"Low-Rank Convex-Convex Quadratic Fractional Programming"Krishtal, IlyaWe present an efficient algorithm for solving fractional programming problems whose objective functions are the ratio of a low-rank quadratic to a positive definite quadratic with convex constraints. The proposed algorithm for these convex-convex problems is based on the Shen-Yu Quadratic Transform, designed to find stationary points of concave-convex sum-of-ratios problems. We show that our algorithm performs better than previous algorithms for low-rank problems. As an application of our algorithm, we discuss the problem of minimizing the mean squared error in linear inverse problems related to dynamical sampling on graphs. |
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