Improved Interval Constraint Propagation for Constraints on Partial Derivatives

Authors:
Evgueni S. Petrov;Frédéric Benhamou
Affiliations:
-;-
Venue:
ICCS '02 Proceedings of the International Conference on Computational Science-Part II
Year:
2002

Citing 5
Cited 0

Evaluating derivatives: principles and techniques of algorithmic differentiation

Evaluating derivatives: principles and techniques of algorithmic differentiation
Progress in the Solving of a Circuit Design Problem

Journal of Global Optimization
Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming

CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
Interval Constraint Logic Programming

Selected Papers from Constraint Programming: Basics and Trends
Numerica: a modeling language for global optimization

IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2

Quantified Score

Hi-index	0.00

Visualization

Abstract

Automatic differentiation (AD) automatically transforms programs which calculate elementary functions into programs which calculate the gradients of these functions. Unlike other differentiation techniques, AD allows one to calculate the gradient of any function at the cost of at most 5 values of the function (in terms of time). Interval constraint programming (ICP) is a part of constraint programming focused on representation and processing of nonlinear constraints. We adapt AD to the context of ICP and obtain an algorithm which transforms elementary functions into constraints specifying their gradient. We describe some experiments with implementation of our algorithm in the logic programming language ECLiPSe.