| Product |
Publisher |
Restrictions |
Object
or Source Code |
Derivative Calculation Requirements |
| AIMMS |
Paragon Decision Technology B.V. |
Differentiable constraints |
No |
None; Analytic derivatives are automatically computed by AIMMS. |
| CONOPT |
ARKI Consulting & Development A/S |
The model must be smooth and differentiable. It is assumed to be large and sparse. |
Object code (subroutine library);Executable modules w/ modeling languages. |
User must provide derivatives; they must be returned in sparse format and must be accurate. |
| CONOPT for AMPL |
Compass Modeling Solutions |
Differentiable and continuous (preferred) |
Object |
None - automatic differentiation |
| DFNLP |
K. Schittkowski |
Differentiable model functions |
Fortran source code |
Analytical or numerical |
| DOC/DOT |
Vanderplants R&D Inc. |
Continuous with continuous first derivatives |
Object |
user may provide; otherwise, finite differentiation is used |
| FANPAC/NLP |
Aptech Systems Inc. |
Twice differential objective function, P.D. Hession |
Source |
Numerical or user-supplied analytical |
| GRG2 |
Optimal Methods Inc. |
None, but best with a differentiable function |
Source code |
None |
| GRG2 for AMPL and AMPL Plus |
Compass Modeling Solutions |
Differentiable and continuous (preferred) |
Object |
None - automatic differentiation |
| IMSL Libraries |
Visual Numetrics |
None |
Both |
Either user-supplied or finite difference approximations are applied. |
| INTPT |
Optimal Methods Inc. |
None, but best with differentiable functions |
Source |
None |
| LANCELOT |
P. Toint |
Differentiability |
Source |
First derivatives (second if possible) |
| LGO, for Continuous Global Optimization |
Pinter Consulting Services |
Only continuity is assumed; applicable even to stand-alone, black box models |
Object |
None |
| LINGO |
LINDO Systems Inc. |
All standard math. functions and probability/queuing functions supported. Convexity & differentiability help, but not required. |
PC versions include DLL & OLE interfaces. |
None; Derivatives are calculated automatically; user can override defaults. |
| LSGRG for AMPL and AMPL Plus |
Compass Modeling Solutions |
Differentiable and continuous (preferred) |
Object |
None - automatic differentiation |
| LSGRG2 |
Optimal Methods Inc. |
None, but best with a differentiable function |
Source |
None |
| LSSOL |
Stanford Business Software |
Positive definite or semi-definite QP (including LP) linear constraints |
Source |
— |
| Mathcad |
MathSoft Inc. |
Differentiable functions |
Object |
None |
| Microsoft Excel 97 - Solver |
Microsoft Corporation |
None, but convergence results depend on differentiability |
Object |
None |
| MINOS for AMPL |
Compass Modeling Solutions |
Differentiable and continuous (preferred) |
Object |
None - automatic differentiation |
| MINOS 5.5 |
Stanford Business Software |
Nonlinear objectives and constraint functions must be smooth, local optimum obtained for nonconvex problems. |
Source, Mex files for MATLAB |
Automatic or user supply |
| NAG C Library |
Numerical Algorithms Group |
Will use first derivatives if provided, but will estimate otherwise |
Both |
May provide first derivative, but not required |
| NAG Fortran Library |
Numerical Algorithms Group |
Will use first derivatives if provided, but will estimate otherwise |
Both |
May provide first derivative, but not required |
| NLPQL |
K. Schittkowski |
Differentiable model functions |
Fortran source code |
Analytical or numerical |
| NPSOL 5.0 |
Stanford Business Software |
Non-linear objective and constraints functions must be smooth. Local optimum obtaines for non-convex problems. |
Source, Mex files for MATLAB |
Automatic or user supply |
| Optimal EngineerĘ |
Transpower Corporation |
None |
Only under very special conditions |
None - program does it |
| Premium Solver Platform for Excel |
Frontline Systems Inc. |
None, but convergence results depend on differentiability |
Object |
None |
| Premium Solver, Premium Solver Plus for Excel |
Frontline Systems Inc. |
None, but convergence results depend on differentiability |
Object |
None |
| SAS Software |
SAS Institute Inc. |
Continuous objective with continuous 1st-order deriv. (except N-M simplex) Some techniques require continuous 2nd-order deriv. |
n |
Can compute deriv. via analysis or finite differentiation approx., or user can supply exact or approx. numerical functions. |
| SCIENTIST for Windows |
MicroMath Research |
n/a |
n/a |
n/a |
| SLP/GRG |
Optimal Methods Inc. |
None, but best with differentiable functions |
Source code |
None |
| SOCS and NLPSPR |
Boeing Co. |
Differentiable (C squared) |
Object code |
Analytic or finite difference derivatives |
| Solver DLL V3.0, Solver DLL Plus |
Frontline Systems Inc. |
Problem functions should be differentiable. |
Object code (Dynamic Link Library) |
User may optionally write jacobian subroutine to compute derivatives |
| Solver for Lotus 1-2-3 97/98 |
Frontline Systems Inc. |
None, but convergence results depend on differentiability |
Object code |
None |
| SOPT-CP |
SAITECH Inc. |
For convex problems, SOPT finds a global optimum. Otherwise, local optimum. |
Yes |
Without AMPL, need to set up Hession and/or Jacobian. |
| SQP |
Optimal Methods Inc. |
None, but best with differentiable functions |
Source code |
None |
| What's Best! |
LINDO Systems Inc. |
All standard math. functions & probability/queuing functions supported. Convexity & differentiability help, but not required. |
— |
None; Derivatives are calculated automatically; user can override defaults. |
| XPRESS Barrier QP Solver |
Dash Associates Ltd. |
Convex quadratic objective and constraints allowed |
Object code only |
None |
| X Solver 2.0 |
Exatech Corporation |
None |
No |
No derivative calculations are used. |
