Open AccessBook
Practical mathematical optimization : an introduction to basic optimization theory and classical and new gradient-based algorithms /by Jan A. Snyman
Jan A. Snyman
- 01 Jan 2005
832
TL;DR: The Simplex Method for Linear Programming Problems is a method for solving linear programming problems with real-time requirements.
read more
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
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A continuous measure of phasic electrodermal activity.
TL;DR: A deconvolution approach is proposed, which separates SC data into continuous signals of tonic and phasic activity, which shows a zero baseline, and overlapping SCRs are represented by predominantly distinct, compact impulses showing an average duration of less than 2 s.
1.5K
VTEAM: A General Model for Voltage-Controlled Memristors
TL;DR: The VTEAM model extends the previously proposed ThrEshold Adaptive Memristor (TEAM) model, which describes current-controlled memristors and has similar advantages as the TEAM model, i.e., it is simple, general, and flexible, and can characterize different voltage-controlled Memristors.
813
Decomposition of skin conductance data by means of nonnegative deconvolution
TL;DR: A two-compartment diffusion model was found to adequately describe a standard SCR shape based on the process of sweat diffusion and nonnegative deconvolution is used to decompose SC data into discrete compact responses.
Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications
Swagatam Das,Arijit Biswas,Sambarta Dasgupta,Ajith Abraham +3 more
- 01 Jan 2009
TL;DR: This chapter 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, and discusses the hybridization of B FOA with other optimization techniques.
•Posted Content
Maximum Entropy Deep Inverse Reinforcement Learning
TL;DR: It is shown that the Maximum Entropy paradigm for IRL lends itself naturally to the efficient training of deep architectures, and the approach achieves performance commensurate to the state-of-the-art on existing benchmarks while exceeding on an alternative benchmark based on highly varying reward structures.
References
A simplex method for function minimization
John A. Nelder,R. Mead +1 more
TL;DR: A method is described for the minimization of a function of n variables, which depends on the comparison of function values at the (n 41) vertices of a general simplex, followed by the replacement of the vertex with the highest value by another point.
30.6K
•Book
Practical Methods of Optimization
Roger Fletcher
- 01 Jan 2009
TL;DR: The aim of this book is to provide a Discussion of Constrained Optimization and its Applications to Linear Programming and Other Optimization Problems.
9.3K
•Book
Nonlinear Programming: Theory and Algorithms
Mokhtar S. Bazaraa
- 03 Mar 1993
TL;DR: The book is a solid reference for professionals as well as a useful text for students in the fields of operations research, management science, industrial engineering, applied mathematics, and also in engineering disciplines that deal with analytical optimization techniques.
6.4K
Related Papers (5)
George Lindfield,John Penny +1 more
- 01 Jan 2017
Marco Locatelli,Fabio Schoen +1 more
- 14 Oct 2013