End-To-End Resource Analysis for Quantum Interior-Point Methods and Portfolio Optimization [PDF]
PRX Quantum, 2023 We study quantum interior-point methods (QIPMs) for second-order cone programming (SOCP), guided by the example use case of portfolio optimization (PO).
Alexander M. Dalzell +10 more
doaj +2 more sources
Sparse Approximations with Interior Point Methods [PDF]
SIAM Review, 2022 Large-scale optimization problems that seek sparse solutions have become ubiquitous. They are routinely solved with various specialized first-order methods. Although such methods are often fast, they usually struggle with not-so-well conditioned problems.
Valentina De Simone +4 more
openaire +6 more sources
Matrix Scaling and Balancing via Box Constrained Newton's Method and Interior Point Methods [PDF]
IEEE Annual Symposium on Foundations of Computer Science, 2017 In this paper, we study matrix scaling and balancing, which are fundamental problems in scientific computing, with a long line of work on them that dates back to the 1960s.
Cohen, Michael B. +3 more
core +2 more sources
Interior point methods 25 years later [PDF]
European Journal of Operational Research, 2012 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
J. Gondzio
openaire +4 more sources
Accelerating Optimal Power Flow with GPUs: SIMD Abstraction of Nonlinear Programs and Condensed-Space Interior-Point Methods [PDF]
Electric power systems research, 2023 This paper introduces a framework for solving alternating current optimal power flow (ACOPF) problems using graphics processing units (GPUs). While GPUs have demonstrated remarkable performance in various computing domains, their application in ACOPF has
Sungho Shin +2 more
semanticscholar +1 more source
Riemannian Interior Point Methods for Constrained Optimization on Manifolds [PDF]
Journal of Optimization Theory and Applications, 2022 We extend the classical primal-dual interior point method from the Euclidean setting to the Riemannian one. Our method, named the Riemannian interior point method, is for solving Riemannian constrained optimization problems.
Zhijian Lai, Akiko Yoshise
semanticscholar +1 more source
Interior point methods are not worse than Simplex [PDF]
IEEE Annual Symposium on Foundations of Computer Science, 2022 Whereas interior point methods provide polynomial-time linear programming algorithms, the running time bounds depend on bit-complexity or condition measures that can be unbounded in the problem dimension.
Xavier Allamigeon +4 more
semanticscholar +1 more source
General-purpose preconditioning for regularized interior point methods [PDF]
Computational optimization and applications, 2021 In this paper we present general-purpose preconditioners for regularized augmented systems, and their corresponding normal equations, arising from optimization problems. We discuss positive definite preconditioners, suitable for CG and MINRES.
J. Gondzio +2 more
semanticscholar +1 more source
An Inexact Feasible Quantum Interior Point Method for Linearly Constrained Quadratic Optimization [PDF]
Entropy, 2023 Quantum linear system algorithms (QLSAs) have the potential to speed up algorithms that rely on solving linear systems. Interior point methods (IPMs) yield a fundamental family of polynomial-time algorithms for solving optimization problems. IPMs solve a
Zeguan Wu +4 more
semanticscholar +1 more source
Improvements to Quantum Interior Point Method for Linear Optimization [PDF]
ACM Transactions on Quantum Computing, 2023 Quantum linear system algorithms (QLSAs) have the potential to speed up Interior Point Methods (IPMs). However, a major bottleneck is the inexactness of quantum tomography to extract classical solutions from quantum states.
Mohammadhossein Mohammadisiahroudi +4 more
semanticscholar +1 more source