Results 21 to 30 of about 1,785,875 (332)
Interior-point algorithms for a class of convex optimization problems [PDF]
Yugoslav Journal of Operations Research, 2009 In this paper we consider interior-point methods (IPM) for the nonlinear, convex optimization problem where the objective function is a weighted sum of reciprocals of variables subject to linear constraints (SOR).
Lešaja Goran, Slaughter Verlynda N.
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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.
Michael B. Cohen +3 more
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Volumetric Barrier Cutting Plane Algorithms for Stochastic Linear Semi-Infinite Optimization
IEEE Access, 2020 In this paper, we study the two-stage stochastic linear semi-infinite programming with recourse to handle uncertainty in data defining (deterministic) linear semi-infinite programming.
Baha Alzalg, Asma Gafour, Lewa Alzaleq
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A primal–dual interior point method for a novel type-2 second order cone optimization
Results in Control and Optimization, 2021 In this paper, we define a new, special second order cone as a type-k second order cone. We focus on the case of k=2, which can be viewed as a second order conic optimization (SOCO) problem with an additional complicating variable.
Md Sarowar Morshed +2 more
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Learning to steer nonlinear interior-point methods
EURO Journal on Computational Optimization, 2019 Interior-point or barrier methods handle nonlinear programs by sequentially solving barrier subprograms with a decreasing sequence of barrier parameters.
Renke Kuhlmann
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Log-Barrier Interior Point Methods Are Not Strongly Polynomial [PDF]
SIAM Journal on applied algebra and geometry, 2017 We prove that primal-dual log-barrier interior point methods are not strongly polynomial, by constructing a family of linear programs with $3r+1$ inequalities in dimension $2r$ for which the number of iterations performed is in $\Omega(2^r)$.
Xavier Allamigeon +3 more
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The Symbolic Interior Point Method
Proceedings of the AAAI Conference on Artificial Intelligence, 2017 Numerical optimization is arguably the most prominent computational framework in machine learning and AI. It can be seen as an assembly language for hard combinatorial problems ranging from classification and regression in learning, to computing optimal policies and equilibria in decision theory, to entropy minimization in information ...
Mladenov, Martin +2 more
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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
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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
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A new search direction for full-Newton step infeasible interior-point method in linear optimization
Croatian Operational Research Review, 2023 In this work, we investigate a full Newton step infeasible interior-point method for linear optimization based on a new search direction which is obtained from an algebraic equivalent transformation of the central path system.
Behrouz Kheirfam
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