Suchergebnisse - regularized stochastic BFGS algorithm

RES: Regularized Stochastic BFGS Algorithm von Mokhtari, Aryan, Ribeiro, Alejandro

“… RES, a regularized stochastic version of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton method, is proposed to solve strongly convex optimization problems with stochastic objectives …”
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Regularized stochastic BFGS algorithm von Mokhtari, Aryan, Ribeiro, Alejandro

“… A regularized stochastic version of the Broyden-Fletcher- Goldfarb-Shanno (BFGS) quasi-Newton method is proposed to solve optimization problems with stochastic objectives that arise in large scale machine learning …”
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An Improved Technique for Pneumonia Infected Patients Image Recognition Based on Combination Algorithm of Smooth Generalized Pinball SVM and Variational Autoencoders von Ratiphaphongthon, Wachiraphong, Panup, Wanida, Wangkeeree, Rabian

“… More specifically, using the proposed smoothing functions, we employ the smoothing stochastic quasi-Newton method to solve the primal problem …”
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A new technique for Pneumonia infected patients image recognition based on combination algorithm of smooth generalized pinball SVM and VAEs von Ratiphaphongthon, Wachiraphong, Panup, Wanida, Wangkeeree, Rabian

“… Pneumonia is the most common disease caused by various microbial species such as bacteria, viruses, and fungi that inflamed the air sacs in one or both lungs …”
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RES: Regularized Stochastic BFGS Algorithm von Mokhtari, Aryan, Ribeiro, Alejandro

“… RES, a regularized stochastic version of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton method is proposed to solve convex optimization problems with stochastic objectives …”
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An Iterative L1 Regularized Limited Memory Stochastic BFGS Algorithm and Numerical Experiments for Big Data Applications von Patel, Vandan

“… Such models are often formulated as large scale stochastic optimization problems. When the probability distribution of the data is unknown, the Sample Average Approximation (SAA …”
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A Stochastic Quasi-Newton Method for Large-Scale Nonconvex Optimization With Applications von Chen, Huiming, Wu, Ho-Chun, Chan, Shing-Chow, Lam, Wong-Hing

“… ) method are significant in solving nonconvex problems. This article proposes a novel stochastic version of a damped and regularized BFGS method for addressing the above problems …”
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Variance-Reduced Stochastic Quasi-Newton Methods for Decentralized Learning von Zhang, Jiaojiao, Liu, Huikang, So, Anthony Man-Cho, Ling, Qing

“… In this work, we investigate stochastic quasi-Newton methods for minimizing a finite sum of cost functions over a decentralized network …”
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The regularized stochastic Nesterov’s accelerated Quasi-Newton method with applications von Makmuang, Dawrawee, Suppalap, Siwakon, Wangkeeree, Rabian

“… The stochastic Broyden–Fletcher–Goldfarb–Shanno (BFGS) method has effectively solved strongly convex optimization problems …”
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On stochastic and deterministic quasi-Newton methods for non-Strongly convex optimization: Asymptotic convergence and rate analysis von Yousefian, Farzad, Nedić, Angelia, Shanbhag, Uday

“… Motivated by applications arising from large scale optimization and machine learning, we consider stochastic quasi-Newton (SQN …”
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A Robust Stochastic Quasi-Newton Method with the Application in Machine Learning von Wu, Lin, Shi, Tuo, Wang, Yongbin, Chen, Huiming, Lam, Wong-Hing

“… We study a novel stochastic version of damped and regularized limited memory Broyden-Fletcher-Goldfarb-Shanno (Sd-REG-LBFGS …”
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Distributed Sparse Class-Imbalance Learning and Its Applications von Maurya, Chandresh Kumar, Toshniwal, Durga, Venkoparao, Gopalan Vijendran

“… In the present work, the study on class imbalance problems in a distributed setting exploiting sparsity structure in the data has been carried out. We …”
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Variance-Reduced First-Order Methods for Convex Stochastic Optimization and Monotone Stochastic Variational Inequality Problems von Jalilzadeh, Afrooz

“… A popular Monte-Carlo sampling approach to resolve this issue is stochastic approximation (SA …”
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Variance-Reduced Stochastic Quasi-Newton Methods for Decentralized Learning: Part II von Zhang, Jiaojiao, Liu, Huikang, Anthony Man-Cho So, Ling, Qing

“… Hessian inverse approximations have bounded positive eigenvalues. In Part II, we specify two fully decentralized stochastic quasi-Newton methods, damped regularized limited-memory DFP …”
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Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation von Si Yi Meng, Vaswani, Sharan, Laradji, Issam, Schmidt, Mark, Lacoste-Julien, Simon

“… Furthermore, we analyze stochastic BFGS algorithms in the interpolation setting and prove their global …”
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Efficient Second-order Algorithms for Decentralized Optimization with Provable Convergence Guarantees von Zhang, Jiaojiao

“… However, there are few second-order decentralized algorithms available, and those that …”
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