Slide 2a.1 Stiff Structures, Compliant Mechanisms, and MEMS: A short course offered at IISc, Bangalore, India. Aug.-Sep., 2003. G. K. Ananthasuresh Lecture.

Slides:



Advertisements
Similar presentations
Engineering Optimization
Advertisements

Business Calculus Improper Integrals, Differential Equations.
L12 LaGrange Multiplier Method Homework Review Summary Test 1.
Slide 4b.1 Stiff Structures, Compliant Mechanisms, and MEMS: A short course offered at IISc, Bangalore, India. Aug.-Sep., G. K. Ananthasuresh Lecture.
Optimality conditions for constrained local optima, Lagrange multipliers and their use for sensitivity of optimal solutions Today’s lecture is on optimality.
Slide 4f.1 Stiff Structures, Compliant Mechanisms, and MEMS: A short course offered at IISc, Bangalore, India. Aug.-Sep., G. K. Ananthasuresh Lecture.
Optimization of thermal processes2007/2008 Optimization of thermal processes Maciej Marek Czestochowa University of Technology Institute of Thermal Machinery.
© 2011 Autodesk Freely licensed for use by educational institutions. Reuse and changes require a note indicating that content has been modified from the.
Optimization in Engineering Design 1 Lagrange Multipliers.
Numerical Optimization
Calculus of Variations
Engineering Optimization
Economics 214 Lecture 37 Constrained Optimization.
Constrained Optimization
Constrained Optimization Rong Jin. Outline  Equality constraints  Inequality constraints  Linear Programming  Quadratic Programming.
Constrained Optimization Economics 214 Lecture 41.
MAE 552 – Heuristic Optimization Lecture 1 January 23, 2002.
Theoretical Mechanics - PHY6200 Chapter 6 Introduction to the calculus of variations Prof. Claude A Pruneau, Physics and Astronomy Department Wayne State.
D Nagesh Kumar, IIScOptimization Methods: M2L5 1 Optimization using Calculus Kuhn-Tucker Conditions.
Optimality Conditions for Nonlinear Optimization Ashish Goel Department of Management Science and Engineering Stanford University Stanford, CA 94305, U.S.A.
D Nagesh Kumar, IIScOptimization Methods: M2L3 1 Optimization using Calculus Optimization of Functions of Multiple Variables: Unconstrained Optimization.
1 Optimization. 2 General Problem 3 One Independent Variable x y (Local) maximum Slope = 0.
An Introduction to Optimization Theory. Outline Introduction Unconstrained optimization problem Constrained optimization problem.
Slide 3.1 Stiff Structures, Compliant Mechanisms, and MEMS: A short course offered at IISc, Bangalore, India. Aug.-Sep., G. K. Ananthasuresh Lecture.
Today Wrap up of probability Vectors, Matrices. Calculus
KKT Practice and Second Order Conditions from Nash and Sofer
L13 Optimization using Excel See revised schedule read 8(1-4) + Excel “help” for Mar 12 Test Answers Review: Convex Prog. Prob. Worksheet modifications.
9/08/2014PHY 711 Fall Lecture 61 PHY 711 Classical Mechanics and Mathematical Methods 10-10:50 AM MWF Olin 103 Plan for Lecture 6: Continue reading.
Physics 430: Lecture 14 Calculus of Variations Dale E. Gary NJIT Physics Department.
Revision Previous lecture was about Generating Function Approach Derivation of Conservation Laws via Lagrangian via Hamiltonian.
Computer Animation Rick Parent Computer Animation Algorithms and Techniques Optimization & Constraints Add mention of global techiques Add mention of calculus.
D Nagesh Kumar, IIScOptimization Methods: M2L4 1 Optimization using Calculus Optimization of Functions of Multiple Variables subject to Equality Constraints.
Slide 2b.1 Stiff Structures, Compliant Mechanisms, and MEMS: A short course offered at IISc, Bangalore, India. Aug.-Sep., G. K. Ananthasuresh Lecture.
Lecture 26 Molecular orbital theory II
Optimization unconstrained and constrained Calculus part II.
L8 Optimal Design concepts pt D
Dr. Wang Xingbo Fall , 2005 Mathematical & Mechanical Method in Mechanical Engineering.
Part 4 Nonlinear Programming 4.1 Introduction. Standard Form.
Phy 303: Classical Mechanics (2) Chapter 3 Lagrangian and Hamiltonian Mechanics.
(iii) Lagrange Multipliers and Kuhn-tucker Conditions D Nagesh Kumar, IISc Introduction to Optimization Water Resources Systems Planning and Management:
9/4/2015PHY 711 Fall Lecture 51 PHY 711 Classical Mechanics and Mathematical Methods 10-10:50 AM MWF Olin 103 Plan for Lecture 5: Start reading.
Introduction to Optimization
Calculus-Based Optimization AGEC 317 Economic Analysis for Agribusiness and Management.
Nonlinear Programming In this handout Gradient Search for Multivariable Unconstrained Optimization KKT Conditions for Optimality of Constrained Optimization.
CS B553: A LGORITHMS FOR O PTIMIZATION AND L EARNING Constrained optimization.
Economics 2301 Lecture 37 Constrained Optimization.
Linear Programming Chapter 9. Interior Point Methods  Three major variants  Affine scaling algorithm - easy concept, good performance  Potential.
Dr. Wang Xingbo Fall , 2005 Mathematical & Mechanical Method in Mechanical Engineering 1/3304/03/ :41.
Inequality Constraints Lecture 7. Inequality Contraints (I) n A Review of Lagrange Multipliers –As we discussed last time, the first order necessary conditions.
1 Introduction Optimization: Produce best quality of life with the available resources Engineering design optimization: Find the best system that satisfies.
Ch. 2: Variational Principles & Lagrange’s Eqtns Sect. 2.1: Hamilton’s Principle Our derivation of Lagrange’s Eqtns from D’Alembert’s Principle: Used.
D Nagesh Kumar, IISc Water Resources Systems Planning and Management: M2L2 Introduction to Optimization (ii) Constrained and Unconstrained Optimization.
Optimal Control.
1 Support Vector Machines: Maximum Margin Classifiers Machine Learning and Pattern Recognition: September 23, 2010 Piotr Mirowski Based on slides by Sumit.
Physics 312: Lecture 2 Calculus of Variations
Lecture 7 Constrained Optimization Lagrange Multipliers
Force as gradient of potential energy
Chapter 11 Optimization with Equality Constraints
L11 Optimal Design L.Multipliers
Physics 319 Classical Mechanics
The Lagrange Multiplier Method
L10 Optimal Design L.Multipliers
PRELIMINARY MATHEMATICS
Outline Unconstrained Optimization Functions of One Variable
CS5321 Numerical Optimization
PHY 711 Classical Mechanics and Mathematical Methods
PHY 711 Classical Mechanics and Mathematical Methods
Calculus-Based Optimization AGEC 317
Constraints.
L8 Optimal Design concepts pt D
Presentation transcript:

Slide 2a.1 Stiff Structures, Compliant Mechanisms, and MEMS: A short course offered at IISc, Bangalore, India. Aug.-Sep., G. K. Ananthasuresh Lecture 2a Mathematical Preliminaries for Optimal Design Essential basics of calculus of variations and constrained minimization

Slide 2a.2 Stiff Structures, Compliant Mechanisms, and MEMS: A short course offered at IISc, Bangalore, India. Aug.-Sep., G. K. Ananthasuresh Contents Minimum-time problems –Fermat’s problem and Snell’s law –Brachistochrone problem Constrained minimization –Lagrangian and conventions –Karush-Kuhn-Tucker necessary conditions –Sufficient conditions Calculus of variations –Functional and its variation –Fundamental lemma –Euler-Lagrange equations –Extensions to other situations –Constrained variational calculus problems

Slide 2a.3 Stiff Structures, Compliant Mechanisms, and MEMS: A short course offered at IISc, Bangalore, India. Aug.-Sep., G. K. Ananthasuresh Fermat’s light-ray problem (Feynman’s “life-guard on the beach” problem) What is the minimum-time path from A to B? Can be solved as a constrained minimization problem Leads to Snell’s law of refraction. Speed of light = c2 Speed of light = c1 Lifeguard’s swimming speed = c2 Lifeguard’s running speed = c1 A B A B

Slide 2a.4 Stiff Structures, Compliant Mechanisms, and MEMS: A short course offered at IISc, Bangalore, India. Aug.-Sep., G. K. Ananthasuresh Brachistochrone (minimum time) problem x Y=f(x) The bead slides along a wire under the action of gravity. g A B What shape of the wire (i.e., what f(x)) will lead to the minimum descent time for the bead? Posed as a challenge by Johann Bernoulli. Solved by Leibnitz, Newton, L’Hospital, and Jacob Bernoulli… Functional

Slide 2a.5 Stiff Structures, Compliant Mechanisms, and MEMS: A short course offered at IISc, Bangalore, India. Aug.-Sep., G. K. Ananthasuresh Unconstrained minimization Necessary condition: Sufficient condition:is positive definite Gradient Hessian i.e., If is a solution… ( equations)

Slide 2a.6 Stiff Structures, Compliant Mechanisms, and MEMS: A short course offered at IISc, Bangalore, India. Aug.-Sep., G. K. Ananthasuresh Equality constrained minimization Necessary condition: Sufficient condition: If is a solution… Define a Lagrangian, ( equations) satisfying ( equations) and Lagrange multiplier(s)

Slide 2a.7 Stiff Structures, Compliant Mechanisms, and MEMS: A short course offered at IISc, Bangalore, India. Aug.-Sep., G. K. Ananthasuresh General constrained minimization Define a Lagrangian, If is a solution… An inequality constraint can be active (= sign) or inactive (<sign). Karush-Kuhn-Tucker (KKT) necessary conditions Complementarity conditions A Beautiful Mind

Slide 2a.8 Stiff Structures, Compliant Mechanisms, and MEMS: A short course offered at IISc, Bangalore, India. Aug.-Sep., G. K. Ananthasuresh Solution to Fermat/Feynman’s minimum-time problem speed = c2 speed = c1 A B & Snell’s law

Slide 2a.9 Stiff Structures, Compliant Mechanisms, and MEMS: A short course offered at IISc, Bangalore, India. Aug.-Sep., G. K. Ananthasuresh Return to brachistochrone problem What is different now? The unknown is a function. The objective is a function of the unknown function and its derivative(s). First variation Operationally useful definition:

Slide 2a.10 Stiff Structures, Compliant Mechanisms, and MEMS: A short course offered at IISc, Bangalore, India. Aug.-Sep., G. K. Ananthasuresh Fundamental lemma of calculus of variations for any then If

Slide 2a.11 Stiff Structures, Compliant Mechanisms, and MEMS: A short course offered at IISc, Bangalore, India. Aug.-Sep., G. K. Ananthasuresh Now, it is only integration by parts… Necessary condition for a minimum: Consider Boundary conditions Euler-Lagrange necessary conditions By the fundamental lemma

Slide 2a.12 Stiff Structures, Compliant Mechanisms, and MEMS: A short course offered at IISc, Bangalore, India. Aug.-Sep., G. K. Ananthasuresh Extensions Second variations; sufficient conditions –Refer to any standard text, e.g., Gelfand and Fomin. To multiple derivatives of –Simply integrate by parts as many times as necessary and collect the boundary terms carefully. To multiple unknown functions, i.e., –Straightforward; write the same set of equations for each. To multiple independent variables, i.e., –Need to use the divergence theorem instead of integrating by parts.

Slide 2a.13 Stiff Structures, Compliant Mechanisms, and MEMS: A short course offered at IISc, Bangalore, India. Aug.-Sep., G. K. Ananthasuresh Constrained variational calculus problems Integral (global) constraint Differential (local) constraint What is the Lagrangian now? Single scalar variable Scalar valued function

Slide 2a.14 Stiff Structures, Compliant Mechanisms, and MEMS: A short course offered at IISc, Bangalore, India. Aug.-Sep., G. K. Ananthasuresh Main points KKT necessary conditions for constrained minimization Euler-Lagrange necessary conditions for a functional The KKT conditions can be used for variational calculus problems as well.