Hill-climbing and gradient descent algorithms
Properties of the sign gradient descent algorithms
... sign gradient descent algorithms involving the sign of the gradient instead of the gradient itself and first introduced in the RPROP ...sign gradient descent ...
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Designing an equalizer structure using gradient descent algorithms
... 1.2 MOTIVATION The adaptive equalizers have undergone many changes in the last two decades with the introduction of ANNs and many modifications in their training algorithms [9-12]. Almost much of the research was ...
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Lab06: Hill Climbing and Simulated Annealing Algorithms Objectives
... 2. Simulated Annealing Algorithm 3. Exercise 1. Hill Climbing Algorithm We will assume we are trying to maximize a function. That is, we are trying to find a point in the search space that is better than ...
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mm Wave channel estimation with accelerated gradient descent algorithms
... We have optimized the value of this parameter also for other conditions (e.g., different number of antennas) and forthcoming results are obtained with the optimized ρ. A second relevant parameter for both AGDAR and RCS ...
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COMPARISON OF SIMPLIFIED GRADIENT DESCENT ALGORITHMS FOR DECODING LDPC CODES
... search point appears to be helpful for the search point to escape from an undesirable local maximum. Such a perturbation process can be expected to improve the BER performance of BF algorithms. One of the simplest ...
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Neural Network Training By Gradient Descent Algorithms: Application on the Solar Cell
... optimization algorithms of gradient descent (Levenberg-Marquardt, Gauss-Newton, Quasi-Newton, steepest descent and conjugate ...each gradient descent algorithm, we conducted a ...
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Robot Gaits Evolved by Combining Genetic Algorithms and Binary Hill Climbing
... The robot skeleton is made of aluminium and is provided with two identical legs. The height is 40 cm. Each leg is composed of an upper part (i.e. the thigh) connected through a cylindrical joint to the lower part (i.e. ...
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Competitive Gradient Descent
... other algorithms, while other algorihtms decrease the error more rapidly, ...other algorithms, and it is furthermore the only algorithm that does not diverge for any of the ...other algorithms, for ...
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Comparative Study of Software Module Clustering Algorithms: Hill Climbing, MCA and ECA
... There is a limitation in Hill-Climbing clustering algorithm i.e., it is not practical to use with the systems that have more than 15 modules. IV. M ULTI -O BJECTIVE S EARCH A PPROACH Here we proposed the ...
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Hill-climbing and branch-and-bound algorithms for exact and approximate inference in credal networks
... 7.2. Branch-and-bound algorithm performance Using the branch-and-bound algorithm with the hill-climbing algorithm at the initiali- zation step, we obtain the value of the different parameters in Figs. 12–14 ...
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Behaviour in Comparison with Hill Climbing
... parallel hill climbing algorithm on a typical non-convex function, the Rastrigin function and other well-known mathematic functions which we want to ...parallel hill climbing method on harder ...
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Fast bounded online gradient descent algorithms for scalable kernel-based online learning
... the algorithms in our comparison adopt the same experimental ...the algorithms and ...datasets, algorithms and budgets(More specifically, λ is chosen from { 2 T −3 2 , 2 T −2 2 , ...the ...
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Asynchronous updates for stochastic gradient descent
... µ 2L(1 + pτ 2 ) + µ(1 + τ ) (70) 4 Numerical experiments The objective of the previous section was to give a convergence certificate for SGD that also ap- plies to those problems, such as linear or logistic regression, ...
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Fully Quantized Distributed Gradient Descent
... time gradient is computed and applied, the estimate of the parameters might have changed and the gradient no longer match the current ...such algorithms to work in practice (see Dean et ...
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A step counting hill climbing algorithm
... different algorithms show similar behaviour in respect of computing time, the differences between them should be evaluated to make conclusions about their perfor- ...
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Hill Climbing Search Constraint Satisfaction
... the hill climbing algorithms either general a solution within the ...to climbing search satisfaction is not affect final states until a point both of solvingthe problem gets solveddepends on ...
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David L. Smitley and Insup Lee, "Comparative Analysis of Hill Climbing Mapping Algorithms",. November 1988.
... heuristic algorithms to this problem have been developed, their performance has been evaluated on relatively few combinations of communication and processor ...of hill climbing mapping ...
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Gradient descent localization in wireless sensor networks
... Distributed algorithms are robust against network failures, or, typically, link failures. The latter arise due to many reasons such as channel congestions, message collisions, moving nodes, or dynamic topology ...
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Decentralized Riemannian Gradient Descent on the Stiefel Manifold
... In Figure 1, we show the results of DRSGD, DRDGD and DRGTA on the data with n = 32 and ∆ = 0.8. The y-axis is the log-scale distance. The first four lines in each testing case are for the ring graph, and the last one is ...
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Localization in wireless sensor networks with gradient descent
... proposed algorithms make use of gradient descent to achieve excellent localization ...two gradient descent algorithms are iterative in nature and result is obtained when there is ...
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