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In this paper, we aim to engineer and construct a solver for such matrices directly in circuit representation. We employ ideas from the matrix-free interior ...
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May 26, 2016 · In this paper, we turn to these challenges: we introduce a rich modeling language, for which an interior-point method computes approximate ...
Feb 12, 2017 · To overcome this, we introduce a rich modeling framework for optimization problems that allows convenient codification of symbolic structure.
While logical features easily complicates the underlying model, often yielding intricate dependencies, we exploit and cache local structure using algebraic ...
This work develops an engine that can fully leverage the structure of symbolic representations to solve convex linear and quadratic optimization problems ...
It can be seen as an assembly language for hard combinatorial problems ranging from classification and regression in learning, to computing optimal policies and ...
In this article we describe an implementation of the Mehrotra's primal–dual interior-point method in the package . Usage of 's computer algebra system in our ...
Interior-point methods (also referred to as barrier methods or IPMs) are algorithms for solving linear and non-linear convex optimization problems.
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In this article we describe an implementation of the Mehrotra's primal–dual interior-point method in the package MATHEMATICA.
Nov 19, 2018 · In 1984, Karmarkar developed an Interior Point. Method for solving linear programs [2], which was asymptotically faster than ellipsoid and, more ...