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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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Quasi Semi and Pseudo Semi (p,E)-Convexity in Non-Linear Optimization Programming
Ibn Al-Haitham Journal for Pure and Applied Sciences, 2023 The class of quasi semi -convex functions and pseudo semi -convex functions are presented in this paper by combining the class of -convex functions with the class of quasi semi -convex functions and pseudo semi -convex functions, respectively.
Revan I. Hazim, Saba N. Majeed
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Optimization Method for Wide Beam Sonar Transmit Beamforming
Sensors, 2022 Imaging and mapping sonars such as forward-looking sonars (FLS) and side-scan sonars (SSS) are sensors frequently used onboard autonomous underwater vehicles.
Louise Rixon Fuchs +2 more
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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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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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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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Quantum algorithms and lower bounds for convex optimization [PDF]
Quantum, 2020 While recent work suggests that quantum computers can speed up the solution of semidefinite programs, little is known about the quantum complexity of more general convex optimization.
Shouvanik Chakrabarti +3 more
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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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