SIAM Review
A Spectral Bundle Method for Semidefinite Programming
SIAM Journal on Optimization
Mathematical Programming for Data Mining: Formulations and Challenges
INFORMS Journal on Computing
On the Relation Between Option and Stock Prices: A Convex Optimization Approach
Operations Research
Journal of Computational and Applied Mathematics - Special issue: Papers presented at the 1st Sino--Japan optimization meeting, 26-28 October 2000, Hong Kong, China
Computational Optimization and Applications
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We will propose an outer-approximation (cutting plane) method for minimizing a function fX subject to semi-definite constraints on the variables X∈Rn. A number of efficient algorithms have been proposed when the objective function is linear. However, there are very few practical algorithms when the objective function is nonlinear. An algorithm to be proposed here is a kind of outer-approximation(cutting plane) method, which has been successfully applied to several low rank global optimization problems including generalized convex multiplicative programming problems and generalized linear fractional programming problems, etc. We will show that this algorithm works well when f is convex and n is relatively small. Also, we will provide the proof of its convergence under various technical assumptions.