'Lagrange multipliers' diaporamas de présentation

??????? Phases

??????? Phases. Philippe CHOMAZ - GANIL. Thermodynamic, Equilibrium Statistical ensemble Observation space Information & entropy Gibbs Equilibria Example: Mean-field Discussion What is temperature? What is equilibrium? Ensemble inequivalence Time dependent equilibria

2.72k views • 253 slides

PARTIAL DERIVATIVES

1.03k views • 79 slides

PARTIAL DERIVATIVES

958 views • 79 slides

MATH 685/ CSI 700/ OR 682 Lecture Notes

MATH 685/ CSI 700/ OR 682 Lecture Notes. Lecture 9. Optimization problems. Optimization. Optimization problems. Examples. Global vs. local optimization. Global optimization. Finding, or even verifying, global minimum is difficult, in general

953 views • 69 slides

Inexact SQP methods for equality constrained optimization

ISMP 2006. Inexact SQP methods for equality constrained optimization. Frank Edward Curtis Department of IE/MS, Northwestern University with Richard Byrd and Jorge Nocedal August 1, 2006. Outline. Introduction/Motivation Unconstrained optimization Nonlinear equations

640 views • 44 slides

Resource Allocation and Pricing

Resource Allocation and Pricing. R. Srikant University of Illinois. References. The Mathematics of Internet Congestion Control , Birkhauser, 2004. Pricing: Kelly Distributed Resource Allocation: Kelly, Mauloo and Tan, Low and Lapsley, Kunniyur and S., Wen and Arcak, Liu, Basar and S.

557 views • 37 slides

A Cloud Motion Winds Diffusion Scheme for Quantitative Rainfall Estimation

497 views • 35 slides

Lecture 7 Rolling Constraints

Lecture 7 Rolling Constraints. The most common, and most important nonholonomic constraints. They cannot be written in terms of the variables alone you must include some derivatives. The resulting differential is not integrable. The major assumption/simplification is point contact.

373 views • 30 slides

By :-

473 views • 30 slides

A Combinatorial Maximum Cover Approach to 2D Translational Geometric Covering

A Combinatorial Maximum Cover Approach to 2D Translational Geometric Covering. Karen Daniels, Arti Mathur, Roger Grinde University of Massachusetts Lowell and University of New Hampshire 11 August, 2003. http://www.cs.uml.edu/~kdaniels. Acknowledgment: Cristina Neacsu. future work:.

418 views • 29 slides

Support Vector Machines Kernel Machines

Support Vector Machines Kernel Machines. Ata Kaban The University of Birmingham. Remember the XOR problem?. Today we learn how to solve it. Remember the XOR problem?. Support Vector Machines (SVM). Method for supervised learning problems Classification Regression Two key ideas

427 views • 24 slides

Gang Wang Derek Hoiem David Forsyth

Learning Image Similarity from Flick r Groups Using Stochastic Intersection Kernel Machines ICCV 2009, UIUC. Gang Wang Derek Hoiem David Forsyth. OUTLINE. INTRODUCTION APROACH (implement detail) EXPERIMENTS CONCLUSION. Introduction.

346 views • 19 slides

CoNMF: Exploiting User Comments for Clustering Web2.0 Items

CoNMF: Exploiting User Comments for Clustering Web2.0 Items. Presenter: He Xiangnan 28 June 2013 Email: xiangnan@comp.nus.edu.sg School of Computing National University of Singapore. Introduction. Motivations: Users comment on items based on their own interests.

276 views • 17 slides

ACTIVE LEARNING USING CONFORMAL PREDICTORS: APPLICATION TO IMAGE CLASSIFICATION

Hyp Introduction Hyp Conceptual overview Hyp Experiments and results Hyp Conclusions. ACTIVE LEARNING USING CONFORMAL PREDICTORS: APPLICATION TO IMAGE CLASSIFICATION. L. Makili 1 , J. Vega 2 , S. Dormido-Canto 3 1 Universidade Katyavala Bwila . Benguela (Angola)

325 views • 16 slides

_______________________________________ _______________________________________

_______________________________________ _______________________________________ _______________________________________ _______________________________________ _______________________________________ _______________________________________ _______________________________________

262 views • 15 slides

12.10 Lagrange Multipliers

12.10 Lagrange Multipliers. Method of Lagrange Multipliers Use Lagrange to solve constrained optimization problems Use Lagrange with two constraints. 1 constraint. In terms of level curves. Steps. 2 constraints. Homework. Pg. 927 #1-9 odd, 13, 15, 19, 23.

594 views • 14 slides

CS B553: Algorithms for Optimization and Learning

CS B553: Algorithms for Optimization and Learning. Constrained optimization. Key Concepts. Constraint formulations Necessary o ptimality conditions Lagrange multipliers: equality constraints KKT conditions: equalities and inequalities. Figure 1. Objective function f. Figure 1.

219 views • 13 slides

Chapter 7 Functions of Several Variables

308 views • 12 slides

Lagrange Multipliers

Lagrange Multipliers. Lagrange Multipliers with One Constraint. 1. Use Lagrange multipliers to find the indicated extrema , assuming that x and y are positive (Similar to p.976 #5-10).

332 views • 7 slides

LS Cost with a Linear Equality Constraint

120 views • 2 slides