Jan A. Snyman

Bio: Jan A. Snyman is an academic researcher. The author has contributed to research in topics: Simplex algorithm & Linear programming. The author has an hindex of 1, co-authored 1 publications receiving 799 citations.

Practical mathematical optimization : an introduction to basic optimization theory and classical and new gradient-based algorithms /by Jan A. Snyman

Abstract: Preface Table of Notation Chapter 1. Introduction Chapter 2. Line Search Descent Methods for Unconstrained Minimization Chapter 3. Standard Methods for Constrained Optimization Chapter 4. New Gradient-Based Trajectory and Approximation Methods Chapter 5. Example Problems Chapter 6. Some Theorems Chapter 7. The Simplex Method for Linear Programming Problems Bibliography Index

VTEAM: A General Model for Voltage-Controlled Memristors

Abstract: Memristors are novel electrical devices used for a variety of applications, including memory, logic circuits, and neuromorphic systems. Memristive technologies are attractive due to their nonvolatility, scalability, and compatibility with CMOS. Numerous physical experiments have shown the existence of a threshold voltage in some physical memristors. Additionally, as shown in this brief, some applications require voltage-controlled memristors to operate properly. In this brief, a Voltage ThrEshold Adaptive Memristor (VTEAM) model is proposed to describe the behavior of voltage-controlled memristors. The VTEAM model extends the previously proposed ThrEshold Adaptive Memristor (TEAM) model, which describes current-controlled memristors. The VTEAM model has similar advantages as the TEAM model, i.e., it is simple, general, and flexible, and can characterize different voltage-controlled memristors. The VTEAM model is accurate (below 1.5% in terms of the relative root-mean-square error) and computationally efficient as compared with existing memristor models and experimental results describing different memristive technologies.

Decomposition of skin conductance data by means of nonnegative deconvolution

Abstract: Skin conductance (SC) data are usually characterized by a sequence of overlapping phasic skin conductance responses (SCRs) overlying a tonic component. The variability of SCR shapes hereby complicates the proper decomposition of SC data. A method is proposed for full decomposition of SC data into tonic and phasic components. A two-compartment diffusion model was found to adequately describe a standard SCR shape based on the process of sweat diffusion. Nonnegative deconvolution is used to decompose SC data into discrete compact responses and at the same time assess deviations from the standard SCR shape, which could be ascribed to the additional process of pore opening. Based on the result of single non-overlapped SCRs, response parameters can be estimated precisely as shown in a paradigm with varying inter-stimulus intervals.

Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications

Abstract: Bacterial foraging optimization algorithm (BFOA) has been widely accepted as a global optimization algorithm of current interest for distributed optimization and control BFOA is inspired by the social foraging behavior of Escherichia coli BFOA has already drawn the attention of researchers because of its efficiency in solving real-world optimization problems arising in several application domains The underlying biology behind the foraging strategy of Ecoli is emulated in an extraordinary manner and used as a simple optimization algorithm This chapter starts with a lucid outline of the classical BFOA It then analyses the dynamics of the simulated chemotaxis step in BFOA with the help of a simple mathematical model Taking a cue from the analysis, it presents a new adaptive variant of BFOA, where the chemotactic step size is adjusted on the run according to the current fitness of a virtual bacterium Nest, an analysis of the dynamics of reproduction operator in BFOA is also discussed The chapter discusses the hybridization of BFOA with other optimization techniques and also provides an account of most of the significant applications of BFOA until date