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We study distributed optimization problems when nodes minimize the sum of their individual costs subject to a common vector variable. The costs are convex, have Lipschitz continuous gradient (with constant ), and bounded gradient. We propose two fast distributed gradient algorithms based on the centralized Nesterov gradient algorithm and establish their convergence rates in terms of the per-node...
The global convergence (GC) analysis of recurrent neural networks (RNNs) is a first and necessary step for any practical applications of them. In the present paper, when the connecting matrix of the RNNs with projection mapping owning limited norm, the GC property is assured under the critical condition. The results given here not only improve deeply upon the existing relevant critical as well as...
Inspired by the concept and principles of quantum computing, the classical quantum-inspired evolutionary algorithm (QEA) provides a useful way to find out the approximate solution of many optimization problems. However, compared with other heuristic algorithms, the slow convergence speed of QEA has been an important issue when it is applied to solve the optimization problems. As such, an improved...
In this paper, we present a novel in-domain neighborhood approach to clarify the dynamic load balancing problem on a heterogeneous network and to solve many practical problems. The distributed system consists of a network of workstations with different domains, speeds and capacities. Since the number of workstations and the diameter of the network affect the convergences rate, our approach introduces...
This study is mainly focused on iterative solutions to shifted linear systems arising from a Quantum Chromo dynamics (QCD) problem. For solving such systems efficiently, we explore a new shifted QMRCGstab (SQMRCGstab) method, which is derived by extending the quasi-minimum residual to the shifted BiCGstab. The shifted QMRCGstab method takes advantage of the shifted invariant property, so that it could...
The Rapidly Exploring Random Tree Star (RRT∗) is an extension of the Rapidly Exploring Random Tree path finding algorithm. RRT∗ guarantees an optimal, collision free path solution but is limited by slow convergence rates and inefficient memory utilization. This paper presents APGD-RRT∗, a variant of RRT∗ which utilizes Artificial Potential Fields to improve RRT∗ performance, providing relatively better...
Pointing at that Artificial Bee Colony Algorithm (ABC) has the defect of slow search speed and low precision, the article proposed an Improved Artificial Bee Colony Algorithm with Two-Eagle Strategy (ETABC) through using a kind of optimization method-Eagle Strategy, and proved the convergence of ETABC. The simulation results show that ETABC is more effective in solving optimization problems.
In this paper, we propose a new selection mode of 'r, t' for the preconditioner I+C and analyze the convergence performance of the preconditioned AOR iterative method induced by this preconditioner. For a nonsingular M-matrix, we show that the preconditioned AOR iterative method with this choice and the preconditioned methods advised by Evans et al. are all convergent, and that the preconditioned...
This research presents a framework for coordinating multiple intelligent agents within a single virtual environment. Coordination is accomplished via a "next available agent" scheme while learning is achieved through the use of the Q-learning and Sarsa temporal difference reinforcement learning algorithms. To assess the effectiveness of each learning algorithm, experiments were conducted...
We propose a gossip-based mini-batch random projection (GMRP) algorithm that can reduce communication overhead for a distributed optimization problem defined over a network with a very large number of constraints. We state a convergence result and provide an application of the GMRP, text classification with support vector machines.
Scheduling & Optimization problems are iterative in nature. To find a ideal solution to which is a complex task. These types of problems may be effectively solved and optimal solutions which may be close to the ideal solution may be derived with the help of evolutionary algorithms like the Genetic Algorithm. This paper introduces a new variant of genetic algorithm called Modified Enhanced Steady...
We investigate the mean-squared error (MSE) performance of the Kiefer-Wolfowitz (KW) stochastic approximation (SA) algorithm and two of its variants, namely the scaled-and-shifted KW (SSKW) in Broadie, Cicek, and Zeevi (2011) and Kesten's rule. We conduct a sensitivity analysis of KW with various tuning sequences and initial start values and implement the algorithms for two contrasting functions....
There have been proposed various types of multiplicative updates for nonnegative matrix factorization. However, these updates have a serious drawback in common: they are not defined for all pairs of nonnegative matrices. Furthermore, due to this drawback, their global convergence in the sense of Zangwill's theorem cannot be proved theoretically. In this paper, we consider slightly modified versions...
We consider the solution of a stochastic convex optimization problem E[f(x;θ∗,ξ)] in x over a closed and convex set X in a regime where θ∗ is unavailable. Instead, θ∗ may be learnt by minimizing a suitable metric E[g(θη)] in θ over a closed and convex set Θ. We present a coupled stochastic approximation scheme for the associated stochastic optimization problem with imperfect information. The schemes...
The purpose of this research is to resolve the problems related to bio and sports science that are found in the field of education, to motivate the students to study and to develop the new converged education program that combines bio and sports science together and that enables sports science education through applied scientific thinking method. Moreover, this is intended to provide the teaching...
In this paper, we propose a smoothing QP-free infeasible method without a penalty function and a filter for inequality constrained nonlinear optimization problems. This iterative method is based on smoothing equations which are the reformulation of the KKT first-order optimality conditions, by using the multipliers and the smoothing NCP function. Comparing with other QP-free method, in each iteration,...
A distributed consensus algorithm for estimating the maximum and the minimum of the initial measurements in a sensor network is proposed. Estimating extrema is useful in many applications such as temperature control. In the absence of communication noise, max estimation can be done by updating the state value with the largest received measurements in every iteration at each sensor. In the presence...
There is a considerable literature devoted to the field of convergence of fuzzy sequence. In this paper, a new convergence is given and the relationships between the convergence and others are proved.
In this paper, we proposed a global convergence of an improved filter method of feasible direction for solving inequality constrained optimization. At every trial point, it is only necessary to solve one QP sub problem with equality constraints. On contrary the traditional SQP algorithm, we use filter method with the new SQP algorithm, which avoid Maratos effect. Under some reasonable conditions,...
Since high-speed and high-resolution ADCs are needed in communication systems, single ADC can hardly achieve. This paper proposed a timing mismatch estimation and calibration algorithm in Time-Interleaved ADC (TIADC). A multistage differentiator-multiplier cascade (DMC) structure is disclosed for timing mismatch correction. As the present estimation and calibration method, the procedure can be performed...
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