Kernel function based interior-point methods for horizontal linear complementarity problems
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Publication:394593
DOI10.1186/1029-242X-2013-215zbMath1285.90075WikidataQ59292643 ScholiaQ59292643MaRDI QIDQ394593
Publication date: 27 January 2014
Published in: Journal of Inequalities and Applications (Search for Journal in Brave)
Complementarity and equilibrium problems and variational inequalities (finite dimensions) (aspects of mathematical programming) (90C33) Interior-point methods (90C51)
Related Items
An infeasible interior-point algorithm with full-Newton steps for \(P_*(\kappa)\) horizontal linear complementarity problems based on a kernel function ⋮ An interior point method for \(P_*(\kappa)\)-horizontal linear complementarity problem based on a new proximity function ⋮ A primal-dual interior point method for \(P_{\ast}\left(\kappa \right)\)-HLCP based on a class of parametric kernel functions
Cites Work
- Interior-point methods for linear optimization based on a kernel function with a trigonometric barrier term
- Polynomial interior-point algorithms for \(P_*(\kappa )\) horizontal linear complementarity problem
- A new large-update interior point algorithm for \(P_{*}(\kappa)\) LCPs based on kernel functions
- Exploring complexity of large update interior-point methods for \(P_*(\kappa )\) linear complementarity problem based on kernel function
- A PRIMAL-DUAL INTERIOR-POINT ALGORITHM BASED ON A NEW KERNEL FUNCTION
- Unified Analysis of Kernel-Based Interior-Point Methods for $P_*(\kappa)$-Linear Complementarity Problems
- A new proximity function generating the best known iteration bounds for both large-update and small-update interior-point methods
- A Comparative Study of Kernel Functions for Primal-Dual Interior-Point Algorithms in Linear Optimization
- A smoothing Gauss-Newton method for the generalized HLCP
