More test examples for nonlinear programming codes
More test examples for nonlinear programming codes
New computer methods for global optimization
New computer methods for global optimization
Convergence qualification of adaptive partition algorithms in global optimization
Mathematical Programming: Series A and B
A deterministic algorithm for global optimization
Mathematical Programming: Series A and B
Subdivision Direction Selection in Interval Methods for Global Optimization
SIAM Journal on Numerical Analysis
Experiments with new stochastic global optimization search techniques
Computers and Operations Research
Testing Unconstrained Optimization Software
ACM Transactions on Mathematical Software (TOMS)
Symbolic-interval cooperation in constraint programming
Proceedings of the 2001 international symposium on Symbolic and algebraic computation
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Multisection in Interval Branch-and-Bound Methods for Global Optimization – I. Theoretical Results
Journal of Global Optimization
Experiments with a new selection criterion in a fast interval optimization algorithm
Journal of Global Optimization
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
An interval component for continuous constraints
Journal of Computational and Applied Mathematics - Special issue: Proceedings of the international conference on linear algebra and arithmetic, Rabat, Morocco, 28-31 May 2001
Interval Analysis on Directed Acyclic Graphs for Global Optimization
Journal of Global Optimization
Efficient interval partitioning for constrained global optimization
Journal of Global Optimization
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In bound constrained global optimization problems, partitioning methods utilizing Interval Arithmetic are powerful techniques that produce reliable results. Subdivision direction selection is a major component of partitioning algorithms and it plays an important role in convergence speed. Here, we propose a new subdivision direction selection scheme that uses symbolic computing in interpreting interval arithmetic operations. We call this approach symbolic interval inference approach (SIIA). SIIA targets the reduction of interval bounds of pending boxes directly by identifying the major impact variables and re-partitioning them in the next iteration. This approach speeds up the interval partitioning algorithm (IPA) because it targets the pending status of sibling boxes produced. The proposed SIIA enables multi-section of two major impact variables at a time. The efficiency of SIIA is illustrated on well-known bound constrained test functions and compared with established subdivision direction selection methods from the literature.