Inexact Infeasible Arc-Search Interior-Point Method for Linear Programming Problems

Core Concepts

Proposing an inexact infeasible arc-search interior-point method for efficient linear programming solutions.

Abstract

Inexact interior-point methods (IPMs) in linear programming.
Arc-search IPMs approximate the central path with an ellipsoidal arc.
Proposed inexact infeasible arc-search interior-point method.
Reduction in the number of iterations compared to existing methods.
Polynomial-time algorithm with convergence analysis.
Numerical experiments show significant reduction in iteration numbers.
Detailed discussion on LP problems, formulas, and convergence analysis.
Introduction of the II-arc-IPM method integrating inexact and arc-search IPMs.
Framework of the proposed method with perturbation and Newton system solutions.
Proof of convergence and polynomial iteration complexity.
Assumptions, notations, and key concepts in LP problems.

Stats

"The numerical experiments with the conjugate gradient method show that the proposed method can reduce the number of iterations compared to an existing method for benchmark problems; the numbers of iterations are reduced to two-thirds for more than 70% of the problems."

Quotes

Key Insights Distilled From

An inexact infeasible arc-search interior-point method for linear programming problems

by Einosuke Iid... at arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18155.pdf

Deeper Inquiries

How can the proposed method be applied to other optimization problems

The proposed inexact infeasible arc-search interior-point method can be applied to a wide range of optimization problems beyond linear programming. This method's framework can be adapted to solve second-order cone programming, semidefinite programming, and other convex optimization problems. By modifying the equations and constraints to fit the specific problem structure, the algorithm can efficiently handle various optimization tasks. Additionally, the integration of quantum linear system algorithms can further enhance the method's applicability to quantum computing optimization problems, providing a versatile and powerful tool for solving complex optimization challenges.

What are the potential drawbacks or limitations of the inexact infeasible arc-search interior-point method

While the inexact infeasible arc-search interior-point method offers significant advantages in terms of reducing the number of iterations and improving computational efficiency, there are potential drawbacks and limitations to consider. One limitation is the sensitivity to the choice of parameters such as the centering parameter, enforcing parameter, and step size. Improper selection of these parameters can lead to convergence issues or suboptimal solutions. Additionally, the method's complexity may increase when applied to large-scale optimization problems with high-dimensional variables and constraints, potentially affecting the algorithm's scalability and performance. Furthermore, the reliance on quantum linear system algorithms may introduce additional computational overhead and complexity, especially in practical implementations.

How does the integration of quantum linear system algorithms impact the efficiency of the proposed method

The integration of quantum linear system algorithms into the proposed inexact infeasible arc-search interior-point method can significantly impact its efficiency and performance. Quantum linear system algorithms have the potential to solve linear equation systems faster and with better scaling properties compared to classical methods. By leveraging quantum computing techniques, the proposed method can potentially achieve faster convergence, reduced computational complexity, and improved scalability for solving optimization problems. This integration opens up new possibilities for tackling large-scale optimization problems that may be challenging for classical computing methods, paving the way for advancements in optimization algorithms and quantum computing applications.