Results 141 to 150 of about 19,030 (190)

Trends and perspectives in deterministic MINLP optimization for integrated planning, scheduling, control, and design of chemical processes. [PDF]

Rev Chem Eng
Liñán DA, Ricardez-Sandoval LA.
europepmc   +1 more source

Selective Inference for Sparse Graphs via Neighborhood Selection. [PDF]

Electron J Stat
Huang Y, Panigrahi S, Dempsey W.
europepmc   +1 more source

Gradient Descent Provably Escapes Saddle Points in the Training of Shallow ReLU Networks. [PDF]

J Optim Theory Appl
Cheridito P, Jentzen A, Rossmannek F.
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Sparse regularization inversion method for transient electromagnetic data and high-resolution prospection of subsurface targets. [PDF]

Sci Rep
Zhou Z, He K, Kang S, Zhang J, Chi F, Ren J, Zhang W, Ning C.   +7 more
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Letter to the Editor - Update from Ukraine: Project Results in Oncology Telerehabilitation Approved at the National Cancer Institute and Showcased at the 4th National PM&R Congress. [PDF]

Int J Telerehabil
Malakhov KS, Malakhov KS.
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On Robustness of the Normalized Subgradient Method with randomly Corrupted Subgradients

, 2020
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Subgradient Methods for Saddle-Point Problems

Journal of Optimization Theory and Applications, 2009
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nedić, A., Ozdaglar, A.
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Subgradient Method for Nonconvex Nonsmooth Optimization

Journal of Optimization Theory and Applications, 2012
Based on the notion of quasisecants introduced by \textit{A. M. Bagirov} and \textit{A. N. Ganjehlou} [Optim. Methods Softw. 25, No. 1, 3--18 (2010; Zbl 1202.65072)], the authors develop a version of the subgradient method for solving nonconvex nonsmooth optimization problems. Quasisecants are subgradients computed in some neighborhood of a point.
Bagirov, A. M., Jin, L., Karmitsa, N., Al Nuaimat, A., Sultanova, N.   +4 more
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Variable target value subgradient method

Mathematical Programming, 1990
Polyak's subgradient algorithm for nondifferentiable optimization problems requires prior knowledge of the optimal value of the objective function to find an optimal solution. In this paper we extend the convergence properties of the Polyak's subgradient algorithm with a fixed target value to a more general case with variable target values.
KIM, SH Kim, Sehun, AHN, HU, CHO, SC
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