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1814. Ai-Zhang-type interior-point algorithms for solving linear complementarity problems
Invited abstract in session MB-38: Interior point methods, stream Conic Optimization: Theory, Algorithms, and Applications.
Monday, 10:30-12:00Room: 34 (building: 306)
Authors (first author is the speaker)
| 1. | Anita Varga
|
| Corvinus Centre for Operations Research, Corvinus University of Budapest | |
| 2. | Marianna E.-Nagy
|
| Corvinus University of Budapest |
Abstract
This talk investigates a long-step interior-point framework for solving linear complementarity problems. The algorithmic framework combines the approach of Ai and Zhang and the algebraically equivalent transformation technique proposed by Darvay. We investigate a set of sufficient conditions on the transformation function applied in the algebraically equivalent transformation technique, under which the general algorithmic framework's convergence and best known iteration complexity can be proved.
We analyze the theoretical and practical role of different neighborhood definitions. As expected, the proposed conditions depend on the definition of the neighborhood of the central path.
Keywords
- Interior Point Methods
- Mathematical Programming
Status: accepted
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