Discrete optimization

About: Discrete optimization is a research topic. Over the lifetime, 4598 publications have been published within this topic receiving 158297 citations. The topic is also known as: discrete optimisation.

An optimization problem in distributed loop computer networks

Abstract: This paper deals with a combinatorial problem arising from studies in distributed and parallel architectures. The interconnection network is the heart of parallel computers, and several topologies have been proposed (see [l], [2], [16]). One of the most used is the loop topology, due to its simplicity [13]; in this case, the network consists of a loop (directed or not), which is modeled by a cycle (directed or not). But the simple loop topology has some disadvantages, in particular, its high vulnerability and low performance. Indeed, the network does not work after the failure of one node or arc (in the directed case) or two nodes or edges (in the undirected case). Furthermore, the diameter of the loop network, which corresponds to

Discrete Optimization of Ray Potentials for Semantic 3D Reconstruction

Abstract: Dense semantic 3D reconstruction is typically formulated as a discrete or continuous problem over label assignments in a voxel grid, combining semantic and depth likelihoods in a Markov Random Field framework. The depth and semantic information is incorporated as a unary potential, smoothed by a pairwise regularizer. However, modelling likelihoods as a unary potential does not model the problem correctly leading to various undesirable visibility artifacts. We propose to formulate an optimization problem that directly optimizes the reprojection error of the 3D model with respect to the image estimates, which corresponds to the optimization over rays, where the cost function depends on the semantic class and depth of the first occupied voxel along the ray. The 2-label formulation is made feasible by transforming it into a graph-representable form under QPBO relaxation, solvable using graph cut. The multi-label problem is solved by applying alpha-expansion using the same relaxation in each expansion move. Our method was indeed shown to be feasible in practice, running comparably fast to the competing methods, while not suffering from ray potential approximation artifacts.

Optimization methods in electronic circuit design

Abstract: Part 1 Optimization as a circuit design tool: a generalized strategy for engineering design optimization and function minimization function space and the optimization problem of computer-aided design scope of the book. Part 2 Preliminary concepts: stationary points of functions unidirectional search classification of optimization methods. Part 3 Direct search optimization methods: tabulation methods sequential methods linear methods quadratically terminating direct search methods. Part 4 Gradient optimization methods: steepest descent Newton's method quasi-Newton methods least squares (Gauss-Newton) methods. Part 5 Unconstrained optimization in practice: local minima selection of an algorithm gradient evaluation. Part 6 Constrained optimization methods: classes of constrained optimization method linear programming quadratic and nonlinear programming commercial availability of constrained optimization algorithms. Part 7 Applications in electronic circuit design: optimization of linear frequency-selective networks optimization of nonlinear networks multiple-criterion optimization and statistical design of integrated circuits simulated annealing - a global optimization method? the future of optimization in electronic systems design.