gradient algorithm
Gradient Algorithm in Subspace Predictive Control
... classical gradient algorithm is put forth to solve the primal dual optimization ...fast gradient method is not suited for this complex optimization problem, as one regularization term is added in our ...
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The Sliding Gradient Algorithm for Linear Programming
... sliding algorithm is how to calculate the projection of the gravity vector g onto the intersection of a group of facets, which is disclosed in the same paper ...the gradient projections on complementary ...
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A gradient algorithm for optimal control problems with model reality differences
... The rest of the paper is organized as follows. In Section 2, a general class of optimal control problem is described. In Section 3, a simplified model-based optimal control problem is discussed, where the adjusted ...
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Performance Of Scaled Conjugate Gradient Algorithm In Face Recognition
... backpropagationn algorithm was probably the main reason behind the repopularisation of neural networks after the publication of "Learning Internal Representations by Error Propagation" in 1986 (Though ...
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DSA: Decentralized Double Stochastic Averaging Gradient Algorithm
... averaging gradient (DSA) algorithm is proposed as a solution alternative that relies on: (i) The use of local stochastic averaging ...averaging gradient, linear convergence, large-scale optimization, ...
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Asynchronous Proximal Stochastic Gradient Algorithm for Composition Optimization Problems
... stochastic gradient descent (SGD) algo- rithm and its variants either have low convergence rate or are computationally ...composition gradient algorithms have been proposed, how- ever, these methods are ...
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A Penalization Gradient Algorithm for Variational Inequalities
... In 2, Attouch et al., based on seminal work by Passty 3, solve this problem with a multivalued operator by using splitting proximal methods. A drawback is the fact that the convergence in general is only ergodic. ...
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The Effect of Adaptive Gain and Adaptive Momentum in Improving Training Time of Gradient Descent Back Propagation Algorithm on Classification Problems
... propagation algorithm based on extrapolation of each individual interconnection ...propagation algorithm is individually ...conjugate gradient algorithm could be used to train multilayer feed ...
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The effect of adaptive gain and adaptive momentum in improving training time of Gradient Descent back propagation algorithm on classification problems
... propagation algorithm based on extrapolation of each individual interconnection ...propagation algorithm is individually ...conjugate gradient algorithm could be used to train multilayer feed ...
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A Lagrangian Relaxation-based Algorithm to Solve a Home Health Care Routing Problem
... relaxation-based algorithm as a type of sub-gradient algorithm to solve the combinatorial optimization problems ...the algorithm aims to fill the gap between them and finds a solution which ...
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A STUDY ON CERTAIN NEW NUMERICAL ALGORITHMS FOR MINIMIZATION OF NON LINEAR FUNCTIONS
... conjugate gradient methods [8], a scaled nonlinear conjugate gradient algorithm[2], a method called, ABS-MPVT algorithm [10] are used for solving unconstrained optimization ...new ...
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Alternate Iterative Algorithms for Minimization of Non-linear Functions
... conjugate gradient methods [3], a scaled nonlinear conjugate gradient algorithm[1], a method called, ABS-MPVT algorithm [12] are used for solving unconstrained optimization ...preconditioned ...
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Research of X ray image fast de noising method of power equipment based on GFNL algorithm
... GFNL algorithm extracts the noise model, the noise reduction processing scanned images, while reducing image noise has a strong ability to maintain spatial resolution, while the smoothing smoother eliminate system ...
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Using Aggregation for Adaptive Super Peer Discovery on the Gradient Topology
... Abstract. Peer-to-peer environments exhibit a very high diversity in in- dividual peer characteristics ranging by orders of magnitude in terms of uptime, available bandwidth, and storage space. Many systems attempt to ...
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Computational estimation of scene structure through texture gradient cues
... To this point, we have been treating gradients as involving a frequency variation. There is a subtlety here, however, since this logic depends on the form of perspective involved. In parallel (isometric) perspective ...
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Predictive Data Mining with Normalized Adaptive Training Method for Neural Networks
... ADAPTIVE GRADIENT BASED TRAINING ALGORITHM In gradient descent algorithm with error back propagation, new weight matrix is calculated from the previous weight matrix according to ...adaptive ...
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Detection of Mobile Keyloggers Using Deep Learning
... saved in a csv file format. The dataset is in binary format. If the APK file consists of that particular feature then it is given the value 1, otherwise 0.The type also is obtained in the dataset, if the apk file is a ...
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Accelerated Mann and CQ algorithms for finding a fixed point of a nonexpansive mapping
... [, ], and the CQ method [] are useful fixed point algorithms to solve the fixed point problems. Meanwhile, to guarantee practical systems and networks (see, e.g., [–]) are stable and reliable, the fixed point has to ...
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Comparative Study of Unconstrained Mechanical Optimization Methods Based on Two variable Rosenbrock Function
... Abstract. The unconstrained optimization method (UOM) plays an important role in the field of physics, mathematics, statistics, etc. Rosenbrock function is a 4-degree function with a curved canyon. It is most suitable ...
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Back to optimality: a formal framework to express the dynamics of learning optimal behavior
... Following David Marr’s Tri-Level Hypothesis (Marr & Poggio, 1976), our proposal addresses the question of how does the system do what it does, rather than what does the system do. The former refers to the algorithmic ...
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