Results 11 to 20 of about 4,731,628 (237)
IEEE Transactions on Automatic Control, 2004 Stephen Boyd, Lieven Vandenberghe
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Non-convex Optimization for Machine Learning [PDF]
Found. Trends Mach. Learn., 2017 A vast majority of machine learning algorithms train their models and perform inference by solving optimization problems. In order to capture the learning and prediction problems accurately, structural constraints such as sparsity or low rank are ...
Jain, Prateek, Kar, Purushottam
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Implementable tensor methods in unconstrained convex optimization. [PDF]
Math Program, 2021 In this paper we develop new tensor methods for unconstrained convex optimization, which solve at each iteration an auxiliary problem of minimizing convex multivariate polynomial.
Nesterov Y.
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Convex and Non-Convex Optimization under Generalized Smoothness [PDF]
Neural Information Processing Systems, 2023 Classical analysis of convex and non-convex optimization methods often requires the Lipshitzness of the gradient, which limits the analysis to functions bounded by quadratics.
Haochuan Li +4 more
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Motion planning around obstacles with convex optimization [PDF]
Science Robotics, 2022 From quadrotors delivering packages in urban areas to robot arms moving in confined warehouses, motion planning around obstacles is a core challenge in modern robotics.
Tobia Marcucci +3 more
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Computer Vision, 2010 This textbook is based on lectures given by the authors at MIPT (Moscow), HSE (Moscow), FEFU (Vladivostok), V.I. Vernadsky KFU (Simferopol), ASU (Republic of Adygea), and the University of Grenoble-Alpes (Grenoble, France).
Stephen P. Boyd, L. Vandenberghe
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Private stochastic convex optimization: optimal rates in linear time [PDF]
Symposium on the Theory of Computing, 2020 We study differentially private (DP) algorithms for stochastic convex optimization: the problem of minimizing the population loss given i.i.d. samples from a distribution over convex loss functions. A recent work of Bassily et al.
V. Feldman, Tomer Koren, Kunal Talwar
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Exact Matrix Completion via Convex Optimization [PDF]
Foundations of Computational Mathematics, 2008 We consider a problem of considerable practical interest: the recovery of a data matrix from a sampling of its entries. Suppose that we observe m entries selected uniformly at random from a matrix M.
E. Candès, B. Recht
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Meta-Learning With Differentiable Convex Optimization [PDF]
Computer Vision and Pattern Recognition, 2019 Many meta-learning approaches for few-shot learning rely on simple base learners such as nearest-neighbor classifiers. However, even in the few-shot regime, discriminatively trained linear predictors can offer better generalization.
Kwonjoon Lee +3 more
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An Improved Convergence Analysis for Decentralized Online Stochastic Non-Convex Optimization [PDF]
IEEE Transactions on Signal Processing, 2020 In this paper, we study decentralized online stochastic non-convex optimization over a network of nodes. Integrating a technique called gradient tracking in decentralized stochastic gradient descent, we show that the resulting algorithm, GT-DSGD, enjoys ...
Ran Xin, U. Khan, S. Kar
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