
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.


LargeScale
Nonlinear Optimization Solver:


NLPIP:
Combined SQPIPM method for largescale optimization with limitedmemory
BFGS updates or Hessian of Lagrangian, taking sparsity of the Jacobian of
constraints into account.


MixedInteger Nonlinear Programming
Solvers:


MISQP: Implementation of
a trust region SQP algorithm for mixedinteger 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 outerapproximation 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 BFGSupdate matrix is generated. Requires
MISQP.


MINLPB4:
Branchandbound 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: Blackbox
optimizer, specially developed for mixed integer nonlinear programs (MINLPs),
but also applicable on a wide range of optimization problems (global
optimization, nonsmooth optimization, ...)


Quadratic Programming Solvers:


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


MIQL:
Solves mixedinteger 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 L_{1} 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 maximumnorm 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 minmax 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 onedimensional 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 mixedinteger optimization


Test problems
for data fitting in dynamical systems
