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arXiv · 2107.09631

A Semismooth Newton-Type Method for the Nearest Doubly Stochastic Matrix Problem

Hao Hu·Haesol Im·Xinxin Li·Henry Wolkowicz
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Abstract

We study a semismooth Newton-type method for the nearest doubly stochastic matrix problem where both differentiability and nonsingularity of the Jacobian can fail. The optimality conditions for this problem are formulated as a system of strongly semismooth functions. We show that the so-called local error bound condition does not hold for this system. Thus the guaranteed convergence rate of Newton-type methods is at most superlinear. By exploiting the problem structure, we construct a modified two step semismooth Newton method that guarantees a nonsingular Jacobian matrix at each iteration, and that converges to the nearest doubly stochastic matrix quadratically. To the best of our knowledge, this is the first Newton-type method which converges $Q$-quadratically in the absence of the local error bound condition.

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Hao Hu, Haesol Im, Xinxin Li, Henry Wolkowicz. 2021-07-20. A Semismooth Newton-Type Method for the Nearest Doubly Stochastic Matrix Problem. https://arxiv.org/abs/2107.09631

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