDataSave & data)
{
#ifdef CLP_INVESTIGATE_SERIAL
z_reason[0]++;
z_thinks = -1;
int nPivots = 9999;
#endif
double largestPrimalError = 0.0;
double largestDualError = 0.0;
double smallestPrimalInfeasibility=COIN_DBL_MAX;
int numberRayTries=0;
// Start can skip some things in transposeTimes
specialOptions_ |= 131072;
int lastCleaned = 0; // last time objective or bounds cleaned up
// This says whether to restore things etc
// startup will have factorized so can skip
int factorType = 0;
// Start check for cycles
progress_.startCheck();
// Say change made on first iteration
changeMade_ = 1;
// Say last objective infinite
//lastObjectiveValue_=-COIN_DBL_MAX;
progressFlag_ = 0;
/*
Status of problem:
0 - optimal
1 - infeasible
2 - unbounded
-1 - iterating
-2 - factorization wanted
-3 - redo checking without factorization
-4 - looks infeasible
*/
while (problemStatus_ < 0) {
int iRow, iColumn;
// clear
for (iRow = 0; iRow < 4; iRow++) {
rowArray_[iRow]->clear();
}
for (iColumn = 0; iColumn < 2; iColumn++) {
columnArray_[iColumn]->clear();
}
// give matrix (and model costs and bounds a chance to be
// refreshed (normally null)
matrix_->refresh(this);
// If getting nowhere - why not give it a kick
// does not seem to work too well - do some more work
if (perturbation_ < 101 && numberIterations_ > 2 * (numberRows_ + numberColumns_) && (moreSpecialOptions_&1048576)==0
&& initialStatus != 10) {
perturb();
// Can't get here if values pass
gutsOfSolution(NULL, NULL);
if (handler_->logLevel() > 2) {
handler_->message(CLP_SIMPLEX_STATUS, messages_)
<< numberIterations_ << objectiveValue();
handler_->printing(sumPrimalInfeasibilities_ > 0.0)
<< sumPrimalInfeasibilities_ << numberPrimalInfeasibilities_;
handler_->printing(sumDualInfeasibilities_ > 0.0)
<< sumDualInfeasibilities_ << numberDualInfeasibilities_;
handler_->printing(numberDualInfeasibilitiesWithoutFree_
< numberDualInfeasibilities_)
<< numberDualInfeasibilitiesWithoutFree_;
handler_->message() << CoinMessageEol;
}
}
// see if in Cbc etc
bool inCbcOrOther = (specialOptions_ & 0x03000000) != 0;
#if 0
bool gotoPrimal = false;
if (inCbcOrOther && numberIterations_ > disasterArea_ + numberRows_ &&
numberDualInfeasibilitiesWithoutFree_ && largestDualError_ > 1.0e-1) {
if (!disasterArea_) {
printf("trying all slack\n");
// try all slack basis
allSlackBasis(true);
disasterArea_ = 2 * numberRows_;
} else {
printf("going to primal\n");
// go to primal
gotoPrimal = true;
allSlackBasis(true);
}
}
#endif
bool disaster = false;
if (disasterArea_ && inCbcOrOther && disasterArea_->check()) {
disasterArea_->saveInfo();
disaster = true;
}
// may factorize, checks if problem finished
statusOfProblemInDual(lastCleaned, factorType, saveDuals, data,
ifValuesPass);
smallestPrimalInfeasibility=CoinMin(smallestPrimalInfeasibility,
sumPrimalInfeasibilities_);
if (sumPrimalInfeasibilities_>1.0e5 &&
sumPrimalInfeasibilities_>1.0e5*smallestPrimalInfeasibility &&
(moreSpecialOptions_&256)==0 &&
progress_.lastObjective(0)<-1.0e10 &&
progress_.lastObjective(1)>-1.0e5) {
// problems - try dual
problemStatus_=10;
// mark as large infeasibility cost wanted
sumPrimalInfeasibilities_ = -123456789.0;
//for (int i=0;i<numberRows_+numberColumns_;i++) {
//if (fabs(cost_[i]*solution_[i])>1.0e4)
// printf("col %d cost %g sol %g bounds %g %g\n",
// i,cost_[i],solution_[i],lower_[i],upper_[i]);
//}
}
if ((specialOptions_&2097152)!=0&&problemStatus_==1&&!ray_&&
!numberRayTries && numberIterations_) {
numberRayTries=1;
problemStatus_=-1;
}
largestPrimalError = CoinMax(largestPrimalError, largestPrimalError_);
largestDualError = CoinMax(largestDualError, largestDualError_);
if (disaster)
problemStatus_ = 3;
// If values pass then do easy ones on first time
if (ifValuesPass &&
progress_.lastIterationNumber(0) < 0 && saveDuals) {
doEasyOnesInValuesPass(saveDuals);
}
// Say good factorization
factorType = 1;
if (data.sparseThreshold_) {
// use default at present
factorization_->sparseThreshold(0);
factorization_->goSparse();
}
// exit if victory declared
if (problemStatus_ >= 0)
break;
// test for maximum iterations
if (hitMaximumIterations() || (ifValuesPass == 2 && !saveDuals)) {
problemStatus_ = 3;
break;
}
if (ifValuesPass && !saveDuals) {
// end of values pass
ifValuesPass = 0;
int status = eventHandler_->event(ClpEventHandler::endOfValuesPass);
if (status >= 0) {
problemStatus_ = 5;
secondaryStatus_ = ClpEventHandler::endOfValuesPass;
break;
}
}
// Check event
{
int status = eventHandler_->event(ClpEventHandler::endOfFactorization);
if (status >= 0) {
problemStatus_ = 5;
secondaryStatus_ = ClpEventHandler::endOfFactorization;
break;
}
}
// If looks odd try other way
if ((moreSpecialOptions_&256)==0 &&
fabs(objectiveValue_)>1.0e20&&sumDualInfeasibilities_>1.0
&&problemStatus_<0) {
problemStatus_=10;
break;
}
// Do iterations
int returnCode = whileIterating(saveDuals, ifValuesPass);
if (problemStatus_ == 1 && (progressFlag_&8) != 0 &&
fabs(objectiveValue_) > 1.0e10 )
problemStatus_ = 10; // infeasible - but has looked feasible
#ifdef CLP_INVESTIGATE_SERIAL
nPivots = factorization_->pivots();
#endif
if (!problemStatus_ && factorization_->pivots())
computeDuals(NULL); // need to compute duals
if (returnCode == -2)
factorType = 3;
}
#ifdef CLP_INVESTIGATE_SERIAL
// NOTE - can fail if parallel
if (z_thinks != -1) {
assert (z_thinks < 4);
if ((!factorization_->pivots() && nPivots < 20) && z_thinks >= 0 && z_thinks < 2)
z_thinks += 4;
z_reason[1+z_thinks]++;
}
if ((z_reason[0] % 1000) == 0) {
printf("Reason");
for (int i = 0; i < 7; i++)
printf(" %d", z_reason[i]);
printf("\n");
}
#endif
// Stop can skip some things in transposeTimes
specialOptions_ &= ~131072;
largestPrimalError_ = largestPrimalError;
largestDualError_ = largestDualError;
}
int
ClpSimplexDual::dual(int ifValuesPass, int startFinishOptions)
{
//handler_->setLogLevel(63);
//yprintf("STARTing dual %d rows\n",numberRows_);
bestObjectiveValue_ = -COIN_DBL_MAX;
algorithm_ = -1;
moreSpecialOptions_ &= ~16; // clear check replaceColumn accuracy
// save data
ClpDataSave data = saveData();
double * saveDuals = NULL;
int saveDont = dontFactorizePivots_;
if ((specialOptions_ & 2048) == 0)
dontFactorizePivots_ = 0;
else if(!dontFactorizePivots_)
dontFactorizePivots_ = 20;
if (ifValuesPass) {
saveDuals = new double [numberRows_+numberColumns_];
CoinMemcpyN(dual_, numberRows_, saveDuals);
}
if (alphaAccuracy_ != -1.0)
alphaAccuracy_ = 1.0;
minimumPrimalTolerance_=primalTolerance();
int returnCode = startupSolve(ifValuesPass, saveDuals, startFinishOptions);
// Save so can see if doing after primal
int initialStatus = problemStatus_;
if (!returnCode && !numberDualInfeasibilities_ &&
!numberPrimalInfeasibilities_ && perturbation_ < 101) {
returnCode = 1; // to skip gutsOfDual
problemStatus_ = 0;
}
if (!returnCode)
gutsOfDual(ifValuesPass, saveDuals, initialStatus, data);
if (!problemStatus_) {
// see if cutoff reached
double limit = 0.0;
getDblParam(ClpDualObjectiveLimit, limit);
if(fabs(limit) < 1.0e30 && objectiveValue()*optimizationDirection_ >
limit + 1.0e-7 + 1.0e-8 * fabs(limit)) {
// actually infeasible on objective
problemStatus_ = 1;
secondaryStatus_ = 1;
}
}
// If infeasible but primal errors - try dual
if (problemStatus_==1 && numberPrimalInfeasibilities_) {
bool inCbcOrOther = (specialOptions_ & 0x03000000) != 0;
double factor = (!inCbcOrOther) ? 1.0 : 0.3;
double averageInfeasibility = sumPrimalInfeasibilities_/
static_cast<double>(numberPrimalInfeasibilities_);
if (averageInfeasibility<factor*largestPrimalError_)
problemStatus_= 10;
}
if (problemStatus_ == 10)
startFinishOptions |= 1;
finishSolve(startFinishOptions);
delete [] saveDuals;
// Restore any saved stuff
restoreData(data);
dontFactorizePivots_ = saveDont;
if (problemStatus_ == 3)
objectiveValue_ = CoinMax(bestObjectiveValue_, objectiveValue_ - bestPossibleImprovement_);
return problemStatus_;
}
// old way
#if 0
int ClpSimplexDual::dual (int ifValuesPass , int startFinishOptions)
{
algorithm_ = -1;
// save data
ClpDataSave data = saveData();
// Save so can see if doing after primal
int initialStatus = problemStatus_;
// If values pass then save given duals round check solution
double * saveDuals = NULL;
if (ifValuesPass) {
saveDuals = new double [numberRows_+numberColumns_];
CoinMemcpyN(dual_, numberRows_, saveDuals);
}
// sanity check
// initialize - no values pass and algorithm_ is -1
// put in standard form (and make row copy)
// create modifiable copies of model rim and do optional scaling
// If problem looks okay
// Do initial factorization
// If user asked for perturbation - do it
if (!startup(0, startFinishOptions)) {
// looks okay
// Superbasic variables not allowed
// If values pass then scale pi
if (ifValuesPass) {
if (problemStatus_ && perturbation_ < 100)
perturb();
int i;
if (scalingFlag_ > 0) {
for (i = 0; i < numberRows_; i++) {
dual_[i] = saveDuals[i] * inverseRowScale_[i];
}
} else {
CoinMemcpyN(saveDuals, numberRows_, dual_);
}
// now create my duals
for (i = 0; i < numberRows_; i++) {
// slack
double value = dual_[i];
value += rowObjectiveWork_[i];
saveDuals[i+numberColumns_] = value;
}
CoinMemcpyN(objectiveWork_, numberColumns_, saveDuals);
transposeTimes(-1.0, dual_, saveDuals);
// make reduced costs okay
for (i = 0; i < numberColumns_; i++) {
if (getStatus(i) == atLowerBound) {
if (saveDuals[i] < 0.0) {
//if (saveDuals[i]<-1.0e-3)
//printf("bad dj at lb %d %g\n",i,saveDuals[i]);
saveDuals[i] = 0.0;
}
} else if (getStatus(i) == atUpperBound) {
if (saveDuals[i] > 0.0) {
//if (saveDuals[i]>1.0e-3)
//printf("bad dj at ub %d %g\n",i,saveDuals[i]);
saveDuals[i] = 0.0;
}
}
}
CoinMemcpyN(saveDuals, numberColumns_ + numberRows_, dj_);
// set up possible ones
for (i = 0; i < numberRows_ + numberColumns_; i++)
clearPivoted(i);
int iRow;
for (iRow = 0; iRow < numberRows_; iRow++) {
int iPivot = pivotVariable_[iRow];
if (fabs(saveDuals[iPivot]) > dualTolerance_) {
if (getStatus(iPivot) != isFree)
setPivoted(iPivot);
}
}
} else if ((specialOptions_ & 1024) != 0 && CLEAN_FIXED) {
// set up possible ones
for (int i = 0; i < numberRows_ + numberColumns_; i++)
clearPivoted(i);
int iRow;
for (iRow = 0; iRow < numberRows_; iRow++) {
int iPivot = pivotVariable_[iRow];
if (iPivot < numberColumns_ && lower_[iPivot] == upper_[iPivot]) {
setPivoted(iPivot);
}
}
}
double objectiveChange;
numberFake_ = 0; // Number of variables at fake bounds
numberChanged_ = 0; // Number of variables with changed costs
changeBounds(1, NULL, objectiveChange);
int lastCleaned = 0; // last time objective or bounds cleaned up
if (!ifValuesPass) {
// Check optimal
if (!numberDualInfeasibilities_ && !numberPrimalInfeasibilities_)
problemStatus_ = 0;
}
if (problemStatus_ < 0 && perturbation_ < 100) {
perturb();
// Can't get here if values pass
gutsOfSolution(NULL, NULL);
if (handler_->logLevel() > 2) {
handler_->message(CLP_SIMPLEX_STATUS, messages_)
<< numberIterations_ << objectiveValue();
handler_->printing(sumPrimalInfeasibilities_ > 0.0)
<< sumPrimalInfeasibilities_ << numberPrimalInfeasibilities_;
handler_->printing(sumDualInfeasibilities_ > 0.0)
<< sumDualInfeasibilities_ << numberDualInfeasibilities_;
handler_->printing(numberDualInfeasibilitiesWithoutFree_
< numberDualInfeasibilities_)
<< numberDualInfeasibilitiesWithoutFree_;
handler_->message() << CoinMessageEol;
}
}
// This says whether to restore things etc
// startup will have factorized so can skip
int factorType = 0;
// Start check for cycles
progress_.startCheck();
// Say change made on first iteration
changeMade_ = 1;
/*
Status of problem:
0 - optimal
1 - infeasible
2 - unbounded
-1 - iterating
-2 - factorization wanted
-3 - redo checking without factorization
-4 - looks infeasible
*/
while (problemStatus_ < 0) {
int iRow, iColumn;
// clear
for (iRow = 0; iRow < 4; iRow++) {
rowArray_[iRow]->clear();
}
for (iColumn = 0; iColumn < 2; iColumn++) {
columnArray_[iColumn]->clear();
}
// give matrix (and model costs and bounds a chance to be
// refreshed (normally null)
matrix_->refresh(this);
// If getting nowhere - why not give it a kick
// does not seem to work too well - do some more work
if (perturbation_ < 101 && numberIterations_ > 2 * (numberRows_ + numberColumns_) && (moreSpecialOptions_&1048576)==0
&& initialStatus != 10) {
perturb();
// Can't get here if values pass
gutsOfSolution(NULL, NULL);
if (handler_->logLevel() > 2) {
handler_->message(CLP_SIMPLEX_STATUS, messages_)
<< numberIterations_ << objectiveValue();
handler_->printing(sumPrimalInfeasibilities_ > 0.0)
<< sumPrimalInfeasibilities_ << numberPrimalInfeasibilities_;
handler_->printing(sumDualInfeasibilities_ > 0.0)
<< sumDualInfeasibilities_ << numberDualInfeasibilities_;
handler_->printing(numberDualInfeasibilitiesWithoutFree_
< numberDualInfeasibilities_)
<< numberDualInfeasibilitiesWithoutFree_;
handler_->message() << CoinMessageEol;
}
}
// may factorize, checks if problem finished
statusOfProblemInDual(lastCleaned, factorType, saveDuals, data,
ifValuesPass);
// If values pass then do easy ones on first time
if (ifValuesPass &&
progress_.lastIterationNumber(0) < 0 && saveDuals) {
doEasyOnesInValuesPass(saveDuals);
}
// Say good factorization
factorType = 1;
if (data.sparseThreshold_) {
// use default at present
factorization_->sparseThreshold(0);
factorization_->goSparse();
}
// exit if victory declared
if (problemStatus_ >= 0)
break;
// test for maximum iterations
if (hitMaximumIterations() || (ifValuesPass == 2 && !saveDuals)) {
problemStatus_ = 3;
break;
}
if (ifValuesPass && !saveDuals) {
// end of values pass
ifValuesPass = 0;
int status = eventHandler_->event(ClpEventHandler::endOfValuesPass);
if (status >= 0) {
problemStatus_ = 5;
secondaryStatus_ = ClpEventHandler::endOfValuesPass;
break;
}
}
// Check event
{
int status = eventHandler_->event(ClpEventHandler::endOfFactorization);
if (status >= 0) {
problemStatus_ = 5;
secondaryStatus_ = ClpEventHandler::endOfFactorization;
break;
}
}
// Do iterations
whileIterating(saveDuals, ifValuesPass);
}
}
assert (problemStatus_ || !sumPrimalInfeasibilities_);
// clean up
finish(startFinishOptions);
delete [] saveDuals;
// Restore any saved stuff
restoreData(data);
return problemStatus_;
}
#endif
//#define CHECK_ACCURACY
#ifdef CHECK_ACCURACY
static double zzzzzz[100000];
#endif
/* Reasons to come out:
-1 iterations etc
-2 inaccuracy
-3 slight inaccuracy (and done iterations)
+0 looks optimal (might be unbounded - but we will investigate)
+1 looks infeasible
+3 max iterations
*/
int
ClpSimplexDual::whileIterating(double * & givenDuals, int ifValuesPass)
{
#ifdef CLP_INVESTIGATE_SERIAL
z_thinks = -1;
#endif
#ifdef CLP_DEBUG
int debugIteration = -1;
#endif
{
int i;
for (i = 0; i < 4; i++) {
rowArray_[i]->clear();
}
for (i = 0; i < 2; i++) {
columnArray_[i]->clear();
}
}
#ifdef CLP_REPORT_PROGRESS
double * savePSol = new double [numberRows_+numberColumns_];
double * saveDj = new double [numberRows_+numberColumns_];
double * saveCost = new double [numberRows_+numberColumns_];
unsigned char * saveStat = new unsigned char [numberRows_+numberColumns_];
#endif
// if can't trust much and long way from optimal then relax
if (largestPrimalError_ > 10.0)
factorization_->relaxAccuracyCheck(CoinMin(1.0e2, largestPrimalError_ / 10.0));
else
factorization_->relaxAccuracyCheck(1.0);
// status stays at -1 while iterating, >=0 finished, -2 to invert
// status -3 to go to top without an invert
int returnCode = -1;
double saveSumDual = sumDualInfeasibilities_; // so we know to be careful
#if 0
// compute average infeasibility for backward test
double averagePrimalInfeasibility = sumPrimalInfeasibilities_ /
((double ) (numberPrimalInfeasibilities_ + 1));
#endif
// Get dubious weights
CoinBigIndex * dubiousWeights = NULL;
#ifdef DUBIOUS_WEIGHTS
factorization_->getWeights(rowArray_[0]->getIndices());
dubiousWeights = matrix_->dubiousWeights(this, rowArray_[0]->getIndices());
#endif
// If values pass then get list of candidates
int * candidateList = NULL;
int numberCandidates = 0;
#ifdef CLP_DEBUG
bool wasInValuesPass = (givenDuals != NULL);
#endif
int candidate = -1;
if (givenDuals) {
assert (ifValuesPass);
ifValuesPass = 1;
candidateList = new int[numberRows_];
// move reduced costs across
CoinMemcpyN(givenDuals, numberRows_ + numberColumns_, dj_);
int iRow;
for (iRow = 0; iRow < numberRows_; iRow++) {
int iPivot = pivotVariable_[iRow];
if (flagged(iPivot))
continue;
if (fabs(dj_[iPivot]) > dualTolerance_) {
// for now safer to ignore free ones
if (lower_[iPivot] > -1.0e50 || upper_[iPivot] < 1.0e50)
if (pivoted(iPivot))
candidateList[numberCandidates++] = iRow;
} else {
clearPivoted(iPivot);
}
}
// and set first candidate
if (!numberCandidates) {
delete [] candidateList;
delete [] givenDuals;
givenDuals = NULL;
candidateList = NULL;
int iRow;
for (iRow = 0; iRow < numberRows_; iRow++) {
int iPivot = pivotVariable_[iRow];
clearPivoted(iPivot);
}
}
} else {
assert (!ifValuesPass);
}
#ifdef CHECK_ACCURACY
{
if (numberIterations_) {
int il = -1;