Optimization Techniques

MATLAB Optimization Techniques  eBooks & eLearning

Posted by ksveta6 at Nov. 23, 2014
MATLAB Optimization Techniques

MATLAB Optimization Techniques by Cesar Lopez
2014 | ISBN: 1484202937 | English | 292 pages | PDF | 8 MB
Stochastic Global Optimization Techniques and Applications in Chemical Engineering: Techniques and Applications in... (repost)

Stochastic Global Optimization Techniques and Applications in Chemical Engineering: Techniques and Applications in Chemical Engineering by Gade Pandu Rangaiah
English | 2010-06-04 | ISBN: 9814299200 | PDF | 724 pages | 6,6 MB
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning By Abhijit Gosavi (auth.)
2003 | 554 Pages | ISBN: 144195354X | PDF | 22 MB
Stochastic Global Optimization Techniques and Applications in Chemical Engineering: Techniques and Applications in Chemical

Stochastic Global Optimization Techniques and Applications in Chemical Engineering: Techniques and Applications in Chemical Engineering by Gade Pandu Rangaiah
English | 2010-06-04 | ISBN: 9814299200 | PDF | 724 pages | 6,6 MB

Ultimate Search Engine Optimization Techniques 2018  eBooks & eLearning

Posted by Sigha at Jan. 23, 2019
Ultimate Search Engine Optimization Techniques 2018

Ultimate Search Engine Optimization Techniques 2018
.MP4 | Video: 1280x720, 30 fps(r) | Audio: AAC, 48000 Hz, 2ch | 527 MB
Duration: 2.5 hours | Genre: eLearning | Language: English

Learn The Best Techniques And Strategies To Get Your Website To NO 1 - Ultimate Search Engine Optimization (SEO) Course.

Adaptive Stochastic Optimization Techniques with Applications  eBooks & eLearning

Posted by ksveta6 at Jan. 19, 2016
Adaptive Stochastic Optimization Techniques with Applications

Adaptive Stochastic Optimization Techniques with Applications by James A. Momoh
2015 | ISBN: 1439829780 | English | 442 pages | PDF | 6 MB
Optimization Techniques in Computer Vision: Ill-Posed Problems and Regularization

Optimization Techniques in Computer Vision: Ill-Posed Problems and Regularization (Advances in Computer Vision and Pattern Recognition) by Mongi A. Abidi
English | 21 Dec. 2016 | ISBN: 3319463632 | 312 Pages | PDF | 7.47 MB

This book presents practical optimization techniques used in image processing and computer vision problems. Ill-posed problems are introduced and used as examples to show how each type of problem is related to typical image processing and computer vision problems. Unconstrained optimization gives the best solution based on numerical minimization of a single, scalar-valued objective function or cost function.

Adaptive Stochastic Optimization Techniques with Applications  eBooks & eLearning

Posted by arundhati at Jan. 26, 2020
Adaptive Stochastic Optimization Techniques with Applications

James A. Momoh, "Adaptive Stochastic Optimization Techniques with Applications"
English | ISBN: 1439829780 | 2015 | 442 pages | PDF | 6 MB

Optimization Techniques in Computer Vision: Ill-Posed Problems and Regularization  eBooks & eLearning

Posted by AlenMiler at Sept. 13, 2018
Optimization Techniques in Computer Vision: Ill-Posed Problems and Regularization

Optimization Techniques in Computer Vision: Ill-Posed Problems and Regularization (Advances in Computer Vision and Pattern Recognition) by Mongi A. Abidi, Andrei V. Gribok, Joonki Paik
English | December 7, 2016 | ISBN: 3319463632 | 293 pages | AZW3 | 4.27 MB
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning, 2 edition

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning, 2 edition by Abhijit Gosavi
English | 2014 | ISBN: 1489974903 | 508 pages | PDF | 5 MB

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques – especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms.