Suchergebnisse - regularized stochastic BFGS algorithm

RES: Regularized Stochastic BFGS Algorithm

Autoren: Aryan Mokhtari Alejandro Ribeiro

Quelle: IEEE Transactions on Signal Processing. 62:6089-6104

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, Optimization and Control (math.OC), FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Machine Learning (stat.ML), 14. Life underwater, 02 engineering and technology, Mathematics - Optimization and Control, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/pdf/1401.7625
http://arxiv.org/abs/1401.7625

Regularized stochastic BFGS algorithm

Autoren: Aryan Mokhtari Alejandro Ribeiro

Quelle: 2013 IEEE Global Conference on Signal and Information Processing. :1109-1112

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://arxiv.org/pdf/1401.7625v1
https://dblp.uni-trier.de/db/journals/corr/corr1401.html#MokhtariR14
https://www.seas.upenn.edu/~aribeiro/preprints/c_2013_mokhtari_ribeiro_a.pdf
https://arxiv.org/abs/1401.7625
https://ieeexplore.ieee.org/document/6737088/

RES: Regularized Stochastic BFGS Algorithm

Autoren: Mokhtari, Aryan Ribeiro, Alejandro

Index Begriffe: Computer Science - Machine Learning, Mathematics - Optimization and Control, Statistics - Machine Learning, text

URL: http://arxiv.org/abs/1401.7625

An Improved Technique for Pneumonia Infected Patients Image Recognition Based on Combination Algorithm of Smooth Generalized Pinball SVM and Variational Autoencoders

Autoren: Wachiraphong Ratiphaphongthon Wanida Panup Rabian Wangkeeree

Quelle: IEEE Access, Vol 10, Pp 107431-107445 (2022)

Schlagwörter: generalized pinball loss, feature noise, Support vector machine, image recognition, regularized stochastic BFGS algorithm, 0202 electrical engineering, electronic engineering, information engineering, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, TK1-9971

Zugangs-URL: https://doaj.org/article/f6a6cf36569c41d5bc6cc634a00638b2

Variance-Reduced Stochastic Quasi-Newton Methods for Decentralized Learning

Autoren: Zhang, Jiaojiao Liu, Huikang So, Anthony Man-Cho et al.

Quelle: IEEE Transactions on Signal Processing. 71:311-326

Schlagwörter: Costs, Convergence, Signal processing algorithms, Approximation algorithms, Cost function, Newton method, Computational complexity, Decentralized optimization, stochastic quasi-Newton methods, variance reduction, damped limited-memory DFP, damped limited-memory BFGS

Dateibeschreibung: print

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-325198

Iterative L1 Regularized Limited Memory Stochastic BFGS Algorithm and Numerical Experiments for Big Data Applications

Autoren: Patel, Vandan Yousefian, Farzad Heragu, Sunderesh et al.

Weitere Verfasser: Patel, Vandan Yousefian, Farzad Heragu, Sunderesh et al.

Dateibeschreibung: application/pdf

Relation: https://hdl.handle.net/11244/300037

Verfügbarkeit: https://hdl.handle.net/11244/300037

Stochastic Conjugate Gradient Algorithm With Variance Reduction.

Autoren: Jin, Xiao-Bo Zhang, Xu-Yao Huang, Kaizhu et al.

Quelle: IEEE Transactions on Neural Networks & Learning Systems; May2019, Vol. 30 Issue 5, p1360-1369, 10p

Schlagwörter: NONLINEAR equations, ALGORITHMS, SMOOTHNESS of functions, LINEAR equations, CONVEX functions, NONSMOOTH optimization, RECEIVER operating characteristic curves

Regularized stochastic BFGS algorithm.

Autoren: Mokhtari, Aryan Ribeiro, Alejandro

Quelle: 2013 IEEE Global Conference on Signal & Information Processing; 2013, p1109-1112, 4p

A New Stochastic Limited Memory BFGS Algorithm.

Autoren: Momeni, M. Peyghami, M. R. Tarzanagh, D. Ataee

Quelle: Journal of Mathematical Extension; 2020, Vol. 14 Issue 3, p65-83, 19p

Schlagwörter: STOCHASTIC analysis, OPTIMALITY theory (Linguistics), SECANT function, MATHEMATICAL models, MACHINE learning

A Stochastic Quasi-Newton Method for Large-Scale Nonconvex Optimization With Applications.

Autoren: Chen, Huiming Wu, Ho-Chun Chan, Shing-Chow et al.

Quelle: IEEE Transactions on Neural Networks & Learning Systems; Nov2020, Vol. 31 Issue 11, p4776-4790, 15p

Schlagwörter: QUASI-Newton methods, MACHINE learning, LOGISTIC regression analysis, LINEAR programming, PROBLEM solving

Stochastic L-BFGS: Improved Convergence Rates and Practical Acceleration Strategies.

Autoren: Renbo Zhao Haskell, William Benjamin Tan, Vincent Y. F.

Quelle: IEEE Transactions on Signal Processing; Mar2018, Vol. 66 Issue 5, p1155-1169, 15p

Schlagwörter: CONVERGENCE (Telecommunication), ALGORITHMS, COMPUTATIONAL complexity, LOGISTIC regression analysis, MATHEMATICAL optimization

Stochastic Super-resolution for Gaussian Microtextures.

Autoren: Pierret, Émile Galerne, Bruno

Quelle: SIAM Journal on Imaging Sciences; 2025, Vol. 18 Issue 2, p1176-1207, 32p

The regularized stochastic Nesterov's accelerated Quasi-Newton method with applications.

Autoren: Makmuang, Dawrawee Suppalap, Siwakon Wangkeeree, Rabian

Quelle: Journal of Computational & Applied Mathematics. Aug2023, Vol. 428, pN.PAG-N.PAG. 1p.

Schlagwörter: *QUASI-Newton methods, *PROBLEM solving

Faster Stochastic Quasi-Newton Methods.

Autoren: Zhang, Qingsong Huang, Feihu Deng, Cheng et al.

Quelle: IEEE Transactions on Neural Networks & Learning Systems; Sep2022, Vol. 33 Issue 9, p4388-4397, 10p

Schlagwörter: COMPUTATIONAL complexity, MACHINE tools, APPROXIMATION algorithms, INTERIOR-point methods, QUASI-Newton methods, ALGORITHMS

New BFGS method for unconstrained optimization problem based on modified Armijo line search.

Autoren: Wan, Zhong Teo, Kok Lay Shen, XianLong et al.

Quelle: Optimization; Feb2014, Vol. 63 Issue 2, p285-304, 20p

Schlagwörter: MATHEMATICAL optimization, ALGORITHMS, MATHEMATICAL formulas, STOCHASTIC convergence, NUMERICAL analysis, MATHEMATICAL analysis

On Stochastic and Deterministic Quasi-Newton Methods for Nonstrongly Convex Optimization: Asymptotic Convergence and Rate Analysis

Autoren: Farzad Yousefian Angelia Nedić Uday V. Shanbhag

Quelle: SIAM Journal on Optimization. 30:1144-1172

Schlagwörter: Optimization and Control (math.OC), FOS: Mathematics, 0211 other engineering and technologies, 02 engineering and technology, Mathematics - Optimization and Control

Zugangs-URL: http://arxiv.org/pdf/1710.05509
http://arxiv.org/abs/1710.05509
https://asu.pure.elsevier.com/en/publications/on-stochastic-and-deterministic-quasi-newton-methods-for-nonstron
https://dblp.uni-trier.de/db/journals/siamjo/siamjo30.html#YousefianNS20
https://epubs.siam.org/doi/abs/10.1137/17M1152474
https://doi.org/10.1137/17M1152474

HPPP: Halpern-Type Preconditioned Proximal Point Algorithms and Applications to Image Restoration.

Autoren: Zhang, Shuchang Zhang, Hui Wang, Hongxia

Quelle: SIAM Journal on Imaging Sciences; 2025, Vol. 18 Issue 2, p1493-1521, 29p

Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation

Autoren: Meng, Si Yi Vaswani, Sharan Laradji, Issam et al.

Index Begriffe: Computer Science - Machine Learning, Mathematics - Optimization and Control, Statistics - Machine Learning, text

URL: http://arxiv.org/abs/1910.04920

Sparse Regularized Optimal Transport with Deformed q -Entropy.

Autoren: Bao, Han Sakaue, Shinsaku

Quelle: Entropy; Nov2022, Vol. 24 Issue 11, p1634, 27p

Schlagwörter: UNCERTAINTY (Information theory), STATISTICAL mechanics, DISTRIBUTION (Probability theory), COMPUTATIONAL complexity, QUASI-Newton methods, ENTROPY, TOPOLOGICAL entropy, YANG-Baxter equation

Marginal Likelihood Estimation in Semiblind Image Deconvolution: A Stochastic Approximation Approach.

Autoren: Mbakam, Charlesquin Kemajou Pereyra, Marcelo Giovannelli, Jean-François

Quelle: SIAM Journal on Imaging Sciences; 2024, Vol. 17 Issue 2, p1206-1254, 49p