|
| | bound01.f90 |
| | Model in which a simple inequality is converted into a simple bound in the preprocessor.
|
| |
| | bound02.f90 |
| | Model in which several simple inequalities are converted into simple bounds.
|
| |
| | bound03.f90 |
| | Model in which several simple inequalities are converted into simple bounds. The implied bounds are infeasible.
|
| |
| | bound04.f90 |
| | Very simple model with a bound and in inconsistent inequalities that could be converted into a simple bounds. Intended to check error messages and status for the solution.
|
| |
| | circle.f90 |
| | The model approximates the inner of a circle with center in (0,0) and radius 1 by NP tangents defines in NP points spread evenly on the circle In addition we add the constraint y = 0.5 and maximize x.
|
| |
| | circles.f90 |
| | Intersection of two circles with radius 1 and center in (0,0) and (1.6,0).
|
| |
| | cns02.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | cns03.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | cns12.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | const01.f90 |
| | Model with derivatives that become constant after other variables are fixed.
|
| |
| | const02.f90 |
| | Model with derivatives that become constant after other variables are fixed.
|
| |
| | const03.f90 |
| | Model with derivatives that become constant after other variables are fixed.
|
| |
| | const04.f90 |
| | Model with derivatives that become constant after other variables are fixed.
|
| |
| | const05.f90 |
| | Model with derivatives that become constant after other variables are fixed.
|
| |
| | const06.f90 |
| | Model with derivatives that become constant after other variables are fixed.
|
| |
| | const07.f90 |
| | Model with derivatives that become constant after other variables are fixed.
|
| |
| | const08.f90 |
| | Model with derivatives that become constant after other variables are fixed.
|
| |
| | const09.f90 |
| | Model with derivatives that become constant after other variables are fixed.
|
| |
| | const10.f90 |
| | Model with derivatives that become constant after other variables are fixed.
|
| |
| | const11.f90 |
| | Model with derivatives that become constant after other variables are fixed.
|
| |
| | const12.f90 |
| | Model with derivatives that become constant after other variables are fixed.
|
| |
| | const13.f90 |
| | Model with derivatives that become constant after other variables are fixed.
|
| |
| | const14.f90 |
| | Model with derivatives that become constant after other variables are fixed.
|
| |
| | const15.f90 |
| | Model with derivatives that become constant after other variables are fixed.
|
| |
| | const16.f90 |
| | Model with derivatives that become constant after other variables are fixed.
|
| |
| | elec.f90 |
| | Electron model from COPS test set.
|
| |
| | elec2.f90 |
| | Electron model from COPS test set, this time with a Hessian.
|
| |
| | eq2a.f90 |
| | Tests EQ2 constraints that are made post-triangular by transfer of bounds.
|
| |
| | eq2b.f90 |
| | Tests EQ2 constraints that are made post-triangular by transfer of bounds.
|
| |
| | eq2d.f90 |
| | Tests EQ2 constraints that are made post-triangular by transfer of bounds.
|
| |
| | evalerr01.f90 |
| | Model with function evaluation errors – 01.
|
| |
| | evalerror01.f90 |
| | Test function evaluation errors. Case 01: Function evaluation error in the initial point.
|
| |
| | evalerror02.f90 |
| | Test function evaluation errors. Case 02: Derivative evaluation error in the initial point.
|
| |
| | evalerror03.f90 |
| | Test function evaluation errors. Case 03: Function value overflow in the initial point.
|
| |
| | evalerror04.f90 |
| | Test function evaluation errors. Case 04: Derivative value overflow in the initial point.
|
| |
| | evalerror05.f90 |
| | Test function evaluation errors. Case 05: function evalutation error in the preprocessor.
|
| |
| | evalerror06.f90 |
| | Test function evaluation errors. Case 06: Derivative evalutation error in the preprocessor.
|
| |
| | evalerror07.f90 |
| | Test function evaluation errors. Case 07: Function overflow in the preprocessor.
|
| |
| | evalerror08.f90 |
| | Test function evaluation errors. Case 08: Derivative overflow in the preprocessor.
|
| |
| | evalerror09.f90 |
| | Test function evaluation errors. Case 09: function and Derivative evaluation error during AdjustInitialPoint.
|
| |
| | evalerror10.f90 |
| | Test function evaluation errors. Case 10: function evaluation error after the preprocessor.
|
| |
| | evalerror11.f90 |
| | Test function evaluation errors. Case 10: function evaluation error after the preprocessor.
|
| |
| | evalerror12.f90 |
| | Test function evaluation errors. Case 12: function evaluation error after AdjustInitialPoint.
|
| |
| | evalerror13.f90 |
| | Test function evaluation errors. Case 13: function evalutation error after AdjustInitialPoint.
|
| |
| | evalerror14.f90 |
| | Test function evaluation errors. Case 14: Function evaluation error after linear feasibility model has been solved.
|
| |
| | ex01.f90 |
| | This is an implementation of the model PORT.GMS (seq 50) in the GAMS model library. The model is repeated here for easy reference:
|
| |
| | force01.f90 |
| | In the first round equation e2 is solved w.r.t. x2 and the value is 1.0 and e3 is converted into an upper bound of 1.0 on x4. In the second round equation e1 is solved w.r.t. x1 and the value is 1.0 and e4 is converted into an upper bound of 0.0 on x4 that subsequently is fixed at 0.0. Finally, e5 is forcing x5 and x6 zero. The solution is unique.
|
| |
| | force02.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | force03.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | force04.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | force05.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | fvboth.f90 |
| | This is the pindyck example rewritten to use both the FVforAll and FVincLin way of defining nonlinear functions. All functions, linear as well as nonlinear, will be called, and the functions must return the sum of the linear and nonlinear terms.
|
| |
| | fvboth2.f90 |
| | This is the pindyck example rewritten to use both the FVforAll and FVincLin way of defining nonlinear functions. All functions, linear as well as nonlinear, will be called, and the functions must return the sum of the linear and nonlinear terms. Similar to FvBoth but with Linear Jacobian elements defined in FDEval.
|
| |
| | fvforall.f90 |
| | This is the pindyck example rewritten to use the FVforAll way of defining nonlinear functions. All functions, linear as well as nonlinear, will be called.
|
| |
| | fvforall2.f90 |
| | This is the pindyck example rewritten to use the FVforAll2 way of defining nonlinear functions. All functions, linear as well as nonlinear, will be called.
|
| |
| | fvinclin.f90 |
| | This is the pindyck example rewritten to use the FVincLin way of defining nonlinear functions. The nonlinear functions must contain both linear and nonlinear terms.
|
| |
| | fvinclin2.f90 |
| | This is the pindyck example rewritten to use the FVincLin way of defining nonlinear functions. The nonlinear functions must contain both linear and nonlinear terms. In this FvincLin2 example we also define the linear derivatives that appear in the nonlinear constraints.
|
| |
| | ident01.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | ident02.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | ident03.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | ident04.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | ident05.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | ident06.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | ident07.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | ident08.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | ident09.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | ident10.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | ident11.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | ident12.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | ident13.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | ident20.f90 |
| | Model with identical variables, i.e. a*xi =E= b*xj
|
| |
| | ident21.f90 |
| | Model with identical variables, i.e. a*xi =E= b*xj. Similar to ident20, but with bounds on the variables. The bounds are not consistent with the identities.
|
| |
| | ident22.f90 |
| | Model with identical variables, i.e. a*xi =E= b*xj. Similar to ident21, but with bounds on the variables. The bounds are not consistent with the identities and they prevent the global minimum of zero.
|
| |
| | ident23.f90 |
| | Model with identical variables, i.e. a*xi =E= b*xj Similar to ident20, but with an extra redundant identity.
|
| |
| | identnl01.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | identnl03.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | largebnd.f90 |
| | This is the standard Tutorial model with a little twist: We assign some of the upper bounds a value above the maximum value of Rtmaxv. Earlier this would be a fatal error. Now the bounds are projected on the legal interval, a message is issued, and the model solved.
|
| |
| | largeres1.f90 |
| | Example with a Large Residual in the initial point.
|
| |
| | largeres2.f90 |
| | Example with a Large Residual in the initial point.
|
| |
| | largerhs.f90 |
| | Model with large right Hand Side.
|
| |
| | leastl1.f90 |
| | We solve the following nonlinear linear L1 estimation model:
|
| |
| | leastsq.f90 |
| | We solve the following nonlinear least squares model:
|
| |
| | leastsq10.f90 |
| | This model is similar to leastsq5, but this time we define 2nd order directional information in the form of both an 2DDirIni routine where the bulk of the work is done and a 2DDir routine that just copies the information.
|
| |
| | leastsq2.f90 |
| | This model is similar to leastsq. The key difference is that we supply a callback routine that can compute 2nd derivatives of the model. However, we only include part of the 2nd derivatives corresponding to the direct objective terms, res(i)**2. The terms from b(i,j)*x(j)**2 are ignored in the 2nd derivatives. CONOPT will not notice the incorrect derivatives but it may converge more slowly.
|
| |
| | leastsq4.f90 |
| | This model is similar to leastsq2. The key difference is that the 2nd derivatives this time are complete as opposed to only including the res(i)**2 term.
|
| |
| | leastsq5.f90 |
| | This model is similar to leastsq, but this time we define 2nd order information in the form of a 2DDir routine.
|
| |
| | leastsq6.f90 |
| | Currently, the model crashes in the inversion – have it fixed later.
|
| |
| | leastsq7.f90 |
| | The model is similar to leastsq, but we test that the duals have the right value, run again without a post triangle and check the duals and we also compare duals with the change in objective from a perturbation.
|
| |
| | leastsq8.f90 |
| | We solve the following nonlinear least squares model:
|
| |
| | leastsq9.f90 |
| | The model is similar to leastsq7, but we test an alternative optimal solution that was found at some stage with bounded residuals. The new solution is then used as a starting point for a run without bounded residuals and the solution should still be optimal.
|
| |
| | lp01a.f90 |
| | Test model lp01a.gms – case with an error.
|
| |
| | lp01b.f90 |
| | Test model lp01.gms – case with an error.
|
| |
| | lp10.f90 |
| | Test model lp10.gms – case with an error.
|
| |
| | minimax.f90 |
| | We solve the following nonlinear minimax model:
|
| |
| | minimax01.f90 |
| | MiniMax01 is a small version of minimax used for quick turnaround.
|
| |
| | minimax02.f90 |
| | Minimax02 is a small version of minimax used for quick turnaround.
|
| |
| | minimax03.f90 |
| | MiniMax03 is a small version of minimax used for quick turnaround.
|
| |
| | minimax04.f90 |
| | Minimax04 is a minimax model with multiple minimax variables.
|
| |
| | minimax05.f90 |
| | Minimax05 is a minimax model with multiple minimax variables and some side constraints.
|
| |
| | minimax06.f90 |
| | Minimax06 is a minimax model with multiple minimax variables and some side constraints. Compared to Minimax05 we have lower bounds of -1.0 on x
|
| |
| | minimax07.f90 |
| | Minimax07 is a minimax model with multiple minimax variables and some side constraints.
|
| |
| | minimax08.f90 |
| | Minimax08 is a minimax model with multiple minimax variables and some side constraints. Same as minimax06 except that the quadratic b-term is much smaller so the SLP part should work well.
|
| |
| | mono01.f90 |
| | Monotone function to bound conversion example 01.
|
| |
| | mono02.f90 |
| | Monotone function to bound conversion example 02.
|
| |
| | mono03.f90 |
| | Monotone function to bound conversion example 03.
|
| |
| | mono04.f90 |
| | Monotone function to bound conversion example 04.
|
| |
| | mono05.f90 |
| | Monotone function to bound conversion example 05.
|
| |
| | mono06.f90 |
| | Monotone function to bound conversion example 06.
|
| |
| | mono07.f90 |
| | Monotone function to bound conversion example 07.
|
| |
| | mono08.f90 |
| | Monotone function to bound conversion example 08.
|
| |
| | mono09.f90 |
| | Monotone function to bound conversion example 09.
|
| |
| | mono10.f90 |
| | Monotone function to bound conversion example 10.
|
| |
| | nleq01.f90 |
| | Nonlinear singleton to bound conversion example 01.
|
| |
| | nleq02.f90 |
| | Nonlinear function to bound conversion example 02.
|
| |
| | nleq03.f90 |
| | Nonlinear function to bound conversion example 03.
|
| |
| | nleq04.f90 |
| | Nonlinear constraint to bound conversion example 04.
|
| |
| | nleq05.f90 |
| | nonlinear function to bound conversion example 05
|
| |
| | objconvar.f90 |
| | Both ObjVar and ObjCon are defined. The last one is used.
|
| |
| | objnotn.f90 |
| | Objective constraint is not type =N=. Test the error message.
|
| |
| | overflow01.f90 |
| | A program that will give large post-triangular variables. Used to check the back-tracking in Newton and Conedm.
|
| |
| | overlap01.f90 |
| | Example 01 in a series of examples where the same variable appears as a singleton in several nonlinear constraints.
|
| |
| | overlap02.f90 |
| | Example 02 in a series of examples where the same variable appears as a singleton in several nonlinear constraints.
|
| |
| | pen01.f90 |
| | Test model pen01.gms – a model with penalty pairs.
|
| |
| | pen02.f90 |
| | Test model pen02.gms – a model with penalty pairs.
|
| |
| | pinadd.f90 |
| | This is a CONOPT implementation of the Pindyck model from the GAMS model library. The implementation is similar to the one in pindyck, but this time we first solve the model for 16 periods and then we gradually increase the number of periods one at a time up to 20.
|
| |
| | pinadd2.f90 |
| | This is a CONOPT implementation of the Pindyck model from the GAMS model library. The implementation is similar to the one in pindyck, but this time we first solve the model for 16 periods and then we gradually increase the number of periods one at a time up to 20.
|
| |
| | pinadd2err.f90 |
| | This is a CONOPT implementation of the Pindyck model from the GAMS model library. The implementation is similar to the one in pindyck, but this time we first solve the model for 16 periods and then we gradually increase the number of periods one at a time up to 20.
|
| |
| | pindyck.f90 |
| | This is a CONOPT implementation of the Pindyck model from the GAMS model library. The GAMS model follows as documentation:
|
| |
| | pinopt.f90 |
| | pinopt is a version of pindyck in which we define options with an options routine.
|
| |
| | pinsquare.f90 |
| | This is a CONOPT implementation of the Pindyck model from the GAMS model library. The original model is revised so the P variables are fixed. This gives rise to a square system of equations and the Square option is tested.
|
| |
| | pinstaterr.f90 |
| | This is a CONOPT implementation of the Pindyck model from the GAMS model library. The implementation is similar to the one in pindyck, but this time we first solve the model for 16 periods and then we gradually increase the number of periods one at a time up to 20.
|
| |
| | polygon.f90 |
| | Polygon model from COPS test set.
|
| |
| | post.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | postmo01.f90 |
| | Model with derivatives that become constant after other variables are fixed.
|
| |
| | postmo02.f90 |
| | Model with derivatives that become constant after other variables are fixed. Similar to postmo01 except that the Rhs and Lhs have been reversed so the monotone terms changes between increasing and decreasing.
|
| |
| | postmo03.f90 |
| | Model with derivatives that become constant after other variables are fixed. The model is similar to postmo01 but the bounds are changed.
|
| |
| | postmo04.f90 |
| | Model with derivatives that become constant after other variables are fixed. The model is similar to postmo01 but the bounds are changed.
|
| |
| | qp1.f90 |
| | The current model is a simple QP model with a sparse Q matrix, bounded variables, and one constraint.
|
| |
| | qp2.f90 |
| | Similar to qp1 but uses directional 2nd derivatives.
|
| |
| | qp3.f90 |
| | Similar to qp1 but uses 2nd derivatives as a matrix.
|
| |
| | qp4.f90 |
| | A combination of qp2 and qp3.
|
| |
| | qp5.f90 |
| | Similar to qp1 but uses 2nd derivatives computed using perturbations.
|
| |
| | qp7.f90 |
| | Similar to qp5 but with the objective defined as a positive variable, i.e. it cannot be removed in the post-triangle.
|
| |
| | range01.f90 |
| | Range01: Model where some constraints can be combined into a single ranged constraint.
|
| |
| | range02.f90 |
| | Range02: Model where some constraints can be combined into a single ranged constraint.
|
| |
| | range03.f90 |
| | Range03: Model where some constraints can be combined into a single ranged constraint.
|
| |
| | range04.f90 |
| | Range04: Model where some constraints can be combined into a single ranged constraint.
|
| |
| | range05.f90 |
| | Range05: Model where some constraints can be combined into a single ranged constraint.
|
| |
| | range06.f90 |
| | Range06: Model where some constraints can be combined into a single ranged constraint.
|
| |
| | range07.f90 |
| | Range07: Model where some constraints can be combined into a single ranged constraint.
|
| |
| | range08.f90 |
| | Range08: Model where some constraints can be combined into a single ranged constraint.
|
| |
| | range09.f90 |
| | Range09: Model where some constraints can be combined into a single ranged constraint.
|
| |
| | range10.f90 |
| | Range10: Model where some constraints can be combined into a single equality constraint.
|
| |
| | roseq.f90 |
| | Optimality conditions for Rosenbrock function.
|
| |
| | rosex.f90 |
| | Extended Rosenbrock function.
|
| |
| | sqp1.f90 |
| | SQP1: Sparse QP model with 2DLagrStr and 2DLagrVal routines.
|
| |
| | sqp2.f90 |
| | SQP2: Sparse QP model with 2DLagrStr and 2DLagrVal routines.
|
| |
| | sqp3.f90 |
| | SQP3: Sparse QP model with 2DLagrStr and 2DLagrVal routines.
|
| |
| | square.f90 |
| | This is a square model where we pretend that the second constraint is completely nonlinear.
|
| |
| | square2.f90 |
| | A square model where we pretend that the second constraint is completely nonlinear.
|
| |
| | square3.f90 |
| | A square model where we pretend that the second and third constraints are completely nonlinear.
|
| |
| | square4.f90 |
| | A square model where we pretend that the last two constraints are completely nonlinear except for the last term.
|
| |
| | square5.f90 |
| | A square model where we pretend that the last three constraints are nonlinear except for their last entry.
|
| |
| | square6.f90 |
| | A square model where we pretend that the last three constraints are nonlinear except for their last entry.
|
| |
| | square7.f90 |
| | This model is solved as a Square System.
|
| |
| | square8.f90 |
| | Similar to square7 but with a different right hand side in e3.
|
| |
| | stress1.f90 |
| | This is a CONOPT implementation of the gams model.
|
| |
| | tria01.f90 |
| | Triangular Demo model 01.
|
| |
| | tria01a.f90 |
| | Similar to Tria01 except that FDEval has an error in the Rowno test and it will therefore return an error that the main program should detect.
|
| |
| | tria02.f90 |
| | Triangular Demo model 02.
|
| |
| | tria03.f90 |
| | Triangular Demo model 03.
|
| |
| | tria04.f90 |
| | Triangular Demo model 04.
|
| |
| | triabad01.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | triabad02.f90 |
| | Similar to triabad01, but this time e2 will converge to a point with a bad pivot. This is not useful.
|
| |
| | triabad03.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | triabad04.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | triabad05.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | triabad06.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | triabad07.f90 |
| | Similar to triabad01 but with different constant coefficients in e3 and e4.
|
| |
| | triabad08.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | triabad09.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | triabad10.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | triabad11.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | triabad12.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | triabad13.f90 |
| | Triangular Demo model 13.
|
| |
| | tutorial.f90 |
| | a tutorial providing an introduction to the CONOPT API
|
| |
| | tutorial2.f90 |
| | This model is a revision of Tutorial in which we have added a set of 2nd derivative routines, Tut_2DLagrStr and Tut_2DLagrVal.
|
| |
| | tutorial2r.f90 |
| | This is a revised version of Tutorial2 in which the production function is split into two equations using an intermediate variable.
|
| |
| | tutoriali.f90 |
| | This model is a revision of Tutorial in which we have added a an initialization callback for the First derivative, Tut_FDEvalIni.
|
| |
| | tutorialk.f90 |
| | This model is a revision of Tutorial in which we have made the capital stock, K, a variable in the model fixed at the value 4.
|
| |
| | unbounded.f90 |
| | This model has an implementation error – the lower bounds on resp and resm are missing. The model should therefore be unbounded, but it is not reported as such at the moment. Investigate!!!
|
| |
| | undef01.f90 |
| | Test with some undefined Jacobian elements.
|
| |
| | undef02.f90 |
| | Model undef02: It is taken from square3 and a start-of-column element has not been defined. The message should say 'undefined' instead of '-1'.
|
| |
| | vpost01.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | vpost02.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | vpost03.f90 |
| | This is a CONOPT implementation of the GAMS model:
|
| |
| | zero01.f90 |
| | Zero01. Test model for constraints with coefficient that are identically Zero.
|
| |
1.13.2