This paper addresses the problem of designing a data-dimension reducing transformation matrix, to be used for beamspace direction-of-arrival (DOA) estimation. The design of the transformation matrix is based on numerical optimization techniques. The criteria to be satissed is to retain as much as possible of the achievable, optimal estimation accuracy using the non-reduced data-sizes, while als...

We present a bundle method for convex nondifferentiable minimization where the model is a piecewise-quadratic convex approximation of the objective function. Unlike standard bundle approaches, the model only needs to support the objective function from below at a properly chosen (small) subset of points, as opposed to everywhere. We provide the convergence analysis for the algorithm, with a gen...

in this work, by using the particle swarm optimization the electron raman scattering for square double quantum wells is optimized. for this purpose, by combining the particle swarm algorithm together with the numerical solution procedures for equations, and also the perturbation theory we find the optimal structure that maximizes the electron raman scattering. application of this algorithm to t...

Differential evolution (DE) is a population-based stochastic search algorithm which has shown a good performance in solving many benchmarks and real-world optimization problems. Individuals in the standard DE, and most of its modifications, exhibit the same search characteristics because of the use of the same DE scheme. This paper proposes a simple and effective heterogeneous DE (HDE) to balan...

This paper presents a software infrastructure for high performance numerical optimization on clusters of multicore systems. At the core, a runtime system implements a programming and execution environment for irregular and adaptive task-based parallelism. Building on this, we extract and exploit the parallelism of a global optimization application at multiple levels, which include Hessian calcu...

Many applications require the minimization of a smooth function f : R nu → R whose evaluation requires the solution of a system of nonlinear equations. This system represents a numerical simulation that must be run to evaluate f. This system of nonlinear equations is referred to as an implicit constraint. In principle f can be minimized using the steepest descent method or Newton-type methods f...

Powerful simulation tools are required for every accelerator and light source to study the motion of charged particles through electromagnetic fields during the accelerator design process, to optimize the performance of machine diagnostics and to assess beam stability and non-linear effects. The Optimization of Particle Accelerators (oPAC) Project is funded by the EU within the 7 Framework Prog...

In this paper we describe a novel framework for finding numerical solutions to a wide range of shape optimization problems. It is based on classical dynamic programming approach augmented with discretization of the space of trajectories and controls. This allows for straightforward algorithmic implementation. This method has been used to solve a well known problem called the ”dividing tube prob...

A swarm algorithm framework (SWAF), realized by agent-based modeling, is presented to solve numerical optimization problems. Each agent is a bare bones cognitive architecture, which learns knowledge by appropriately deploying a set of simple rules in fast and frugal heuristics. Two essential categories of rules, the generate-and-test and the problem-formulation rules, are implemented, and both ...

This study presents the numerical simulation of three-dimensional incompressible steady and laminar fluid flow and conjugate heat transfer of a trapezoidal microchannel heat sink using water as a cooling fluid in a silicon substrate. Navier-Stokes equations with conjugate energy equation are discretized by finite-volume method. We perform numerical computations for a range of 50 Re 600, 0.05W ...