Finite convergence into a convex polytope via facet reflections

Dinesh B. Ekanayake; Douglas J. LaFountain; Boris Petracovici

Displaying similar documents to “Finite convergence into a convex polytope via facet reflections”

Distributed accelerated Nash equilibrium learning for two-subnetwork zero-sum game with bilinear coupling

Xianlin Zeng, Lihua Dou, Jinqiang Cui (2023)

Kybernetika

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This paper proposes a distributed accelerated first-order continuous-time algorithm for $O (1 / t^{2})$ convergence to Nash equilibria in a class of two-subnetwork zero-sum games with bilinear couplings. First-order methods, which only use subgradients of functions, are frequently used in distributed/parallel algorithms for solving large-scale and big-data problems due to their simple structures. However, in the worst cases, first-order methods for two-subnetwork zero-sum games often have an asymptotic...

Safe consensus control of cooperative-competitive multi-agent systems via differential privacy

Jiayue Ma, Jiangping Hu (2022)

Kybernetika

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This paper investigates a safe consensus problem for cooperative-competitive multi-agent systems using a differential privacy (DP) approach. Considering that the agents simultaneously interact cooperatively and competitively, we propose a novel DP bipartite consensus algorithm, which guarantees that the DP strategy only works on competitive pairs of agents. We then prove that the proposed algorithm can achieve the mean square bipartite consensus and $(p, r)$ -accuracy. Furthermore, a differential...

New hybrid conjugate gradient method for nonlinear optimization with application to image restoration problems

Youcef Elhamam Hemici, Samia Khelladi, Djamel Benterki (2024)

Kybernetika

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The conjugate gradient method is one of the most effective algorithm for unconstrained nonlinear optimization problems. This is due to the fact that it does not need a lot of storage memory and its simple structure properties, which motivate us to propose a new hybrid conjugate gradient method through a convex combination of $β_{k}^{R M I L}$ and $β_{k}^{H S}$ . We compute the convex parameter $θ_{k}$ using the Newton direction. Global convergence is established through the strong Wolfe conditions. Numerical experiments...

On the weighted Euclidean matching problem in $ℝ^{d}$

Birgit Anthes, Ludger Rüschendorf (2001)

Applicationes Mathematicae

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A partitioning algorithm for the Euclidean matching problem in $ℝ^{d}$ is introduced and analyzed in a probabilistic model. The algorithm uses elements from the fixed dissection algorithm of Karp and Steele (1985) and the Zig-Zag algorithm of Halton and Terada (1982) for the traveling salesman problem. The algorithm runs in expected time $n {(l o g n)}^{p - 1}$ and approximates the optimal matching in the probabilistic sense.

A viscosity-proximal gradient method with inertial extrapolation for solving certain minimization problems in Hilbert space

L.O. Jolaoso, H.A. Abass, O.T. Mewomo (2019)

Archivum Mathematicum

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In this paper, we study the strong convergence of the proximal gradient algorithm with inertial extrapolation term for solving classical minimization problem and finding the fixed points of $δ$ -demimetric mapping in a real Hilbert space. Our algorithm is inspired by the inertial proximal point algorithm and the viscosity approximation method of Moudafi. A strong convergence result is achieved in our result without necessarily imposing the summation condition $\sum_{n = 1}^{\infty} β_{n} ∥ x_{n - 1} - x_{n} ∥ < + \infty$ on the inertial term. Finally,...

Equilibrium analysis of distributed aggregative game with misinformation

Meng Yuan, Zhaoyang Cheng, Te Ma (2024)

Kybernetika

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This paper considers a distributed aggregative game problem for a group of players with misinformation, where each player has a different perception of the game. Player’s deception behavior is inevitable in this situation for reducing its own cost. We utilize hypergame to model the above problems and adopt $ϵ$ -Nash equilibrium for hypergame to investigate whether players believe in their own cognition. Additionally, we propose a distributed deceptive algorithm for a player implementing...

An interior-point algorithm for semidefinite least-squares problems

Chafia Daili, Mohamed Achache (2022)

Applications of Mathematics

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We propose a feasible primal-dual path-following interior-point algorithm for semidefinite least squares problems (SDLS). At each iteration, the algorithm uses only full Nesterov-Todd steps with the advantage that no line search is required. Under new appropriate choices of the parameter $β$ which defines the size of the neighborhood of the central-path and of the parameter $θ$ which determines the rate of decrease of the barrier parameter, we show that the proposed algorithm is well defined...

A stochastic mirror-descent algorithm for solving $A X B = C$ over an multi-agent system

Yinghui Wang, Songsong Cheng (2021)

Kybernetika

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In this paper, we consider a distributed stochastic computation of $A X B = C$ with local set constraints over an multi-agent system, where each agent over the network only knows a few rows or columns of matrixes. Through formulating an equivalent distributed optimization problem for seeking least-squares solutions of $A X B = C$ , we propose a distributed stochastic mirror-descent algorithm for solving the equivalent distributed problem. Then, we provide the sublinear convergence of the proposed algorithm....

A descent algorithm for $ε$ - approximation of continuous functions with values in unitary space

M. Janc (1982)

Matematički Vesnik

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Constrained $𝐤$ -means algorithm for resource allocation in mobile cloudlets

Rasim M. Alguliyev, Ramiz M. Aliguliyev, Rashid G. Alakbarov (2023)

Kybernetika

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With the rapid increase in the number of mobile devices connected to the Internet in recent years, the network load is increasing. As a result, there are significant delays in the delivery of cloud resources to mobile users. Edge computing technologies (edge, cloudlet, fog computing, etc.) have been widely used in recent years to eliminate network delays. This problem can be solved by allocating cloud resources to the cloudlets that are close to users. The article proposes a clustering-based...

A New overlapping community detection algorithm based on similarity of neighbors in complex networks

Pelin Çetin, Sahin Emrah Amrahov (2022)

Kybernetika

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Community detection algorithms help us improve the management of complex networks and provide a clean sight of them. We can encounter complex networks in various fields such as social media, bioinformatics, recommendation systems, and search engines. As the definition of the community changes based on the problem considered, there is no algorithm that works universally for all kinds of data and network structures. Communities can be disjointed such that each member is in at most one...

An Algorithm for $L C^{1}$ Optimization

Nada I. Đuranović-Miličić (2005)

The Yugoslav Journal of Operations Research

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Distributed dual averaging algorithm for multi-agent optimization with coupled constraints

Zhipeng Tu, Shu Liang (2024)

Kybernetika

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This paper investigates a distributed algorithm for the multi-agent constrained optimization problem, which is to minimize a global objective function formed by a sum of local convex (possibly nonsmooth) functions under both coupled inequality and affine equality constraints. By introducing auxiliary variables, we decouple the constraints and transform the multi-agent optimization problem into a variational inequality problem with a set-valued monotone mapping. We propose a distributed...

The adaptation of the $k$ -means algorithm to solving the multiple ellipses detection problem by using an initial approximation obtained by the DIRECT global optimization algorithm

Rudolf Scitovski, Kristian Sabo (2019)

Applications of Mathematics

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We consider the multiple ellipses detection problem on the basis of a data points set coming from a number of ellipses in the plane not known in advance, whereby an ellipse $E$ is viewed as a Mahalanobis circle with center $S$ , radius $r$ , and some positive definite matrix $Σ$ . A very efficient method for solving this problem is proposed. The method uses a modification of the $k$ -means algorithm for Mahalanobis-circle centers. The initial approximation consists of the set of circles whose centers...

A penalty ADMM with quantized communication for distributed optimization over multi-agent systems

Chenyang Liu, Xiaohua Dou, Yuan Fan, Songsong Cheng (2023)

Kybernetika

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In this paper, we design a distributed penalty ADMM algorithm with quantized communication to solve distributed convex optimization problems over multi-agent systems. Firstly, we introduce a quantization scheme that reduces the bandwidth limitation of multi-agent systems without requiring an encoder or decoder, unlike existing quantized algorithms. This scheme also minimizes the computation burden. Moreover, with the aid of the quantization design, we propose a quantized penalty ADMM...

On the power of randomization for job shop scheduling with $k$ -units length tasks

Tobias Mömke (2009)

RAIRO - Theoretical Informatics and Applications - Informatique Théorique et Applications

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In the job shop scheduling problem $k$ -units- $J_{m}$ , there are $m$ machines and each machine has an integer processing time of at most $k$ time units. Each job consists of a permutation of $m$ tasks corresponding to all machines and thus all jobs have an identical dilation $D$ . The contribution of this paper are the following results; (i) for $d = o (\sqrt{D})$ jobs and every fixed $k$ , the makespan of an optimal schedule is at most $D + o (D)$ , which extends the result of [3] for $k = 1$ ; (ii) a randomized on-line approximation algorithm...