Constraint propagation with interval labels
Artificial Intelligence
Arc-consistency for continuous variables
Artificial Intelligence
Presolving in linear programming
Mathematical Programming: Series A and B
On Finitely Terminating Branch-and-Bound Algorithms for Some Global Optimization Problems
SIAM Journal on Optimization
A Finite Algorithm for Global Minimization ofSeparable Concave Programs
Journal of Global Optimization
MINLPLib--A Collection of Test Models for Mixed-Integer Nonlinear Programming
INFORMS Journal on Computing
Global optimization of mixed-integer nonlinear programs: A theoretical and computational study
Mathematical Programming: Series A and B
Interval Analysis on Directed Acyclic Graphs for Global Optimization
Journal of Global Optimization
Integrated Methods for Optimization (International Series in Operations Research & Management Science)
Introduction to Interval Analysis
Introduction to Interval Analysis
Branching and bounds tighteningtechniques for non-convex MINLP
Optimization Methods & Software - GLOBAL OPTIMIZATION
Interval propagation and search on directed acyclic graphs for numerical constraint solving
Journal of Global Optimization
An analysis of slow convergence in interval propagation
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
On convex relaxations of quadrilinear terms
Journal of Global Optimization
The reformulation-optimization software engine
ICMS'10 Proceedings of the Third international congress conference on Mathematical software
Experiments with a feasibility pump approach for nonconvex MINLPs
SEA'10 Proceedings of the 9th international conference on Experimental Algorithms
The complexity of integer bound propagation
Journal of Artificial Intelligence Research
Compact relaxations for polynomial programming problems
SEA'12 Proceedings of the 11th international conference on Experimental Algorithms
Discrete Applied Mathematics
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The search tree size of the spatial Branch-and-Bound algorithm for Mixed-Integer Nonlinear Programming depends on many factors, one of which is the width of the variable ranges at every tree node. A range reduction technique often employed is called Feasibility Based Bounds Tightening, which is known to be practically fast, and is thus deployed at every node of the search tree. From time to time, however, this technique fails to converge to its limit point in finite time, thereby slowing the whole Branch-and-Bound search considerably. In this paper we propose a polynomial time method, based on solving a linear program, for computing the limit point of the Feasibility Based Bounds Tightening algorithm applied to linear equality and inequality constraints.