
The Leibniz System is a software package created by Klaus Truemper
for the
development and implementation of logicbased 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
 Get the files
leibniz.complete.zip and
installation.pdf.
 Install the Leibniz System using the instructions in the file
installation.pdf.
 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.
 More mirror sites: Are being developed.
The DerivativeFree 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
blackbox (BB) or simulationbased (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 linesearchbased 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 derivativefree local searches for bound constrained global optimization problems
 DIRDFN, a DiRect algorithm with derivativefree local searches for general constrained global optimization problems
 DIRECT, an implementation of the DiRect algorithm for bound constrained global optimization problems
 Mixedinteger 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

The SWIM  A
Software
Suite to Unveiling Crucial Nodes in Complex Networks
SWItchMiner (SWIM) is a software suite for the identification of a small
pool of genes, called switch genes, which are likely to be critically
associated with drastic changes in many biological settings. This
procedure was set in studying grapevine genome [see Plant Cell],
where switch genes were found to be master regulators of the
previously reported transcriptome remodeling that marks the
developmental shift from immature to mature growth in grapevine.
In another study switch genes have been investigated in
different human cancer types and the results strongly support the
hypothesis of their key role in cancer development [see Nature Scientific Reports].

 Software requirements:
SWIM has been developed in MATLAB ®1 (version R2013a including the Bioinformatics and Statistics
Toolboxes) and tested on the following operative systems:
 OSX 10.9.5
 GNU/Linux Ubuntu 14.04
 Windows 10 Pro
 Setting up:
 Install MATLAB ® and the Bioinformatics and Statistics Toolboxes
 Download and unzip the compressed file SWIM.zip that is available here. This will create a folder named SWIM in the current directory.
 The User Guide can downloaded from here.

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 (CNRIASI),
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 (bestbound or depthfirst policies)
 possibility to use binary or nary node generation
 use the power of Gurobi to efficiently solve the LP subproblems
