Software Repositories

mirror site of the Leibniz System Software

Leibniz System: Development Tool for Logic-based
            Intelligent Systems The Leibniz System is a software package created by Klaus Truemper for the development and implementation of logic-based intelligent systems. The package covers various aspect of the construction of such systems with modules for
  • logic computation
  • learning logic formulas from data
  • discretization of data
  • subgroup discovery from data
  • data estimation by a lazy learner
  • dimension reduction of models
  • decomposition of graphs and matrices
  • solution of constrained optimization problems involving single or multiple objective functions

Downloading and Installation of Leibniz System

  1. Get the files leibniz.complete.zip and installation.pdf.
  2. Install the Leibniz System using the instructions in the file installation.pdf.
  3. Optional: Send email to klaus@utdallas.edu with "Leibniz System Download" in the subject line. We will inform you about updates of the system. No other use will be made of the information.
  4. More mirror sites: Are being developed.

The Derivative-Free Library (DFL) is available here.

The library offers a variety of codes (freely available under the GPL) to solve optimization problems when first order information on the objective or constraint functions are not available. Such problems are also known as black-box (BB) or simulation-based (SB) optimization problems. They are ubiquitous in the real world and especially in industrial design and production. In particular, the library offers:
  • Local optimization solvers:
    • SDBOX, an algorithm for bound constrained optimization problems
    • SDPEN, an algorithm for general (inequality) constrained optimization problems
    • DFN, a linesearch-based algorithm for nonsmooth constrained optimization problems
    • SDMINMAX, an algorithm for finite minimax optimization problems
  • Global optimization solvers:
    • ACRS, an Adaptively Controlled Random Search algorithm for bound constrained global optimization problems
    • DDFSA, a Simulated Annealing algorithm for bound constrained global optimization problems
    • DFSA, a Simulated Annealing algorithm for general constrained global optimization problems
    • DIRMIN, a DiRect algorithm with derivative-free local searches for bound constrained global optimization problems
    • DIRDFN, a DiRect algorithm with derivative-free local searches for general constrained global optimization problems
    • DIRECT, an implementation of the DiRect algorithm for bound constrained global optimization problems
  • Mixed-integer optimization solvers:
    • DFL box, a linesearch program for bound constrained Mixed Integer NonLinear Programming
    • DFL gen, a linesearch program for general (inequality) constrained Mixed Integer NonLinear Programming
  • Multiobjective optimization solvers:
    • DFMO, a linesearch program for Multiobjective Optimization

A new branch&bound algorithm for Standard Quadratic Programming (StQP) problems is available here

StQP_BB is a branch&bound algorithm developped by G. Liuzzi (CNR-IASI), M. Locatelli (University of Parma) and V. Piccialli (University of Rome "Tor Vergata"), to solve difficult standard quadratic programming problems to global optimality. StQP_BB main features are:
  • implicit enumeration of all the KKT (stationary) points of the problem
  • use of an efficient polyhedral bounding technique
  • customizable B&B tree exploration (best-bound or depth-first policies)
  • possibility to use binary or n-ary node generation
  • use the power of Gurobi to efficiently solve the LP subproblems


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