SNAP algorithm finds approximate SOSPs for non-convex linearly constrained problems in O(1/ε^{2.5}) iterations with polynomial per-iteration complexity by leveraging strict complementarity on generic instances.
A proximal alternating directi on method of multiplier for linearly constrained nonconvex minimization
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The authors reformulate unimodular sequence design as a consensus nonconvex problem and propose ADMM/PDMM algorithms that converge to stationary points and outperform prior methods in simulations.
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SNAP: Finding Approximate Second-Order Stationary Solutions Efficiently for Non-convex Linearly Constrained Problems
SNAP algorithm finds approximate SOSPs for non-convex linearly constrained problems in O(1/ε^{2.5}) iterations with polynomial per-iteration complexity by leveraging strict complementarity on generic instances.
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Designing Unimodular Sequences with Optimized Auto/cross-correlation Properties via Consensus-ADMM/PDMM Approaches
The authors reformulate unimodular sequence design as a consensus nonconvex problem and propose ADMM/PDMM algorithms that converge to stationary points and outperform prior methods in simulations.