An optimality criteria (OC)-based algorithm for optimization of a general class of nonlinear programming (NLP) problems is presented. The algorithm is only applicable to problems where the objective and constraint functions satisfy certain monotonicity properties. For multiply constrained problems which ...
Starting from the technical results, we obtain the global convergence of: (i) the variable metric proximal methods presented by Bonnans, Gilbert, Lemarechal, and Sagastizabal, (ii) some algorithms proposed by Correa and Lemarechal, and (iii) the proximal point algorithm given by Rockafellar. ...
Among numerous methods, the elimination method is time-consuming and complex, but it has the highest precision and can get all the solutions under the same terminal posture. This paper optimized the elimination method to improve the solving speed and make the algorithm more complete. On the ...
Two optimization methods, in our case the differential evolution (DE) algorithm and the Nelder-Mead simplex method, are used for the reconstruction at low frequencies. The Nelder-Mead simplex method is then used to obtain the solutions at higher frequencies, where the initial guess is obtained ...
The aim is to draw general guidelines on the choice of the most suitable techniques for a given optimization process. It is stressed out that an optimization process should not be a one-shot application of a certain algorithm. It must generally be composed of different steps, if not all, ...
Gumbel-softmax optimization method builds a mixed algorithm that combines the batched version of GSO algorithm and evolutionary computation methods. The key idea is to treat the batched optimization variables—the parameters as a population, such that the evolutionary operators, e.g., substitution, ...
Heuristics Hyperheuristic Adaptive metaheuristic Deep reinforcement learning Combinatorial optimization 1. Introduction A metaheuristic is an algorithmic framework that offers a coherent set of guidelines for the design of heuristic optimization methods. Classical frameworks such as Genetic Algorithm (GA), Parti...
For this special case both packages implement models using essentially the same penalized likelihoods used by the new method, but they optimize localized marginal likelihood scores within the penalized likelihood optimization algorithm to estimate the smoothing parameters. The comparison was performed using...
Moreover, as long as authors know the proposed algorithm is a new optimization technique and has not been tested on this kind of problems before. In summary, the main novelties and contribution of this work are: The rest of this work is organized as follows: in Section 2, the energy hub...
nonzero residuals/ B0260 Optimisation techniques B0290Z Other numerical methods C1180 Optimisation techniques C4190 Other numerical methodsAn algorithm for solving the general nonlinear least-square problem is developed. An estimate for the Hessian matrix is constructed as the sum of two matrices. The...