A superlinearly convergent wide-neighborhood predictor-corrector interior-point algorithm for linear programming
From MaRDI portal
Publication:1677164
DOI10.1007/s12190-016-1055-2zbMath1374.90378OpenAlexW2522750699MaRDI QIDQ1677164
Publication date: 10 November 2017
Published in: Journal of Applied Mathematics and Computing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s12190-016-1055-2
Newton-type methods (49M15) Complementarity and equilibrium problems and variational inequalities (finite dimensions) (aspects of mathematical programming) (90C33) Numerical methods for variational inequalities and related problems (65K15)
Related Items
A wide-neighborhood predictor-corrector interior-point algorithm for linear complementarity problems, A step-truncated method in a wide neighborhood interior-point algorithm for linear programming
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A new wide neighborhood primal-dual infeasible-interior-point method for symmetric cone programming
- An \(O(\sqrt nL)\) iteration primal-dual second-order corrector algorithm for linear programming
- Convergence behavior of interior-point algorithms
- Superlinear and quadratic convergence of primal-dual interior-point methods for linear programming revisited
- Interior-point methods
- Mehrotra-type predictor-corrector algorithms for sufficient linear complementarity problem
- Polynomial convergence of second-order mehrotra-type predictor-corrector algorithms over symmetric cones
- A new \(O(\sqrt{n}L)\)-iteration predictor-corrector algorithm with wide neighborhood for semidefinite programming
- A quadratically convergent \(O(\sqrt n\;L)\)-iteration algorithm for linear programming
- A second-order Mehrotra-type predictor-corrector algorithm with a new wide neighbourhood for semi-definite programming
- On the Superlinear and Quadratic Convergence of Primal-Dual Interior Point Linear Programming Algorithms
- Solving large-scale linear programs by interior-point methods under the Matlab∗Environment†
- On Adaptive-Step Primal-Dual Interior-Point Algorithms for Linear Programming
- A Superlinearly Convergent Polynomial Primal-Dual Interior-Point Algorithm for Linear Programming
- Interior Point Methods for Sufficient Horizontal LCP in a Wide Neighborhood of the Central Path with Best Known Iteration Complexity
- An O$(\sqrtn L)$ Iteration Primal-dual Path-following Method, Based on Wide Neighborhoods and Large Updates, for Monotone LCP
