A Low Complexity Interior-Point Algorithm for Linear Programming | Zendy

Michael J. Todd | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

A Low Complexity Interior-Point Algorithm for Linear Programming

Author(s) -

Michael J. Todd

Publication year - 1992

Publication title -

siam journal on optimization

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.066

H-Index - 136

eISSN - 1095-7189

pISSN - 1052-6234

DOI - 10.1137/0802011

Subject(s) - interior point method , mathematics , linear programming , algorithm , affine transformation , simple (philosophy) , scaling , point (geometry) , function (biology) , mathematical optimization , dual (grammatical number) , combinatorics , pure mathematics , art , philosophy , geometry , literature , epistemology , evolutionary biology , biology

This paper describes an interior-point algorithm for linear programming that is almost as simple as the affine-scaling method and yet achieves the currently best complexity of $O(\sqrt{n} t)$ iterations to attain precision t. The basic algorithm needs neither dual estimates nor lower bounds, although its analysis is based on Ye’s results for the primal–dual potential function. Some computationally preferable variants are also presented.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research