Results 31 to 40 of about 619,204 (327)
Communication complexity of convex optimization [PDF]
1986 25th IEEE Conference on Decision and Control, 1986 AbstractWe consider a situation where each of two processors has access to a different convex function φi, i = 1, 2, defined on a common bounded domain. The processors are to exchange a number of binary messages, according to some protocol, until they find a point in the domain at which φ1 + φ2 is minimized, within some prespecified accuracy ε.
Zhi-Quan Luo, John N. Tsitsiklis
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ROBOTIC MOTION PLANNING USING CONVEX OPTIMIZATION METHODS
Iraqi Journal for Computers and Informatics, 2019 Collision avoidance techniques tend to derive the robot away of the obstacles in minimal total travel distance. Most of the collision avoidance algorithms have trouble get stuck in a local minimum.
Thaker Nayl
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Some Inequalities of Generalized p-Convex Functions concerning Raina’s Fractional Integral Operators
Journal of Mathematics, 2021 Convex functions play an important role in pure and applied mathematics specially in optimization theory. In this paper, we will deal with well-known class of convex functions named as generalized p-convex functions.
Changyue Chen +2 more
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Manifold Optimization Over the Set of Doubly Stochastic Matrices: A Second-Order Geometry [PDF]
, 2018 Convex optimization is a well-established research area with applications in almost all fields. Over the decades, multiple approaches have been proposed to solve convex programs.
Douik, Ahmed, Hassibi, Babak
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Some notes on continuity in convex optimization [PDF]
arXiv, 2021 We provide proof that the optimal value function of a convex parametrized optimization problem in Euclidean spaces is itself a convex function onto the extended real line.
arxiv
Distributed H∞ Method Design and Operation using Dis- tributed Computing [PDF]
ITM Web of Conferences, 2023 Control systems have two significant components: controllers and filters. Controllers are used to control the output while filters are used to estimate the internal state of a system from a series of noisy output measurements.
Shaheer Sanaul, P.B Jayaraj
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Robust Adaptive Beamforming for General-Rank Signal Model with Positive Semi-Definite Constraint via POTDC [PDF]
, 2012 The robust adaptive beamforming (RAB) problem for general-rank signal model with an additional positive semi-definite constraint is considered. Using the principle of the worst-case performance optimization, such RAB problem leads to a difference-of ...
Khabbazibasmenj, Arash +1 more
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The Convex Geometry of Backpropagation: Neural Network Gradient Flows Converge to Extreme Points of the Dual Convex Program [PDF]
arXiv, 2021 We study non-convex subgradient flows for training two-layer ReLU neural networks from a convex geometry and duality perspective. We characterize the implicit bias of unregularized non-convex gradient flow as convex regularization of an equivalent convex model.
arxiv
Energy Informatics, 2021 Non-convex scheduling of energy production allows for more complex models that better describe the physical nature of the energy production system. Solutions to non-convex optimization problems can only be guaranteed to be local optima.
Jakob Bjørnskov +5 more
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Convex Discrete Optimization [PDF]
, 2008 We develop an algorithmic theory of convex optimization over discrete sets. Using a combination of algebraic and geometric tools we are able to provide polynomial time algorithms for solving broad classes of convex combinatorial optimization problems and convex integer programming problems in variable dimension. We discuss some of the many applications
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