<ul><li>This paper introduces FADMM, the first Alternating Direction Method of Multipliers tailored for a class of structured fractional minimization problems.</li><li>FADMM decouples the original problem into linearized proximal subproblems, featuring two variants: FADMM-D and FADMM-Q.</li><li>The authors establish that FADMM converges to ε-approximate critical points of the problem within an oracle complexity of O(1/ε^3).</li><li>Experiments on synthetic and real-world datasets demonstrate the effectiveness of FADMM in various applications, including sparse Fisher discriminant analysis and robust sparse recovery.</li></ul>

ADMM for Structured Fractional Minimization

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