Linear programming, complexity theory and elementary functional analysis
Mathematical Programming: Series A and B
A Bicriterial Optimization Problem of Antenna Design
Computational Optimization and Applications
Warm start of the primal-dual method applied in the cutting-plane scheme
Mathematical Programming: Series A and B
A mathematical view of interior-point methods in convex optimization
A mathematical view of interior-point methods in convex optimization
Warm-Start Strategies in Interior-Point Methods for Linear Programming
SIAM Journal on Optimization
Warm Start and ε-Subgradients in a Cutting Plane Scheme
Computational Optimization and Applications
Reoptimization With the Primal-Dual Interior Point Method
SIAM Journal on Optimization
Computational Optimization and Applications
A Semidefinite Programming approach for solving Multiobjective Linear Programming
Journal of Global Optimization
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In multicriteria optimization, several objective functions have to be minimized simultaneously. We propose a new efficient method for approximating the solution set of a multicriteria optimization problem, where the objective functions involved are arbitrary convex functions and the set of feasible points is convex. The method is based on generating warm-start points for an efficient interior-point algorithm, while the approximation computed consists of a finite set of discrete points. Polynomial-time complexity results for the method proposed are derived. In these estimates, the number of operations per point decreases when the number of points generated for the approximation increases. This reduced theoretical complexity estimate is a novel feature and is not observed in standard solution techniques for multicriteria optimization problems.