Search results for "Mathematical optimization"
showing 10 items of 1300 documents
A fast 3D dual boundary element method based on hierarchical matrices
2008
AbstractIn this paper a fast solver for three-dimensional BEM and DBEM is developed. The technique is based on the use of hierarchical matrices for the representation of the collocation matrix and uses a preconditioned GMRES for the solution of the algebraic system of equations. The preconditioner is built exploiting the hierarchical arithmetic and taking full advantage of the hierarchical format. Special algorithms are developed to deal with crack problems within the context of DBEM. The structure of DBEM matrices has been efficiently exploited and it has been demonstrated that, since the cracks form only small parts of the whole structure, the use of hierarchical matrices can be particula…
On Using a Hierarchy of Twofold Resource Allocation Automata to Solve Stochastic Nonlinear Resource Allocation Problems
2007
Recent trends in AI attempt to solve difficult NP-hard problems using intelligent techniques so as to obtain approximately-optimal solutions. In this paper, we consider a family of such problems which fall under the general umbrella of "knapsack-like" problems, and demonstrate how we can solve all of them fast and accurately using a hierarchy of Learning Automata (LA). In a multitude of real-world situations, resources must be allocated based on incomplete and noisy information, which often renders traditional resource allocation techniques ineffective. This paper addresses one such class of problems, namely, Stochastic Non-linear Fractional Knapsack Problems. We first present a completely …
Estimating biophysical variable dependences with kernels
2010
This paper introduces a nonlinear measure of dependence between random variables in the context of remote sensing data analysis. The Hilbert-Schmidt Independence Criterion (HSIC) is a kernel method for evaluating statistical dependence. HSIC is based on computing the Hilbert-Schmidt norm of the cross-covariance operator of mapped samples in the corresponding Hilbert spaces. The HSIC empirical estimator is very easy to compute and has good theoretical and practical properties. We exploit the capabilities of HSIC to explain nonlinear dependences in two remote sensing problems: temperature estimation and chlorophyll concentration prediction from spectra. Results show that, when the relationshi…
Hydrological post-processing based on approximate Bayesian computation (ABC)
2019
[EN] This study introduces a method to quantify the conditional predictive uncertainty in hydrological post-processing contexts when it is cumbersome to calculate the likelihood (intractable likelihood). Sometimes, it can be difficult to calculate the likelihood itself in hydrological modelling, specially working with complex models or with ungauged catchments. Therefore, we propose the ABC post-processor that exchanges the requirement of calculating the likelihood function by the use of some sufficient summary statistics and synthetic datasets. The aim is to show that the conditional predictive distribution is qualitatively similar produced by the exact predictive (MCMC post-processor) or …
Optimal Delay-Power Tradeoff in Sparse Delay Tolerant Networks: a preliminary study
2006
In this paper we present a first attempt to study analytically the tradeoff between delivery delay and resource consumption for epidemic routing in Delay Tolerant Networks. We assume that the nodes cooperate in order to minimize a common cost equal to a weighted sum of the packet delivery delay and the total number of copies, which is strongly related to the power consumption. In this framework we determine the best policy each node should deploy in a very simple scenario where all the nodes have perfect knowledge of the system status. The result is used as an ideal reference to evaluate the performance of some heuristics proposed, investigating potential performance improvements and config…
A Local Selection Algorithm for Switching Function Minimization
1984
The minimization algorithms which do not require any preliminary generation of all the prime implicants (PI's) of a function are the most efficient. In this work a new algorithm is described which follows such an approach. It is based on a local selection of PI's carried out by examining a set of vertices whose number is never greater than the number of PI's of a minimum cost cover. This algorithm takes advantage of a technique which uses numerical equivalents of the function vertices as pointers. For this reason it is well suited for implementation by computer. To illustrate the features of this algorithm a few examples are reported.
Cross-entropy-based adaptive optimization of simulation parameters for Markovian-driven service systems
2005
Abstract Markov fluid models represent a general description of the process of service request arrivals to service systems. The solution of performance analysis problems incorporating them often calls for a simulation approach, for which a reference methodology is Importance Sampling. However, in this case the appropriate choice of the biasing conditions is a problem in itself. In this paper an iterative method based on the cross-entropy is proposed for this choice. The equations are given that allow to derive the biasing conditions from the simulation itself. The application of the proposed method to three different sample cases, referring to one transient scenario (finite time horizon and…
Optimal Sizing and Siting of Distributed Energy Resources Considering Public and Private Incentive Policies
2008
The present work presents the formulation and solution approach for the problem of optimal sizing and siting of distributed energy resources based on Photovoltaic, PV, technology. The considered system is an isolated grid (small island) and the parts involved are the utility and the customers. As it happens in islands, the same utility generates and delivers energy to customers, for this reason, the installation of dispersed generation units is beneficial for reducing power losses, regularizing the voltage profile, but also for increasing the profit. The problem is solved by means of the Non dominated sorting Genetic Algorithm II, NSGA-II, identifying the optimal size and location of PV sys…
An approximate fixed point result for multivalued mappings under two constraint inequalities
2017
We consider an approximate multivalued fixed point problem under two constraint inequalities, for which we provide sufficient conditions for the existence of at least one solution. Then, we present some consequences and related results.
Constructive procedures to solve 2-dimensional bin packing problems with irregular pieces and guillotine cuts
2015
Abstract This paper presents an approach for solving a new real problem in cutting and packing. At its core is an innovative mixed integer programme model that places irregular pieces and defines guillotine cuts. The two-dimensional irregular shape bin packing problem with guillotine constraints arises in the glass cutting industry, for example, the cutting of glass for conservatories. Almost all cutting and packing problems that include guillotine cuts deal with rectangles only, where all cuts are orthogonal to the edges of the stock sheet and a maximum of two angles of rotation are permitted. The literature tackling packing problems with irregular shapes largely focuses on strip packing i…