Changeset 1542:0219ee65ffcc in lemon0.x
 Timestamp:
 07/07/05 17:00:04 (15 years ago)
 Branch:
 default
 Phase:
 public
 Convert:
 svn:c9d7d8f590d60310b91f818b3a526b0e/lemon/trunk@2037
 Files:

 3 edited
Legend:
 Unmodified
 Added
 Removed

lemon/lp_base.h
r1536 r1542 139 139 }; 140 140 141 142 143 144 145 146 147 148 149 150 151 152 153 141 ///\e The type of the investigated LP problem 142 enum ProblemTypes { 143 ///Primaldual feasible 144 PRIMAL_DUAL_FEASIBLE = 0, 145 ///Primal feasible dual infeasible 146 PRIMAL_FEASIBLE_DUAL_INFEASIBLE = 1, 147 ///Primal infeasible dual feasible 148 PRIMAL_INFEASIBLE_DUAL_FEASIBLE = 2, 149 ///Primaldual infeasible 150 PRIMAL_DUAL_INFEASIBLE = 3, 151 ///Could not determine so far 152 UNKNOWN = 4 153 }; 154 154 155 155 ///The floating point type used by the solver … … 553 553 virtual int _addCol() = 0; 554 554 virtual int _addRow() = 0; 555 virtual void _eraseCol(int col) = 0; 556 virtual void _eraseRow(int row) = 0; 555 557 virtual void _setRowCoeffs(int i, 556 558 int length, … … 681 683 ///\param c is the column to be modified 682 684 ///\param e is a dual linear expression (see \ref DualExpr) 683 ///\bug This is a tempor tary function. The interface will change to685 ///\bug This is a temporary function. The interface will change to 684 686 ///a better one. 685 687 void setCol(Col c,const DualExpr &e) { … … 718 720 Row addRow() { Row r; r.id=rows.insert(_addRow()); return r;} 719 721 720 ///\brief Add s several new row721 ///(i.e a variables) at once722 ///\brief Add several new rows 723 ///(i.e a constraints) at once 722 724 /// 723 725 ///This magic function takes a container as its argument … … 844 846 return r; 845 847 } 848 ///Erase a coloumn (i.e a variable) from the LP 849 850 ///\param c is the coloumn to be deleted 851 ///\todo Please check this 852 void eraseCol(Col c) { 853 _eraseCol(cols.floatingId(c.id)); 854 cols.erase(c.id); 855 } 856 ///Erase a row (i.e a constraint) from the LP 857 858 ///\param r is the row to be deleted 859 ///\todo Please check this 860 void eraseRow(Row r) { 861 _eraseRow(rows.floatingId(r.id)); 862 rows.erase(r.id); 863 } 846 864 847 865 ///Set an element of the coefficient matrix of the LP 
lemon/lp_cplex.cc
r1508 r1542 243 243 //CPX_PARAM_LPMETHOD 244 244 status = CPXlpopt(env, lp); 245 //status = CPXprimopt(env, lp); 245 246 if (status == 0){ 246 247 //We want to exclude some cases … … 328 329 // ??case CPX_ABORT_CROSSOVER 329 330 // ??case CPX_INForUNBD 330 // ??case CPX_PIVOT 331 // ??case CPX_PIVOT 332 333 //Some more interesting stuff: 334 335 // CPX_PARAM_LPMETHOD 1062 int LPMETHOD 336 // 0 Automatic 337 // 1 Primal Simplex 338 // 2 Dual Simplex 339 // 3 Network Simplex 340 // 4 Standard Barrier 341 // Default: 0 342 // Description: Method for linear optimization. 343 // Determines which algorithm is used when CPXlpopt() (or "optimize" in the Interactive Optimizer) is called. Currently the behavior of the "Automatic" setting is that CPLEX simply invokes the dual simplex method, but this capability may be expanded in the future so that CPLEX chooses the method based on problem characteristics 344 //Hulye cplex 345 void statusSwitch(CPXENVptr env,int& stat){ 346 int lpmethod; 347 CPXgetintparam (env,CPX_PARAM_LPMETHOD,&lpmethod); 348 if (lpmethod==2){ 349 if (stat==CPX_UNBOUNDED){ 350 stat=CPX_INFEASIBLE; 351 } 352 else{ 353 if (stat==CPX_INFEASIBLE) 354 stat=CPX_UNBOUNDED; 355 } 356 } 357 } 331 358 332 359 LpCplex::SolutionStatus LpCplex::_getPrimalStatus() 333 360 { 361 334 362 int stat = CPXgetstat(env, lp); 363 statusSwitch(env,stat); 364 //CPXgetstat(env, lp); 335 365 //printf("A primal status: %d, CPX_OPTIMAL=%d \n",stat,CPX_OPTIMAL); 336 366 switch (stat) { … … 340 370 return OPTIMAL; 341 371 case CPX_UNBOUNDED://Unbounded 342 return INFINITE; 372 return INFEASIBLE;//In case of dual simplex 373 //return INFINITE; 343 374 case CPX_INFEASIBLE://Infeasible 344 375 // case CPX_IT_LIM_INFEAS: … … 349 380 // case CPX_ABORT_PRIM_INFEAS: 350 381 // case CPX_ABORT_PRIM_DUAL_INFEAS: 351 return INFEASIBLE; 382 return INFINITE;//In case of dual simplex 383 //return INFEASIBLE; 352 384 // case CPX_OBJ_LIM: 353 385 // case CPX_IT_LIM_FEAS: … … 384 416 { 385 417 int stat = CPXgetstat(env, lp); 418 statusSwitch(env,stat); 386 419 switch (stat) { 387 420 case 0: 
test/lp_test.cc
r1508 r1542 1 1 #include<lemon/lp_skeleton.h> 2 2 #include "test_tools.h" 3 3 4 4 5 #ifdef HAVE_CONFIG_H … … 183 184 } 184 185 186 void solveAndCheck(LpSolverBase& lp, LpSolverBase::SolutionStatus stat, 187 double exp_opt){ 188 lp.solve(); 189 //int decimal,sign; 190 std::string buf1; 191 // itoa(stat,buf1, 10); 192 check(lp.primalStatus()==stat,"Primalstatus should be "+buf1); 193 194 if (stat == LpSolverBase::OPTIMAL){ 195 check(std::abs(lp.primalValue()exp_opt)<1e3, 196 "Wrong optimal value: the right optimum is "); 197 //+ecvt(exp_opt,2) 198 } 199 } 200 185 201 void aTest(LpSolverBase & lp) 186 202 { 187 203 typedef LpSolverBase LP; 188 204 189 //The following example is taken from the book by Gáspár and Temesi, page 39.205 //The following example is very simple 190 206 191 207 typedef LpSolverBase::Row Row; … … 198 214 199 215 //Constraints 200 lp.addRow(3*x1+2*x2 >=6);201 lp.addRow( 1*x1+x2<=4);202 lp.addRow( 5*x1+8*x2<=40);203 lp.addRow(x1 2*x2<=4);216 Row upright=lp.addRow(x1+x2 <=1); 217 lp.addRow(x1+x2 >=1); 218 lp.addRow(x1x2 <=1); 219 lp.addRow(x1x2 >=1); 204 220 //Nonnegativity of the variables 205 221 lp.colLowerBound(x1, 0); 206 222 lp.colLowerBound(x2, 0); 207 223 //Objective function 208 lp.setObj( 2*x1+x2);224 lp.setObj(x1+x2); 209 225 210 226 lp.max(); 211 lp.solve(); 212 213 double opt=122.0/9.0; 214 215 if (lp.primalStatus()==LpSolverBase::OPTIMAL){ 216 std::cout<< "Z = "<<lp.primalValue() 217 << " (error = " << lp.primalValue()opt 218 << "); x1 = "<<lp.primal(x1) 219 << "; x2 = "<<lp.primal(x2) 220 <<std::endl; 221 222 } 223 else{ 224 std::cout<<"Optimal solution not found!"<<std::endl; 225 } 226 227 check(lp.primalStatus()==LpSolverBase::OPTIMAL,"Primalstatus should be OPTIMAL"); 228 229 check(std::abs(lp.primalValue()opt)<1e3, 230 "Wrong optimal value: the right optimum is 122/9 (13.555555...)"); 231 227 228 //Maximization of x1+x2 229 //over the triangle with vertices (0,0) (0,1) (1,0) 230 double expected_opt=1; 231 solveAndCheck(lp, LpSolverBase::OPTIMAL, expected_opt); 232 233 //Minimization 234 lp.min(); 235 expected_opt=0; 236 solveAndCheck(lp, LpSolverBase::OPTIMAL, expected_opt); 237 238 //Vertex (1,0) instead of (0,0) 239 lp.colLowerBound(x1, LpSolverBase::INF); 240 expected_opt=1; 241 solveAndCheck(lp, LpSolverBase::OPTIMAL, expected_opt); 242 243 //Erase one constraint and return to maximization 244 lp.eraseRow(upright); 245 lp.max(); 246 expected_opt=LpSolverBase::INF; 247 solveAndCheck(lp, LpSolverBase::INFINITE, expected_opt); 248 249 //Infeasibilty 250 lp.addRow(x1+x2 <=2); 251 solveAndCheck(lp, LpSolverBase::INFEASIBLE, expected_opt); 252 253 //Change problem and forget to solve 254 lp.min(); 255 check(lp.primalStatus()==LpSolverBase::UNDEFINED,"Primalstatus should be UNDEFINED"); 256 257 // lp.solve(); 258 // if (lp.primalStatus()==LpSolverBase::OPTIMAL){ 259 // std::cout<< "Z = "<<lp.primalValue() 260 // << " (error = " << lp.primalValue()expected_opt 261 // << "); x1 = "<<lp.primal(x1) 262 // << "; x2 = "<<lp.primal(x2) 263 // <<std::endl; 264 265 // } 266 // else{ 267 // std::cout<<lp.primalStatus()<<std::endl; 268 // std::cout<<"Optimal solution not found!"<<std::endl; 269 // } 270 271 232 272 233 273 }
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