| |
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.
|
|
MISQPOA: As above,
but with additional outer-approximation stabilization to guarantee
convergence for convex programs. Relaxable integer variables or convex
problem functions are not required. Derivatives subject to integer variables
are internally approximated and a BFGS-update matrix is generated. Requires
MISQP.
|
|
MINLPB4:
Branch-and-bound implementation based on MISQP. Relaxable integer variables
or convex problem functions are not required. Derivatives subject to integer
variables are internally approximated. Requires
MISQP.
|
| |
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, ...)
|
| |
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.
|
| |
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
|
