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• W2963576077 abstract "This paper considers convex programs with a general (possibly nondifferentiable) convex objective function and Lipschitz continuous convex inequality constraint functions. A simple algorithm is developed and achieves an $O(1/t)$ convergence rate. Similar to the classical dual subgradient algorithm and the alternating direction method of multipliers (ADMM) algorithm, the new algorithm has a parallel implementation when the objective and constraint functions are separable. However, the new algorithm has a faster $O(1/t)$ convergence rate compared with the best known $O(1/sqrt{t})$ convergence rate for the dual subgradient algorithm with primal averaging. Further, it can solve convex programs with nonlinear constraints, which cannot be handled by the ADMM algorithm. The new algorithm is applied to a multipath network utility maximization problem and yields a decentralized flow control algorithm with the fast $O(1/t)$ convergence rate." @default.
• W2963576077 created "2019-07-30" @default.
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• W2963576077 date "2017-01-01" @default.
• W2963576077 modified "2023-10-16" @default.
• W2963576077 title "A Simple Parallel Algorithm with an $O(1/t)$ Convergence Rate for General Convex Programs" @default.
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• W2963576077 doi "https://doi.org/10.1137/16m1059011" @default.
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