This study explores the use of teaching-learning-based optimization (TLBO) algorithm for thermodynamic optimization of a plate-fin heat exchanger. Minimizing the total number of entropy generation units for specific heat duty requirement under given space restrictions is considered as objective function. Based on the applications heat exchanger length, fin frequency, numbers of fin layers, lanc...

The application of classical optimization techniques to Graphical Models has led to specialized derivations of powerful paradigms such as the class of EM algorithms, variational inference, max-margin and maximum entropy learning. This view has also sustained a conceptual bridge between the research communities of Graphical Models, Statistical Physics and Numerical Optimization. The optimization...

The cross-entropy method is a recent versatile Monte Carlo technique. This article provides a brief introduction to the cross-entropy method and discusses how it can be used for rare-event probability estimation and for solving combinatorial, continuous, constrained and noisy optimization problems. A comprehensive list of references on cross-entropy methods and applications is included.

Fractal image compression uses the property of selfsimilarity in an image and utilizes the partitioned iterated function system to encode it. Fractal image compression is attractive because of high compression ratio, fast decompression and multi-resolution properties. The main drawback of Fractal Image Compression is the high computational cost and is the poor retrieved image qualities. To over...

To aware the topology of wireless sensor networks (WSN) with time-variety, and load-balance the resource of communication and energy, an opportunistic routing protocol for WSN based on Opportunistic Routing Entropy and ant colony optimization, called ACO-TDOP, is proposed. At first, based on the second law of thermo-dynamics, we introduce the concept of Opportunistic Routing Entropy which is a ...

Rate distortion optimization (RDO) technique is the best known mode decision method in recent video coding standard, such as H.264 and AVS. However, the unbearable computational burden limits its application. According to the proposed block-level pipeline architecture of RDO-based MD, we find that zigzag scanning and entropy coding are the bottlenecks. In our paper, we firstly analyze the time ...

During the past years Lindquist and coworkers have formulated and studied the so called generalized maximum entropy problem. It generalizes the maximum entropy problem and thus includes problem like the Carathéodory extension problem and the Nevanlinna-Pick interpolation problem. A central ingredient in the theory is the dual (in mathematical programming sense) convex optimization problem. This...

Multiple-target tracking (MTT) in the presence of spurious measurements poses difficult computational challenges related to the measurement-to-track data association problem. Different solution approaches have been proposed to tackle this problems, including various approximations and heuristic optimization tools. The Cross Entropy method is a recent optimization heuristic that has proved usefu...

We propose a novel information-theoretic approach for Bayesian optimization called Predictive Entropy Search (PES). At each iteration, PES selects the next evaluation point that maximizes the expected information gained with respect to the global maximum. PES codifies this intractable acquisition function in terms of the expected reduction in the differential entropy of the predictive distribut...

Relative entropy has been shown to provide a principled framework for the selection of coarse-grained potentials. Despite the intellectual appeal of it, its application has been limited by the fact that it requires the solution of an optimization problem with noisy gradients. When using deterministic optimization schemes, one is forced to either decrease the noise by adequate sampling or to res...