Results 21 to 30 of about 9,520,135 (306)
Localization and Approximations for Distributed Non-convex Optimization
Journal of Optimization Theory and Applications, 2023 Distributed optimization has many applications, in communication networks, sensor networks, signal processing, machine learning, and artificial intelligence. Methods for distributed convex optimization are widely investigated, while those for non-convex objectives are not well understood.
Hsu Kao, Vijay G. Subramanian
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Non-convex mixed-integer nonlinear programming : a survey [PDF]
, 2012 A wide range of problems arising in practical applications can be formulated as Mixed-Integer Nonlinear Programs (MINLPs). For the case in which the objective and constraint functions are convex, some quite effective exact and heuristic algorithms are ...
Burer, S, Letchford, Adam
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Adaptive Strategies in Non-convex Optimization
CoRR, 2023 An algorithm is said to be adaptive to a certain parameter (of the problem) if it does not need a priori knowledge of such a parameter but performs competitively to those that know it. This dissertation presents our work on adaptive algorithms in following scenarios: 1.
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Bandwidth Maximization of Disturbance Observer Based on Experimental Frequency Response Data
SICE Journal of Control, Measurement, and System Integration, 2020 A disturbance observer (DOB) has been widely employed in industrial field due to its simplicity and effectiveness in disturbance rejection. This paper focuses on systematic bandwidth-maximized DOB design by frequency response data-based convex ...
Xiaoke Wang +2 more
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On Mixed-Integer Random Convex Programs [PDF]
, 2012 We consider a class of mixed-integer optimization problems subject to N randomly drawn convex constraints. We provide explicit bounds on the tails of the probability that the optimal solution found under these N constraints will become infeasible for the
D. Lyons +8 more
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Online non-convex optimization with imperfect feedback
CoRR, 2020 We consider the problem of online learning with non-convex losses. In terms of feedback, we assume that the learner observes - or otherwise constructs - an inexact model for the loss function encountered at each stage, and we propose a mixed-strategy learning policy based on dual averaging.
Héliou, Amélie +3 more
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Applied Sciences, 2016 Convex 1-D first-order total variation (TV) denoising is an effective method for eliminating signal noise, which can be defined as convex optimization consisting of a quadratic data fidelity term and a non-convex regularization term.
Cancan Yi, Yong Lv, Zhang Dang, Han Xiao
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ON OPTIMUM DESIGN OF FRAME STRUCTURES
Acta Polytechnica CTU Proceedings, 2020 Optimization of frame structures is formulated as a non-convex optimization problem, which is currently solved to local optimality. In this contribution, we investigate four optimization approaches: (i) general non-linear optimization, (ii) optimality ...
Marek Tyburec +3 more
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Optimality and Stability in Non-Convex Smooth Games
J. Mach. Learn. Res., 2020 Convergence to a saddle point for convex-concave functions has been studied for decades, while recent years has seen a surge of interest in non-convex (zero-sum) smooth games, motivated by their recent wide applications. It remains an intriguing research challenge how local optimal points are defined and which algorithm can converge to such points.
Guojun Zhang +2 more
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Communication and Computation Cooperation in Wireless Network for Mobile Edge Computing
IEEE Access, 2019 The advantages of mobile edge computing (MEC) in lower energy consumption, improved bandwidth and reduced delay have attracted extensive studies. We consider a multiple smart wearable devices (SWDs) single smart mobile device (SMD) MEC system where the ...
Yang Li +4 more
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