Search results for "optimization"
showing 10 items of 2824 documents
Noncoincidence of Approximate and Limiting Subdifferentials of Integral Functionals
2011
For a locally Lipschitz integral functional $I_f$ on $L^1(T,\mathbf{R}^n)$ associated with a measurable integrand f, the limiting subdifferential and the approximate subdifferential never coincide at a point $x_0$ where $f(t,\cdot)$ is not subdifferentially regular at $x_0(t)$ for a.e. $t\in T$. The coincidence of both subdifferentials occurs on a dense set of $L^1(T,\mathbf{R}^n)$ if and only if $f(t,\cdot)$ is convex for a.e. $t\in T$. Our results allow us to characterize Aubin's Lipschitz-like property as well as the convexity of multivalued mappings between $L^1$-spaces. New necessary optimality conditions for some Bolza problems are also obtained.
Limited memory bundle algorithm for inequality constrained nondifferentiable optimization
2007
Many practical optimization problems involve nonsmooth (that is, not necessarily differentiable) functions of hundreds or thousands of variables with various constraints. In this paper, we describe a new efficient adaptive limited memory interior point bundle method for large, possible nonconvex, nonsmooth inequality constrained optimization. The method is a hybrid of the nonsmooth variable metric bundle method and the smooth limited memory variable metric method, and the constraint handling is based on the primal-dual feasible direction interior point approach. The preliminary numerical experiments to be presented confirm the effectiveness of the method.
Test problems for large-scale nonsmooth minimization
2007
Many practical optimization problems involve nonsmooth (that is, not necessarily differentiable) functions of hundreds or thousands of variables with various constraints. However, there exist only few large-scale academic test problems for nonsmooth case and there is no established practice for testing solvers for large-scale nonsmooth optimization. For this reason, we now collect the nonsmooth test problems used in our previous numerical experiments and also give some new problems. Namely, we give problems for unconstrained, bound constrained, and inequality constrained nonsmooth minimization.
Randomized heuristics for the Capacitated Clustering Problem
2017
In this paper, we investigate the adaptation of the Greedy Randomized Adaptive Search Procedure (GRASP) and Iterated Greedy methodologies to the Capacitated Clustering Problem (CCP). In particular, we focus on the effect of the balance between randomization and greediness on the performance of these multi-start heuristic search methods when solving this NP-hard problem. The former is a memory-less approach that constructs independent solutions, while the latter is a memory-based method that constructs linked solutions, obtained by partially rebuilding previous ones. Both are based on the combination of greediness and randomization in the constructive process, and coupled with a subsequent l…
A matheuristic for the Team Orienteering Arc Routing Problem
2015
In the Team OrienteeringArc Routing Problem (TOARP) the potential customers are located on the arcs of a directed graph and are to be chosen on the basis of an associated profit. A limited fleet of vehicles is available to serve the chosen customers. Each vehicle has to satisfy a maximum route duration constraint. The goal is to maximize the profit of the served customers. We propose a matheuristic for the TOARP and test it on a set of benchmark instances for which the optimal solution or an upper bound is known. The matheuristic finds the optimal solutions on all, except one, instances of one of the four classes of tested instances (with up to 27 vertices and 296 arcs). The average error o…
High resolution in currents reconstruction applying the extrapolation matrix and spectrum replies
2007
A faster method for the reconstruction of currents has been proposed. For this a new algorithm has been used which extrapolates a 2D signal in less time than the iterative method of Papoulis. Results exposed in this paper show the likeness of the reconstructed currents with the new algorithm with those of the iterative method and the improvement that might be obtained in these new currents with regard to the iterative one. Furthermore, results show the higher speed of the new matrix method.
What is the Best Method of Matrix Adjustment? A Formal Answer by a Return to the World of Vectors
2003
The principle of matrix adjustment methods consists into finding what is the matrix which is the closest to an initial matrix but with respect of the column and row sum totals of a second matrix. In order to help deciding which matrix-adjustment method is the better, the article returns to the simpler problem of vector adjustment then back to matrices. The information-lost minimization (biproportional methods and RAS) leads to a multiplicative form and generalize the linear model. On the other hand, the distance minimization which leads to an additive form tends to distort the data by giving a result asymptotically independent to the initial matrix. The result allows concluding non-ambiguou…
A Novel Artificial Neural Network (ANN) Using The Mayfly Algorithm for Classification
2021
Training of Artificial Neural Networks (ANNs) have been improved over the years using meta heuristic algorithms that introduce randomness into the training method but they might be prone to falling into a local minima in a high-dimensional space and have low convergence rate with the iterative process. To cater for the inefficiencies of training such an ANN, a novel neural network is presented in this paper using the bio-inspired algorithm of the movement and mating of the mayflies. The proposed Mayfly algorithm is explored as a means to update weights and biases of the neural network. As compared to previous meta heuristic algorithms, the proposed approach finds the global minima cost at f…
Simulated Annealing in Bayesian Decision Theory
1992
Since the seminal paper by Kirkpatrick, Gelatt and Vechhi (1983), a number of papers in the scientific literature refer to simulated annealing as a powerful random optimization method which promises to deliver, within reasonable computing times, optimal or nearly optimal solutions to complex decision problems hitherto forbidding. The algorithm, which uses the physical process of annealing as a metaphor, is special in that, at each iteration, one may move with positive probability to solutions with higher values of the function to minimize, rather than directly jumping to the point with the smallest value within the neighborhood, thus drastically reducing the chances of getting trapped in lo…
Experimental Study of the Laser Cutting Process on 1C45 - 3 mm Steel
2014
The Paper Presents the Measurement Systems Analysis, System Capability and Optimization of a Laser Cutting System for 3 Mm Alloy Steel. for the Measurement System Analysis and System Capability the Data was Introduced in a Excel Sheet which was Designed According to QS9000 Standard (Measurement System Analysis) to Calculate the Measuring System Repeatability, Reproducibility and Capability. Design of Experiment (DOE) was Used for Process Optimization the Optimization was Conducted in an Operational Manufacturing Environment and was Based on the Design of a 25 Full Factorial Experiment. the Laser Parameters (input) were Feed Rate v, Gas Pressure p, Power P, Frequency F and Efficiency R while…