Prof. Klaus Schittkowski
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Software

Interactive Optimization Environments:     EASY-FIT ModelDesign   EASY-FIT Express   EASY-OPT Express 
Nonlinear Optimization Solvers: NLPQLP   NLPQLY   NLPQLG   NLPQLB   NLPJOB   NLPQLF  
Large-Scale Nonlinear Optimization Solver: NLPIP
Mixed-Integer Nonlinear Programming Solvers: MISQP  
Global Search: MIDACO BFOUR
Quadratic Programming Solvers: QL   MIQL
Least Squares and Data Fitting Solvers: NLPLSQ   NLPLSX   NLPL1   NLPINF   NLPMMX   PDEFIT   MODFIT  
Modeling Language: PCOMP  
Test Problems: NLP test problems   MINLP test problems   dynamical test problems


    ....  how to get software:

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Academic use: See software and license conditions, free for personal use, verify your status, and send confirmation letter (e-mail)

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Commercial or institutional use: Send e-mail for more details

    ....  MATLAB versions of selected programs:  TOMLAB

    ....  Implementation:

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Numerical codes are implemented in form of thread-safe Fortran subroutines, as close as necessary to F77, without pointers, global variables, or memory allocations. Especially, there are no tricky Fortran constructs like COMMON, DATA, ENTRY or EQUIVALENCE statements. The codes are easily transferred to C by f2c.

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Nonlinear function values are passed to the optimization routines in the most user-friendly and most flexible way by reverse communication. Only one iteration is performed by the algorithm. Depending on a flag, function or derivative values must be computed in the calling program. Then the optimization routine is called again.

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Partial derivatives are either provided in analytical form or are approximated by a difference formula, as illustrated by demo programs.


  Interactive Optimization Environments:
 
EASY-FIT ModelDesign: Interactive software system to estimate parameters and to compute optimal experimental designs for dynamic models consisting of analytical functions, systems of equations (steady-state), Laplace transforms, ordinary differential equations, differential algebraic equations, one-dimensional partial differential equations, and  one-dimensional partial differential algebraic equations. Proceeding from given experimental data, i.e., observation times and measurements, the minimum least squares distances of measured data from a fitting criterion are computed, that may depend on the solution of the dynamic system. Numerous special model variants are available, priority levels of the parameters are determined, and efficient numerical routines are applied.

EASY-FIT Express: Interactive software system to estimate parameters for dynamic models consisting of analytical functions. Proceeding from given experimental data, i.e., observation times and measurements, the minimum least squares distances of measured data from a fitting criterion are computed. Confidence intervals and priority levels of the parameters are determined. The software is free.

EASY-OPT Express: Interactive system running under MS-Windows to facilitate the formulation of nonlinear programming problems, their implementation and numerical solution. The goal is to minimize a general nonlinear objective function subject to nonlinear equality or inequality constraints and continuous and/or integer variables.  The software is free.

 
 
Nonlinear Optimization Solvers:
 
NLPQLP: Solves general nonlinear mathematical programming problems with equality and inequality constraints. It is assumed that all problem functions are continuously differentiable. The new version is prepared to run under a distributed system and applies non-monotone line search procedure in error situations. 
 
NLPQLY: Easy-to-use version of NLPQLP for solving general nonlinear mathematical programming problems with equality and inequality constraints. Objective and constraint function values must be provided by reverse communication and most tolerances are set to default values. Derivatives are internally approximated by forward differences.
 

NLPQLG: Successive execution of NLPQLP for stepwise improvement of local minima.
 

NLPQLB: Extension of the general nonlinear programming code NLPQLP to solve also problems with very many constraints, where the derivative matrix of the constraints does not possess any special sparse structure that can be exploited numerically.
 

NLPJOB: Interactive solution of multicriteria optimization problems, 15 different alternative for providing scalar nonlinear programs solved by NLPQLP.
 

NLPQLF: Solves constrained nonlinear optimization problems, where objective function and some constraints can be evaluated only for arguments of a set defined by additional constraints. It is assumed that all individual problem functions are continuously differentiable and that the feasible set is convex.
 
 
Large-Scale Nonlinear Optimization Solver:
 
NLPIP: Combined SQP-IPM method for large-scale optimization with limited-memory BFGS updates or Hessian of Lagrangian, taking sparsity of the Jacobian of constraints into account.
 
 
Mixed-Integer Nonlinear Programming Solvers:
 
MISQP: Implementation of a trust region SQP algorithm for mixed-integer nonlinear programming. Relaxable integer variables or convex problem functions are not required. Derivatives subject to integer variables are internally approximated and a BFGS matrix is updates.
 
 
General Purpose Optimization Solver:
 
MIDACO: Black-box optimizer, specially developed for mixed integer nonlinear programs (MINLPs), but also applicable on a wide range of optimization problems (global optimization, non-smooth optimization, ...).
 
BFOUR: Branch-and-bound solver, specially developed for mixed integer nonlinear programming.
 
 
Quadratic Programming Solvers:

 

QL: Solves quadratic programming problems with a positive definite objective function matrix and linear equality and inequality constraints.

MIQL: Solves mixed-integer quadratic programming problems with a positive definite objective function matrix and linear equality and inequality constraints by a branch-and-bound method. Comes with a special version of BFOUR.
 
 
Least Squares and Data Fitting Solvers:

 

NLPLSQ: Solves constrained nonlinear least squares problems, where the objective function is the sum of squared functions. In addition there may be any set of equality or inequality constraints. It is assumed that all individual problem functions are continuously differentiable.
 

NLPLSX: Solves constrained nonlinear least squares problems, where the objective function is the sum of very many squared functions. In addition there may be any set of equality or inequality constraints. It is assumed that all individual problem functions are continuously differentiable.
 
NLPL1: Solves constrained nonlinear L1 problems, where the objective function is the sum of absolute function values. In addition there may be any set of equality or inequality constraints. It is assumed that all individual problem functions are continuously differentiable.
 
NLPINF: Solves constrained nonlinear maximum-norm data fitting problems, where the objective function is the maximum of absolute function values. In addition, there may be any set of equality or inequality constraints. It is assumed that all individual problem functions are continuously differentiable. The code is particularly useful for solving nonlinear approximation problems with a large number of support values.
 
NLPMMX: Solves constrained nonlinear min-max problems, where the objective function is the maximum of nonlinear functions. In addition, there may be any set of equality or inequality constraints. It is assumed that all individual problem functions are continuously differentiable.
 

PDEFIT: Solves parameter estimation problems in one-dimensional partial differential equations and partial differential algebraic equations 

MODFIT: Solves parameter estimation in explicit model functions, Laplace transforms, steady state systems, systems of ordinary and algebraic differential equations

 
Modelling Language:

 

PCOMP: Modeling language with automatic differentiation

 
Test Problems:
 
Test problems for nonlinear programming
 
Test problems for nonlinear mixed-integer optimization
 
Test problems for data fitting in dynamical systems
 

 

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