Finite domain constraint programming systems

Tutorial at CP'2002, Principles and Practice of Constraint Programming. Powerpoint slides.</p

Global Optimization based on Contractor Programming: an Overview of the IBEX library

International audienceIBEX is an open-source C++ library for constraint processing over real numbers. It provides reliable algorithms for handling non-linear constraints. In particular, roundoff errors are also taken into account. It is based on interval arithmetic and affine arithmetic. The main feature of IBEX is its ability to build strategies declaratively through the contractor programming paradigm. It can also be used as a black-box solver or with an AMPL interface. Two emblematic problems that can be addressed are: (i) System solving: A guaranteed enclosure for each solution of a system of (nonlinear) equations is calculated; (ii) Global optimization: A global minimizer of some function under non-linear constraints is calculated with guaranteed and reliable bounds on the objective minimum

The impossibility of an effective theory of policy in a complex economy

theory of policy,dynamical system,computation universality,recursive rule,complex economy

Searching the solution space in constructive geometric constraint solving with genetic algorithms

Geometric problems defined by constraints have an exponential number of solution instances in the number of geometric elements involved. Generally, the user is only interested in one instance such that besides fulfilling the geometric constraints, exhibits some additional properties. Selecting a solution instance amounts to selecting a given root every time the geometric constraint solver needs to compute the zeros of a multi valuated function. The problem of selecting a given root is known as the Root Identification Problem. In this paper we present a new technique to solve the root identification problem. The technique is based on an automatic search in the space of solutions performed by a genetic algorithm. The user specifies the solution of interest by defining a set of additional constraints on the geometric elements which drive the search of the genetic algorithm. The method is extended with a sequential niche technique to compute multiple solutions. A number of case studies illustrate the performance of the method.Postprint (published version