Factorization of linear programming (LP) models enables a large portion of the LP tableau to be represented implicitly and generatedfrom the remainingexplicit part. Dynamicfactorization admits algebraicelementswhichchangeindimensionduring the courseof solution.A unifyingmathematical framework for dynamic row factorization is presented with three algorithms which derive from differentLP modelrow...

This paper describes a class of optimization methods that interlace iterations of the limited memory BFGS method L BFGS and a Hessian free Newton method HFN in such a way that the information collected by one type of iteration improves the performance of the other Curvature information about the objective function is stored in the form of a limited memory matrix and plays the dual role of preco...

of the Dissertation Statistical Analysis and Optimization for Timing and Power of VLSI Circuits

Power dissipation has emerged as an important design parameter in the design of microelectronic circuits, especially in portable computing and personal communication applications. In this paper, we survey state-of-the-art optimization methods that target low power dissipation in VLSI circuits. Optimizations at the circuit, logic, architectural and system levels are considered.

Swarm Intelligence (SI) is a relatively new technology that takes its inspiration from the behavior of social insects and flocking animals. In this paper, we focus on two main SI algorithms: Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). An extension of ACO algorithm and a PSO algorithm has been implemented to solve the portfolio optimization problem, which is a continuous...

Optimization applications often depend upon a huge number of uncertain parameters. In many contexts, however, the amount of relevant data per parameter is small, and hence, we may have only imprecise estimates. We term this setting – where the number of uncertainties is large, but all estimates have fixed and low precision – the “small-data, large-scale regime.” We formalize a model for this re...

This paper deals with price optimization, which is to find the best pricing strategy that maximizes revenue or profit, on the basis of demand forecasting models. Though recent advances in regression technologies have made it possible to reveal price-demand relationship of a large number of products, most existing price optimization methods, such as mixed integer programming formulation, cannot ...

Efficiency in aircraft production can be increased by using flexible robotic assembly systems instead of fixed jigs, but the flexibility can only be used in combination with efficient control algorithms. For large components which have an individual deformation, e.g. due to gravity, not only automated but self-optimizing control algorithms are required, which allow an autonomous product-specifi...

ion methods for dimensionality-reduction Abstractions methods are used to complement iterative algorithms for computing equilibria. Abstractions are usually created algorithmically, by utilizing domain-dependent structure to set up a manageable optimization problem that produces a smaller game which retains as much of the original game structure as possible. No reasonable bounds on solution qua...