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arXiv · 1612.04782

An Improved Deterministic Rescaling for Linear Programming Algorithms

Rebecca Hoberg·Thomas Rothvoss
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Abstract

The perceptron algorithm for linear programming, arising from machine learning, has been around since the 1950s. While not a polynomial-time algorithm, it is useful in practice due to its simplicity and robustness. In 2004, Dunagan and Vempala showed that a randomized rescaling turns the perceptron method into a polynomial time algorithm, and later Pe\~{n}a and Soheili gave a deterministic rescaling. In this paper, we give a deterministic rescaling for the perceptron algorithm that improves upon the previous rescaling methods by making it possible to rescale much earlier. This results in a faster running time for the rescaled perceptron algorithm. We will also demonstrate that the same rescaling methods yield a polynomial time algorithm based on the multiplicative weights update method. This draws a connection to an area that has received a lot of recent attention in theoretical computer science.

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Rebecca Hoberg, Thomas Rothvoss. 2016-12-14. An Improved Deterministic Rescaling for Linear Programming Algorithms. https://arxiv.org/abs/1612.04782

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