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Support vector machines (SVMs) are invaluable tools for many practical applications in artificial intelligence, e.g., classification and event recognition. However, popular SVM solvers are not sufficiently efficient for applications with a great deal of samples as well as a large number of features. In this paper, thus, we present NESVM, a fast gradient SVM solver that can optimize various SVM models,...
Protein folding problem is one of the central problems in the cross-discipline field involving biology, computational physics and computer science. In this paper, based on the heuristic physical model, a three-dimensional AB mode-based protein folding problem is converted from a nonlinear constraint-satisfied problem to an unconstrained optimization problem, which can be solved by the gradient method...
Fast convergence-rate, low computation complexity and good stability are important goals in the researching area of neural network learning algorithm. A kind of parallel computing lagged-start hybrid optimization algorithm is studied, it not only integrates the basic gradient method and the unconstrained optimization algorithm to realize the supplement of their advantages, but also makes full use...
This paper aims to minimize the total active losses in electrical distribution systems by means of optimal capacitor bank placement. The proposed methodology to solve this optimization problem is the ant colony optimization (ACO) metaheuristic. The gradient method is combined with the metaheuristic in order to accelerate the convergence of the ACO algorithm. The methodology has been applied successfully...
Extreme eigenpairs computation is of considerable interest in signal processing and estimation. Thus problem of simultaneous computation of the smallest and largest eigenvalues and the corresponding eigenvectors of a symmetric matrix is considered. The proposed methods are derived from optimizing cost functions which are chosen to have optimal values at vectors that are linear combinations of extreme...
We describe in this paper a reliable, fast and accurate design tool for the analysis, optimisation and synthesis of axis-symmetrical antennas of arbitrary shape and constitutive materials. It is based on the combination of a FDTD kernel expressed in cylindrical coordinates with gradient methods, and binary- and real-coded genetic algorithms. To validate the numerical algorithms, several compact light-weight...
The paper deals with the design and implementation of a stereo algorithm. Disparity map is formulated as a Markov Random Field with a new smoothness constraint depending not only on image derivatives, but also on segmentation results and gradient directions. With these constraints we force disparity continuity inside each segmented object, while its contours are well preserved. Moreover we have designed...
An algorithm optimizing total energy consumption of multiple inverter train operation considering DC feeding circuit is investigated in this paper. The proposed mathematical formulation can deal with several characteristics of trains, especially the effect of regenerative braking system. The developed optimization algorithm based on the gradient method is applicable to solve the formulated problems...
A gradient method is used to obtain the signal constellation that yields to the minimum error probability over a channel affected by non-linear amplification and additive noise. The results show the dominance of phase shift keying when the amplifier is driven near saturation, and the better performance given by amplitude-phase shift keying if the amplifier is operated in its linear region.
Iterative algorithms such as the Newton method or the steepest gradient method appear in real-time software to solve online optimization problems as part of autonomous decision making algorithms. Proving safety and in particular convergence properties of such methods in their generic form is hopeless. However, for a special class of problems over a limited range of inputs and uncertain parameters,...
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