Topic
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.
Papers published on a yearly basis
Papers
More filters
Book•
30 Sep 1998TL;DR: In this paper, the average cost optimization theory for countable state spaces is presented, as well as an inventory model for finite state spaces and a cost minimization theory for continuous time processes.
Abstract: Optimization Criteria. Finite Horizon Optimization. Infinite Horizon Discounted Cost Optimization. An Inventory Model. Average Cost Optimization for Finite State Spaces. Average Cost Optimization Theory for Countable State Spaces. Computation of Average Cost Optimal Policies for Infinite State Spaces. Optimization Under Actions at Selected Epochs. Average Cost Optimization of Continuous Time Processes. Appendices. Bibliography. Index.
475 citations
TL;DR: A novel and efficient approach to dense image registration, which does not require a derivative of the employed cost function is introduced, and efficient linear programming using the primal dual principles is considered to recover the lowest potential of the cost function.
469 citations
TL;DR: This paper presents an overview of the research progress in deterministic global optimization during the last decade (1998-2008).
Abstract: This paper presents an overview of the research progress in deterministic global optimization during the last decade (1998---2008). It covers the areas of twice continuously differentiable nonlinear optimization, mixed-integer nonlinear optimization, optimization with differential-algebraic models, semi-infinite programming, optimization with grey box/nonfactorable models, and bilevel nonlinear optimization.
453 citations
Book•
01 Jan 1970
450 citations
TL;DR: In this paper, an improved algorithm is presented for the discrete optimization of finite-impulse response (FIR) digital filter coefficients which are represented by a canonic signed-digit (CSD) code, i.e., numbers representable as sums or differences of powers of two.
Abstract: An improved algorithm is presented for the discrete optimization of finite-impulse-response (FIR) digital filter coefficients which are represented by a canonic signed-digit (CSD) code, ie, numbers representable as sums or differences of powers-of-two The proposed search algorithm allocates an extra nonzero digit in the CSD code to the larger coefficients to compensate for the very nonuniform nature of the CSD coefficient distribution This results in a small increase in the filter complexity; however, the improvement in the frequency response is substantial The coefficient optimization is performed in two stages The first stage searches for an optimum scale factor and the second stage consists of a local bivariate search in the neighborhood of the scaled and rounded coefficients >
447 citations