'Unconstrained optimization' presentation slideshows

CVEN 5393 Apr 11, 2011

727 views • 72 slides

metalab.uniten.my/~farrukh/Utim/Utimoptim.zip

926 views • 59 slides

Derivatives

Derivatives. Derivative of a constant. Y. Y=3. Y1. X. X1. X2. Derivative of a line. Y=5X. Y. Y2=15. Y1=10. X1=2. X2=3. X. Derivative of a polynomial function. Examples:. Derivatives of sums and differences. In general:. For. or. Derivatives of products and quotients.

504 views • 32 slides

Nonlinear Stochastic Programming by the Monte-Carlo method

Nonlinear Stochastic Programming by the Monte-Carlo method. Lecture 4. Leonidas Sakalauskas Institute of Mathematics and Informatics Vilnius, Lithuania EURO Working Group on Continuous Optimization. Content. Stochastic unconstrained optimization Monte Carlo estimators

440 views • 25 slides

Optimization Introduction & 1-D Unconstrained Optimization

329 views • 21 slides

System-level Calibration for Fusion-based Wireless Sensor Networks

System-level Calibration for Fusion-based Wireless Sensor Networks. Rui Tan 1 Guoliang Xing 1 Zhaohui Yuan 2 Xue Liu 3 Jianguo Yao 4 1 Michigan State University, USA 2 Wuhan University, P.R. China 3 University of Nebraska-Lincoln, USA

323 views • 19 slides

Programming Nanocells: Optimization Algorithms

Programming Nanocells: Optimization Algorithms. John Reif CS Dept, Duke University. Optimization of Nanocells. Given imperfect information about interior connectivity of nanocell (probabilistic model) Given desired I/O behavior Find pattern of control variables X input variables

186 views • 4 slides