Abstract
We give a proximal bundle method for constrained convex optimization. It requires only evaluating the problem functions and their subgradients with an unknown accuracy $\epsilon$. Employing a combination of the classic method of centers' improvement function with an exact penalty function, it does not need a feasible starting point. It asymptotically finds points with at least $\epsilon$-optimal objective values that are $\epsilon$-feasible. When applied to the solution of linear programming problems via column generation, it allows for $\epsilon$-accurate solutions of column generation subproblems.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
