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》 资源 》论文》A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements

A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements

2020-02-04 | |  63 |   36 |   0

Abstract 

We propose a simple, scalable, and fast gradient descent algorithm to optimize a nonconvex objective for the rank minimization problem and a closely related family of semidefinite programs. With image.png random measurements of a positive semidefinite n×n matrix of rank r and condition number image.png, our method is guaranteed to converge linearly to the global optimum.

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