The Convex Relaxation Method on Deconvolution Model withMultiplicative Noise
Yu-Mei Huang, M. Ng, T. Zeng
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Sample Average Approximation with Sparsity-Inducing Penalty for High-Dimensional Stochastic Programming. [PDF]
Liu H, Wang X, Yao T, Li R, Ye Y.
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A general double-proximal gradient algorithm for d.c. programming. [PDF]
Banert S, BoČ› RI.
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Optimality condition and iterative thresholding algorithm for [Formula: see text]-regularization problems. [PDF]
Jiao H, Chen Y, Yin J.
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Fast convergence of trust-regions for non-isolated minima via analysis of CG on indefinite matrices. [PDF]
Rebjock Q, Boumal N.
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An efficient method for generalized linear multiplicative programming problem with multiplicative constraints. [PDF]
Zhao Y, Liu S.
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A DECOMPOSITION ALGORITHM FOR TWO-STAGE STOCHASTIC PROGRAMS WITH NONCONVEX RECOURSE FUNCTIONS. [PDF]
Li H, Cui Y.
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A radial basis function method for noisy global optimisation. [PDF]
Banholzer D, Fliege J, Werner R.
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A convergent relaxation of the Douglas-Rachford algorithm. [PDF]
Thao NH.
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