Abstract
We introduce two interior point algorithms for minimizing a convex function subject to linear constraints. Our algorithms require the solution of a nonlinear system of equations at each step. We show that if sufficiently good approximations to the solutions of the nonlinear systems can be found, then the primal-dual gap becomes less that ε in O( n |lnε|) steps, where n is the number of variables.
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.
