void TaoOptimizationSolver<T>::solve () { LOG_SCOPE("solve()", "TaoOptimizationSolver"); this->init (); this->system().solution->zero(); PetscMatrix<T> * hessian = cast_ptr<PetscMatrix<T> *>(this->system().matrix); // PetscVector<T> * gradient = cast_ptr<PetscVector<T> *>(this->system().rhs); PetscVector<T> * x = cast_ptr<PetscVector<T> *>(this->system().solution.get()); PetscVector<T> * ceq = cast_ptr<PetscVector<T> *>(this->system().C_eq.get()); PetscMatrix<T> * ceq_jac = cast_ptr<PetscMatrix<T> *>(this->system().C_eq_jac.get()); PetscVector<T> * cineq = cast_ptr<PetscVector<T> *>(this->system().C_ineq.get()); PetscMatrix<T> * cineq_jac = cast_ptr<PetscMatrix<T> *>(this->system().C_ineq_jac.get()); PetscVector<T> * lb = cast_ptr<PetscVector<T> *>(&this->system().get_vector("lower_bounds")); PetscVector<T> * ub = cast_ptr<PetscVector<T> *>(&this->system().get_vector("upper_bounds")); // Set the starting guess to zero. x->zero(); PetscErrorCode ierr = 0; // Workaround for bug where TaoSetFromOptions *reset* // programmatically set tolerance and max. function evaluation // values when "-tao_type ipm" was specified on the command line: we // call TaoSetFromOptions twice (both before and after setting // custom options programatically) ierr = TaoSetFromOptions(_tao); LIBMESH_CHKERR(ierr); // Set convergence tolerances // f(X) - f(X*) (estimated) <= fatol // |f(X) - f(X*)| (estimated) / |f(X)| <= frtol // ||g(X)|| <= gatol // ||g(X)|| / |f(X)| <= grtol // ||g(X)|| / ||g(X0)|| <= gttol // Command line equivalents: -tao_fatol, -tao_frtol, -tao_gatol, -tao_grtol, -tao_gttol ierr = TaoSetTolerances(_tao, #if PETSC_RELEASE_LESS_THAN(3,7,0) // Releases up to 3.X.Y had fatol and frtol, after that they were removed. // Hopefully we'll be able to know X and Y soon. Guessing at 3.7.0. /*fatol=*/PETSC_DEFAULT, /*frtol=*/PETSC_DEFAULT, #endif /*gatol=*/PETSC_DEFAULT, /*grtol=*/this->objective_function_relative_tolerance, /*gttol=*/PETSC_DEFAULT); LIBMESH_CHKERR(ierr); // Set the max-allowed number of objective function evaluations // Command line equivalent: -tao_max_funcs ierr = TaoSetMaximumFunctionEvaluations(_tao, this->max_objective_function_evaluations); LIBMESH_CHKERR(ierr); // Set the max-allowed number of optimization iterations. // Command line equivalent: -tao_max_it // Not implemented for now as it seems fairly similar to // ierr = TaoSetMaximumIterations(_tao, 4); // LIBMESH_CHKERR(ierr); // Set solution vec and an initial guess ierr = TaoSetInitialVector(_tao, x->vec()); LIBMESH_CHKERR(ierr); // We have to have an objective function libmesh_assert( this->objective_object ); // Set routines for objective, gradient, hessian evaluation ierr = TaoSetObjectiveRoutine(_tao, __libmesh_tao_objective, this); LIBMESH_CHKERR(ierr); if ( this->gradient_object ) { ierr = TaoSetGradientRoutine(_tao, __libmesh_tao_gradient, this); LIBMESH_CHKERR(ierr); } if ( this->hessian_object ) { ierr = TaoSetHessianRoutine(_tao, hessian->mat(), hessian->mat(), __libmesh_tao_hessian, this); LIBMESH_CHKERR(ierr); } if ( this->lower_and_upper_bounds_object ) { // Need to actually compute the bounds vectors first this->lower_and_upper_bounds_object->lower_and_upper_bounds(this->system()); ierr = TaoSetVariableBounds(_tao, lb->vec(), ub->vec()); LIBMESH_CHKERR(ierr); } if ( this->equality_constraints_object ) { ierr = TaoSetEqualityConstraintsRoutine(_tao, ceq->vec(), __libmesh_tao_equality_constraints, this); LIBMESH_CHKERR(ierr); } if ( this->equality_constraints_jacobian_object ) { ierr = TaoSetJacobianEqualityRoutine(_tao, ceq_jac->mat(), ceq_jac->mat(), __libmesh_tao_equality_constraints_jacobian, this); LIBMESH_CHKERR(ierr); } // Optionally set inequality constraints if ( this->inequality_constraints_object ) { ierr = TaoSetInequalityConstraintsRoutine(_tao, cineq->vec(), __libmesh_tao_inequality_constraints, this); LIBMESH_CHKERR(ierr); } // Optionally set inequality constraints Jacobian if ( this->inequality_constraints_jacobian_object ) { ierr = TaoSetJacobianInequalityRoutine(_tao, cineq_jac->mat(), cineq_jac->mat(), __libmesh_tao_inequality_constraints_jacobian, this); LIBMESH_CHKERR(ierr); } // Check for Tao command line options ierr = TaoSetFromOptions(_tao); LIBMESH_CHKERR(ierr); // Perform the optimization ierr = TaoSolve(_tao); LIBMESH_CHKERR(ierr); // Store the convergence/divergence reason ierr = TaoGetConvergedReason(_tao, &_reason); LIBMESH_CHKERR(ierr); }
PetscErrorCode main(int argc,char **argv) { PetscErrorCode ierr; /* used to check for functions returning nonzeros */ PetscMPIInt size; Vec x; /* solution */ KSP ksp; PC pc; Vec ceq,cin; PetscBool flg; /* A return value when checking for use options */ Tao tao; /* Tao solver context */ TaoConvergedReason reason; AppCtx user; /* application context */ /* Initialize TAO,PETSc */ ierr = PetscInitialize(&argc,&argv,(char *)0,help);CHKERRQ(ierr); ierr = MPI_Comm_size(PETSC_COMM_WORLD,&size);CHKERRQ(ierr); /* Specify default parameters for the problem, check for command-line overrides */ ierr = PetscStrncpy(user.name,"HS21",8);CHKERRQ(ierr); ierr = PetscOptionsGetString(NULL,NULL,"-cutername",user.name,24,&flg);CHKERRQ(ierr); ierr = PetscPrintf(PETSC_COMM_WORLD,"\n---- MAROS Problem %s -----\n",user.name);CHKERRQ(ierr); ierr = InitializeProblem(&user);CHKERRQ(ierr); ierr = VecDuplicate(user.d,&x);CHKERRQ(ierr); ierr = VecDuplicate(user.beq,&ceq);CHKERRQ(ierr); ierr = VecDuplicate(user.bin,&cin);CHKERRQ(ierr); ierr = VecSet(x,1.0);CHKERRQ(ierr); ierr = TaoCreate(PETSC_COMM_WORLD,&tao);CHKERRQ(ierr); ierr = TaoSetType(tao,TAOIPM);CHKERRQ(ierr); ierr = TaoSetInitialVector(tao,x);CHKERRQ(ierr); ierr = TaoSetObjectiveAndGradientRoutine(tao,FormFunctionGradient,(void*)&user);CHKERRQ(ierr); ierr = TaoSetEqualityConstraintsRoutine(tao,ceq,FormEqualityConstraints,(void*)&user);CHKERRQ(ierr); ierr = TaoSetInequalityConstraintsRoutine(tao,cin,FormInequalityConstraints,(void*)&user);CHKERRQ(ierr); ierr = TaoSetInequalityBounds(tao,user.bin,NULL);CHKERRQ(ierr); ierr = TaoSetJacobianEqualityRoutine(tao,user.Aeq,user.Aeq,FormEqualityJacobian,(void*)&user);CHKERRQ(ierr); ierr = TaoSetJacobianInequalityRoutine(tao,user.Ain,user.Ain,FormInequalityJacobian,(void*)&user);CHKERRQ(ierr); ierr = TaoSetHessianRoutine(tao,user.H,user.H,FormHessian,(void*)&user);CHKERRQ(ierr); ierr = TaoGetKSP(tao,&ksp);CHKERRQ(ierr); ierr = KSPGetPC(ksp,&pc);CHKERRQ(ierr); ierr = PCSetType(pc,PCLU);CHKERRQ(ierr); /* This algorithm produces matrices with zeros along the diagonal therefore we need to use SuperLU which does partial pivoting */ ierr = PCFactorSetMatSolverPackage(pc,MATSOLVERSUPERLU);CHKERRQ(ierr); ierr = KSPSetType(ksp,KSPPREONLY);CHKERRQ(ierr); ierr = TaoSetTolerances(tao,0,0,0);CHKERRQ(ierr); ierr = TaoSetFromOptions(tao);CHKERRQ(ierr); ierr = TaoSolve(tao);CHKERRQ(ierr); ierr = TaoGetConvergedReason(tao,&reason);CHKERRQ(ierr); if (reason < 0) { ierr = PetscPrintf(MPI_COMM_WORLD, "TAO failed to converge due to %s.\n",TaoConvergedReasons[reason]);CHKERRQ(ierr); } else { ierr = PetscPrintf(MPI_COMM_WORLD, "Optimization completed with status %s.\n",TaoConvergedReasons[reason]);CHKERRQ(ierr); } ierr = DestroyProblem(&user);CHKERRQ(ierr); ierr = VecDestroy(&x);CHKERRQ(ierr); ierr = VecDestroy(&ceq);CHKERRQ(ierr); ierr = VecDestroy(&cin);CHKERRQ(ierr); ierr = TaoDestroy(&tao);CHKERRQ(ierr); ierr = PetscFinalize(); return ierr; }
PetscErrorCode main(int argc,char **argv) { PetscErrorCode ierr; /* used to check for functions returning nonzeros */ Tao tao; KSP ksp; PC pc; AppCtx user; /* application context */ ierr = PetscInitialize(&argc,&argv,(char *)0,help); CHKERRQ(ierr); ierr = PetscPrintf(PETSC_COMM_WORLD,"\n---- TOY Problem -----\n"); CHKERRQ(ierr); ierr = PetscPrintf(PETSC_COMM_WORLD,"Solution should be f(1,1)=-2\n"); CHKERRQ(ierr); ierr = InitializeProblem(&user); CHKERRQ(ierr); ierr = TaoCreate(PETSC_COMM_WORLD,&tao); CHKERRQ(ierr); ierr = TaoSetType(tao,TAOIPM); CHKERRQ(ierr); ierr = TaoSetInitialVector(tao,user.x); CHKERRQ(ierr); ierr = TaoSetVariableBounds(tao,user.xl,user.xu); CHKERRQ(ierr); ierr = TaoSetObjectiveAndGradientRoutine(tao,FormFunctionGradient,(void*)&user); CHKERRQ(ierr); ierr = TaoSetEqualityConstraintsRoutine(tao,user.ce,FormEqualityConstraints,(void*)&user); CHKERRQ(ierr); ierr = TaoSetInequalityConstraintsRoutine(tao,user.ci,FormInequalityConstraints,(void*)&user); CHKERRQ(ierr); ierr = TaoSetJacobianEqualityRoutine(tao,user.Ae,user.Ae,FormEqualityJacobian,(void*)&user); CHKERRQ(ierr); ierr = TaoSetJacobianInequalityRoutine(tao,user.Ai,user.Ai,FormInequalityJacobian,(void*)&user); CHKERRQ(ierr); ierr = TaoSetHessianRoutine(tao,user.H,user.H,FormHessian,(void*)&user); CHKERRQ(ierr); ierr = TaoSetTolerances(tao,0,0,0); CHKERRQ(ierr); ierr = TaoSetFromOptions(tao); CHKERRQ(ierr); ierr = TaoGetKSP(tao,&ksp); CHKERRQ(ierr); ierr = KSPGetPC(ksp,&pc); CHKERRQ(ierr); ierr = PCSetType(pc,PCLU); CHKERRQ(ierr); /* This algorithm produces matrices with zeros along the diagonal therefore we need to use SuperLU which does partial pivoting */ ierr = PCFactorSetMatSolverPackage(pc,MATSOLVERSUPERLU); CHKERRQ(ierr); ierr = KSPSetType(ksp,KSPPREONLY); CHKERRQ(ierr); ierr = KSPSetFromOptions(ksp); CHKERRQ(ierr); ierr = TaoSetTolerances(tao,0,0,0); CHKERRQ(ierr); ierr = TaoSolve(tao); CHKERRQ(ierr); ierr = DestroyProblem(&user); CHKERRQ(ierr); ierr = TaoDestroy(&tao); CHKERRQ(ierr); ierr = PetscFinalize(); return ierr; }