Search results for "Mathematical optimization"
showing 10 items of 1300 documents
Numerical Simulation of a Contractivity Based Multiscale Cancer Invasion Model
2017
We present a problem-suited numerical method for a particularly challenging cancer invasion model. This model is a multiscale haptotaxis advection-reaction-diffusion system that describes the macroscopic dynamics of two types of cancer cells coupled with microscopic dynamics of the cells adhesion on the extracellular matrix. The difficulties to overcome arise from the non-constant advection and diffusion coefficients, a time delay term, as well as stiff reaction terms.
Performance modeling of epidemic routing
2006
In this paper, we develop a rigorous, unified framework based on ordinary differential equations (ODEs) to study epidemic routing and its variations. These ODEs can be derived as limits of Markovian models under a natural scaling as the number of nodes increases. While an analytical study of Markovian models is quite complex and numerical solution impractical for large networks, the corresponding ODE models yield closed-form expressions for several performance metrics of interest, and a numerical solution complexity that does not increase with the number of nodes. Using this ODE approach, we investigate how resources such as buffer space and the number of copies made for a packet can be tra…
Stochastic reconstruction of sandstones
2000
A simulated annealing algorithm is employed to generate a stochastic model for a Berea and a Fontainebleau sandstone with prescribed two-point probability function, lineal path function, and ``pore size'' distribution function, respectively. We find that the temperature decrease of the annealing has to be rather quick to yield isotropic and percolating configurations. A comparison of simple morphological quantities indicates good agreement between the reconstructions and the original sandstones. Also, the mean survival time of a random walker in the pore space is reproduced with good accuracy. However, a more detailed investigation by means of local porosity theory shows that there may be s…
Dealing with uncertainty in consensus protocols
2009
Recent results on consensus protocols for networks are presented. The basic tools and the main contribution available in the literature are considered, together with some of the related challenging aspects: estimation in networks and how to deal with disturbances is considered. Motivated by applications to sensor, peer-to-peer, and ad hoc networks, many papers have considered the problem of estimation in a consensus fashion. Here, the Unknown But Bounded (UBB) noise affecting the network is addressed in details. Because of the presence of UBB disturbances convergence to equilibria with all equal components is, in general, not possible. The solution of the e-consensus problem, where the stat…
Numerical Experiments with Multilevel Schemes for Conservation Laws
2001
Main steps of a point-value multilevel algorithm are presented and numerical results for a two dimensional test case of gas dynamics are discussed in terms of quality and efficiency.
Multiresolution-based adaptive schemes for Hyperbolic Conservation Laws
2006
Starting in the early nineties, wavelet and wavelet-like techniques have been successfully used to design adaptive schemes for the numerical solution of certain types of PDE. In this paper we review two representative examples of the development of such techniques for Hyperbolic Conservation Laws.
Constraint handling in efficient global optimization
2017
Real-world optimization problems are often subject to several constraints which are expensive to evaluate in terms of cost or time. Although a lot of effort is devoted to make use of surrogate models for expensive optimization tasks, not many strong surrogate-assisted algorithms can address the challenging constrained problems. Efficient Global Optimization (EGO) is a Kriging-based surrogate-assisted algorithm. It was originally proposed to address unconstrained problems and later was modified to solve constrained problems. However, these type of algorithms still suffer from several issues, mainly: (1) early stagnation, (2) problems with multiple active constraints and (3) frequent crashes.…
Project duration evaluated using affine arithmetic
2016
A civil engineering work can be performed by organizing the available resources (manpower, equipment and materials) in many different ways. Each different configuration results in a realization time and a cost that a building company has to bear. To produce reliable duration forecasts and money savings, it is essential to take into account all the uncertainties involved in the project operations. Generally, since it is impractical to process numerous uncertain variables - also undefined from a statistical point of view -, traditional probabilistic methods involve application difficulties for complex environments such as construction sites. To properly handle this issue, the authors propose …
NAUTILUS Navigator : free search interactive multiobjective optimization without trading-off
2019
We propose a novel combination of an interactive multiobjective navigation method and a trade-off free way of asking and presenting preference information. The NAUTILUS Navigator is a method that enables the decision maker (DM) to navigate in real time from an inferior solution to the most preferred solution by gaining in all objectives simultaneously as (s)he approaches the Pareto optimal front. This means that, while the DM reaches her/his most preferred solution, (s)he avoids anchoring around the starting solution and, at the same time, sees how the ranges of the reachable objective function values shrink without trading-off. The progress of the motion towards the Pareto optimal front is…
PAINT–SiCon: constructing consistent parametric representations of Pareto sets in nonconvex multiobjective optimization
2014
We introduce a novel approximation method for multiobjective optimization problems called PAINT–SiCon. The method can construct consistent parametric representations of Pareto sets, especially for nonconvex problems, by interpolating between nondominated solutions of a given sampling both in the decision and objective space. The proposed method is especially advantageous in computationally expensive cases, since the parametric representation of the Pareto set can be used as an inexpensive surrogate for the original problem during the decision making process. peerReviewed