DOCSLIB.ORG

ECON 301 Two Variable Optimization (With- and Without- Constraints)

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
ECON 301 Two Variable Optimization (With- and Without- Constraints) ECON 301 Two Variable Optimization (with- and without- constraints) Review of Some Derivative Rules 1. Partial Derivative Rules: U = xy ∂U/∂x = Ux = y ∂U/∂y = Uy = x a b a 1 b a b 1 U = x y ∂U/∂x = Ux = ax − y ∂U/∂y = Uy = bx y − a b xa a 1 b a b 1 U = x y− = b ∂U/∂x = Ux = ax − y− ∂U/∂y = Uy = bx y− − y − U = ax + by ∂U/∂x = Ux = a ∂U/∂y = Uy = b 1/2 1/2 1 1/2 1 1/2 U = ax + by ∂U/∂x = Ux = a 2 x− ∂U/∂y = Uy = b 2 y− 2. Logarithm (Natural log) ln x ¡ ¢ ¡ ¢ (a) Rules of natural log If Then y = AB ln y =ln(AB)=lnA +lnB y = A/B ln y =lnA ln B y = Ab ln y =ln(Ab−)=b ln A NOTE: ln(A + B) =lnA +lnB 6 (b) derivatives IF THEN dy 1 y =lnx dx = x dy 1 y =ln(f(x)) = f 0(x) dx f(x) · (c) Examples If Then 2 1 y =ln(x 2x) dy/dx = (x2 2x) (2x 2) 1/−2 1 1 − 1 −1 y =ln(x )= 2 ln xdy/dx= 2 x = 2x ¡ ¢¡ ¢ 3. The Number e dy if y = ex then = ex dx f(x) dy f(x) if y = e then = e f 0(x) dx · (a) Examples 3x dy 3x y = e dx = e (3) 7x3 dy 7x3 2 y = e dx = e (21x ) rt dy rt y = e dt = re 1 Using Calculus For Maximization Problems OneVariableCase If we have the following function y =10x x2 − we have an example of a dome shaped function. To find the maximum of the dome, we simply need to findthepointwheretheslopeofthedomeiszero,or dy dx =10 2x =0 10 =− 2x x =5 and y =25 Two Variable Case Suppose we want to maximize the following function z = f(x, y)=10x +10y + xy x2 y2 − − Notethattherearetwounknownsthatmustbesolvedfor:x and y. This function is an example of a three-dimensional dome. (i.e. the roof of BC Place) To solve this maximization problem we use partial derivatives. We take a partial derivative for each of the unknown choice variables and set them equal to zero ∂z ∂x = fx =10+y 2x =0 The slope in the ”x” direction = 0 ∂z − = fy =10+x 2y =0 The slope in the ”y” direction = 0 ∂y − This gives us a set of equations, one equation for each of the unknown variables. When you have the same number of independent equations as unknowns, you can solve for each of the unknowns. rewrite each equation as y =2x 10 − x =2y 10 − substitute one into the other x =2(2x 10) 10 − − x =4x 30 − 3x =30 x =10 similarly, y =10 2 REMEMBER: To maximize (minimize) a function of many variables you use the technique of partial differentiation. This produces a set of equations, one equation for each of the unknowns. You then solve the set of equations simulaneously to derive solutions for each of the unknowns. Second order Conditions (second derivative Test) To test for a maximum or minimum we need to check the second partial derivatives. Since we have two first partial derivative equations (fx,fy) and two variable in each equation, we will get four second partials ( fxx,fyy,fxy,fyx) Using our original first order equations and taking the partial derivatives for each of them (a second time) yields: fx =10+y 2x =0 fy =10+x 2y =0 − − fxx = 2 fyy = 2 − − fxy =1 fyx =1 The two partials,fxx,andfyy are the direct effects of of a small change in x and y on the respective slopes in in the x and y direction. The partials, fxy and fyx are the indirect effects, or the cross effects of one variable on the slope in the other variable’s direction. For both Maximums and Minimums, the direct effects must outweigh the cross effects Rules for two variable Maximums and Minimums1 1. Maximum fxx < 0 fyy < 0 fyyfxx fxyfyx > 0 − 2. Minimum fxx > 0 fyy > 0 fyyfxx fxyfyx > 0 − 3. Otherwise, we have a Saddle Point From our second order conditions, above, fxx = 2 < 0 fyy = 2 < 0 − − fxy =1 fyx =1 and fyyfxx fxyfyx =( 2)( 2) (1)(1) = 3 > 0 − − − − therefore we have a maximum. 1 Advanced Topic: This section is optional for ECON 301. For most applications, the structure of the problem will make it clear that this is a max or min problem 3 Example: Profit Max Capital and Labour Suppose we have the following production function q = Output 1 1 q = f(K, L)=L 2 + K 2 L = Labour K = Capital Then the profit function for a competitive firm is π = Pq wL rK P = Market Price or− − w = Wage Rate 1 1 π = PL2 + PK2 wL rK r = Rental Rate − − First order conditions General Form 1 ∂π P −2 1. ∂L = 2 L w =0 PfL w =0 1 ∂π P − − − 2. = K 2 r =0 PfK r =0 ∂k 2 − − Solving (1) and (2), we get 2w 2 2r 2 L∗ =(P )− K∗ =(P )− Example: If P =1000, w =20,andr =10 1. Find the optimal K, L,andπ 2. Check second order conditions Example: Cobb-Douglas production function and a com- petitive firm Consider a competitive firm with the following profitfunction π = TR TC = PQ wL rK (1) − − − where P is price, Q is output, L is labour and K is capital, and w and r are the input prices for L and K respectively. Since the firm operates in a competitive market, the exogenous variables are P,w and r. There are three endogenous variables, K, L and Q. However output, Q, is in turn a function of K and L via the production function Q = f(K, L) which in this case, is the Cobb-Douglas function Q = LaKb (2) 4 where a and b are positive parameters. If we further assume decreasing returns to scale, 1 then a + b < 1. For simplicity, let’s consider the symmetric case where a = b = 4 1 1 Q = L 4 K 4 (3) Substituting Equation 3 into Equation 1 gives us 1 1 π(K, L)=PL4 K 4 wL rK (4) − − The first order conditions are 3 1 ∂π 1 4 4 ∂L = P 4 L− K w =0 ∂π 1 1 3 − (5) = P L 4 K− 4 r =0 ∂K ¡ 4¢ − This system of equations define the optimal¡ ¢ L and K for profit maximization. Rewriting the firstequationinEquation5toisolateK 3 1 1 4 4 P 4 L− K = w 4w 3 4 K =( L 4 ) ¡ ¢ p Substituting into the second equation of Equation 5 3 4 1 3 1 3 4 − P 4 4 P 4 4w 4 4 L K− = 4 L p L = r 4 1 4 3 2 ∙³ ´ ¸ = P 4 w−¡ L¢− = r Re-arranging to get L by itself¡ ¢ gives us P 3 1 2 L =( w 4 r 4 ) ∗ 4 − − Taking advantage of the symmetry of the model, we can quickly find the optimal K P 3 1 2 K =( r 4 w 4 ) ∗ 4 − − L∗ and K∗ are the firm’s factor demand equations. Optimization with Constraints The Lagrange Multiplier Method Sometimes we need to to maximize (minimize) a function that is subject to some sort of constraint. For example Maximize z = f(x, y) subject to the constraint x + y 100 ≤ Forthiskindofproblemthereisatechnique,ortrick, developed for this kind of problem known as the Lagrange Multiplier method. This method involves adding an extra variable to the problem called the lagrange multiplier, or λ. We then set up the problem as follows: 5 1. Create a new equation form the original information L = f(x, y)+λ(100 x y) or − − L = f(x, y)+λ [Zero] 2. Then follow the same steps as used in a regular maximization problem ∂L ∂x = fx λ =0 ∂L − = fy λ =0 ∂y − ∂L =100 x y =0 ∂λ − − 3. In most cases the λ will drop out with substitution. Solving these 3 equations will give you the constrained maximum solution Example 1: Suppose z = f(x, y)=xy. and the constraint is the one from above. The problem then becomes L = xy + λ(100 x y) − − Now take partial derivatives, one for each unknown, including λ ∂L ∂x = y λ =0 ∂L = x − λ =0 ∂y − ∂L =100 x y =0 ∂λ − − Starting with the first two equations, we see that x = y and λ drops out. From the third equation we can easily find that x = y =50and the constrained maximum value for z is z = xy =2500. Example 2: Maximize u =4x2 +3xy +6y2 subject to x + y =56 Set up the Lagrangian Equation: L =4x2 +3xy +6y2 + λ(56 x y) − − Take the first-order partials and set them to zero Lx =8x +3y λ =0 − Ly =3x +12y λ =0 − Lλ =56 x y =0 − − 6 From the first two equations we get 8x +3y =3x +12y x =1.8y Substitute this result into the third equation 56 1.8y y =0 − − y =20 therefore x =36 λ =348 Example 3: Cost minimization A firm produces two goods, x and y. Due to a government quota, the firm must produce subject to the constraint x + y =42.Thefirm’s cost functions is c(x, y)=8x2 xy +12y2 − The Lagrangian is L =8x2 xy +12y2 + λ(42 x y) − − − The first order conditions are Lx =16x y λ =0 − − Ly = x +24y λ =0 − − Lλ =42 x y =0 (6) − − Solving these three equations simultaneously yields x =25 y =17 λ =383 Example of duality for the consumer choice problem Example 4: Utility Maximization Consider a consumer with the utility function U = xy, who faces a budget constraint of B = Pxx + Pyy, where B, Px and Py are the budget and prices, which are given.
Load more

Recommended publications
  • Introduction to the Modern Calculus of Variations
    MA4G6 Lecture Notes Introduction to the Modern Calculus of Variations Filip Rindler Spring Term 2015 Filip Rindler Mathematics Institute University of Warwick Coventry CV4 7AL United Kingdom [email protected] http://www.warwick.ac.uk/filiprindler Copyright ©2015 Filip Rindler. Version 1.1. Preface These lecture notes, written for the MA4G6 Calculus of Variations course at the University of Warwick, intend to give a modern introduction to the Calculus of Variations. I have tried to cover different aspects of the field and to explain how they fit into the “big picture”. This is not an encyclopedic work; many important results are omitted and sometimes I only present a special case of a more general theorem. I have, however, tried to strike a balance between a pure introduction and a text that can be used for later revision of forgotten material. The presentation is based around a few principles: • The presentation is quite “modern” in that I use several techniques which are perhaps not usually found in an introductory text or that have only recently been developed. • For most results, I try to use “reasonable” assumptions, not necessarily minimal ones. • When presented with a choice of how to prove a result, I have usually preferred the (in my opinion) most conceptually clear approach over more “elementary” ones. For example, I use Young measures in many instances, even though this comes at the expense of a higher initial burden of abstract theory. • Wherever possible, I first present an abstract result for general functionals defined on Banach spaces to illustrate the general structure of a certain result.
    [Show full text]
  • Finite Elements for the Treatment of the Inextensibility Constraint
    Comput Mech DOI 10.1007/s00466-017-1437-9 ORIGINAL PAPER Fiber-reinforced materials: finite elements for the treatment of the inextensibility constraint Ferdinando Auricchio1 · Giulia Scalet1 · Peter Wriggers2 Received: 5 April 2017 / Accepted: 29 May 2017 © Springer-Verlag GmbH Germany 2017 Abstract The present paper proposes a numerical frame- 1 Introduction work for the analysis of problems involving fiber-reinforced anisotropic materials. Specifically, isotropic linear elastic The use of fiber-reinforcements for the development of novel solids, reinforced by a single family of inextensible fibers, and efficient materials can be traced back to the 1960s are considered. The kinematic constraint equation of inex- and, to date, it is an active research topic. Such materi- tensibility in the fiber direction leads to the presence of als are composed of a matrix, reinforced by one or more an undetermined fiber stress in the constitutive equations. families of fibers which are systematically arranged in the To avoid locking-phenomena in the numerical solution due matrix itself. The interest towards fiber-reinforced mate- to the presence of the constraint, mixed finite elements rials stems from the mechanical properties of the fibers, based on the Lagrange multiplier, perturbed Lagrangian, and which offer a significant increase in structural efficiency. penalty method are proposed. Several boundary-value prob- Particularly, these materials possess high resistance and lems under plane strain conditions are solved and numerical stiffness, despite the low specific weight, together with an results are compared to analytical solutions, whenever the anisotropic mechanical behavior determined by the fiber derivation is possible. The performed simulations allow to distribution.
    [Show full text]
  • A Variational Approach to Lagrange Multipliers
    JOTA manuscript No. (will be inserted by the editor) A Variational Approach to Lagrange Multipliers Jonathan M. Borwein · Qiji J. Zhu Received: date / Accepted: date Abstract We discuss Lagrange multiplier rules from a variational perspective. This allows us to highlight many of the issues involved and also to illustrate how broadly an abstract version can be applied. Keywords Lagrange multiplier Variational method Convex duality Constrained optimiza- tion Nonsmooth analysis · · · · Mathematics Subject Classification (2000) 90C25 90C46 49N15 · · 1 Introduction The Lagrange multiplier method is fundamental in dealing with constrained optimization prob- lems and is also related to many other important results. There are many different routes to reaching the fundamental result. The variational approach used in [1] provides a deep under- standing of the nature of the Lagrange multiplier rule and is the focus of this survey. David Gale's seminal paper [2] provides a penetrating explanation of the economic meaning of the Lagrange multiplier in the convex case. Consider maximizing the output of an economy with resource constraints. Then the optimal output is a function of the level of resources. It turns out the derivative of this function, if exists, is exactly the Lagrange multiplier for the constrained optimization problem. A Lagrange multiplier, then, reflects the marginal gain of the output function with respect to the vector of resource constraints. Following this observation, if we penalize the resource utilization with a (vector) Lagrange multiplier then the constrained optimization problem can be converted to an unconstrained one. One cannot emphasize enough the importance of this insight. In general, however, an optimal value function for a constrained optimization problem is nei- ther convex nor smooth.
    [Show full text]
  • Derivative, Gradient, and Lagrange Multipliers
    EE263 Prof. S. Boyd Derivative, Gradient, and Lagrange Multipliers Derivative Suppose f : Rn → Rm is differentiable. Its derivative or Jacobian at a point x ∈ Rn is × denoted Df(x) ∈ Rm n, defined as ∂fi (Df(x))ij = , i =1, . , m, j =1,...,n. ∂x j x The first order Taylor expansion of f at (or near) x is given by fˆ(y)= f(x)+ Df(x)(y − x). When y − x is small, f(y) − fˆ(y) is very small. This is called the linearization of f at (or near) x. As an example, consider n = 3, m = 2, with 2 x1 − x f(x)= 2 . x1x3 Its derivative at the point x is 1 −2x2 0 Df(x)= , x3 0 x1 and its first order Taylor expansion near x = (1, 0, −1) is given by 1 1 1 0 0 fˆ(y)= + y − 0 . −1 −1 0 1 −1 Gradient For f : Rn → R, the gradient at x ∈ Rn is denoted ∇f(x) ∈ Rn, and it is defined as ∇f(x)= Df(x)T , the transpose of the derivative. In terms of partial derivatives, we have ∂f ∇f(x)i = , i =1,...,n. ∂x i x The first order Taylor expansion of f at x is given by fˆ(x)= f(x)+ ∇f(x)T (y − x). 1 Gradient of affine and quadratic functions You can check the formulas below by working out the partial derivatives. For f affine, i.e., f(x)= aT x + b, we have ∇f(x)= a (independent of x). × For f a quadratic form, i.e., f(x)= xT Px with P ∈ Rn n, we have ∇f(x)=(P + P T )x.
    [Show full text]
  • Contactmethodsintegratingplasti
    Contact methodsintegrating plasticity B13/1 modelswith application to soil mechanics ChristianWeißenfels Leibniz Universität Hannover Contact methods integrating plasticity models with application to soil mechanics Von der Fakultät für Maschinenbau der Gottfried Wilhelm Leibniz Universität Hannover zur Erlangung des akademischen Grades Doktor-Ingenieur genehmigte Dissertation von Dipl.-Ing. Christian Weißenfels geboren am 30.01.1979 in Rosenheim 2013 Herausgeber: Prof. Dr.-Ing. Peter Wriggers Verwaltung: Institut für Kontinuumsmechanik Gottfried Wilhelm Leibniz Universität Hannover Appelstraße 11 30167 Hannover Tel: +49 511 762 3220 Fax: +49 511 762 5496 Web: www.ikm.uni-hannover.de © Dipl.-Ing. Christian Weißenfels Institut für Kontinuumsmechanik Gottfried Wilhelm Leibniz Universität Hannover Appelstraße 11 30167 Hannover Alle Rechte, insbesondere das der Übersetzung in fremde Sprachen, vorbehalten. Ohne Genehmigung des Autors ist es nicht gestattet, dieses Heft ganz oder teilweise auf photomechanischem, elektronischem oder sonstigem Wege zu vervielfältigen. ISBN 978-3-941302-06-8 1. Referent: Prof. Dr.-Ing. Peter Wriggers 2. Referent: Prof. Dr.-Ing. Karl Schweizerhof Tag der Promotion: 13.12.2012 i Zusammenfassung Der Schwerpunkt der vorliegenden Arbeit liegt in der Entwicklung von neuartigen Kon- zepten zur direkten Integration von drei-dimensionalen Plastizit¨atsmodellen in eine Kontaktformulierung. Die allgemeinen Konzepte wurden speziell auf Boden Bauwerk Interaktionen angewandt und im Rahmen der Mortar-Methode numerisch umgesetzt. Zwei unterschiedliche Strategien wurden dabei verfolgt. Die Erste integriert die Boden- modelle in eine Standard-Kontaktdiskretisierung, wobei die zweite Variante die Kon- taktformulierung in Richtung eines drei-dimensionalen Kontaktelements erweitert. Innerhalb der ersten Variante wurden zwei unterschiedliche Konzepte ausgearbeitet, wobei das erste Konzept das Bodenmodell mit dem Reibbeiwert koppelt. Dabei muss die H¨ohe der Lokalisierungszone entlang der Kontaktfl¨ache bestimmt werden.
    [Show full text]
  • Dual-Dual Formulations for Frictional Contact Problems in Mechanics
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Institutionelles Repositorium der Leibniz Universität... Dual-dual formulations for frictional contact problems in mechanics Von der Fakultät für Mathematik und Physik der Gottfried Wilhelm Leibniz Universität Hannover zur Erlangung des Grades Doktor der Naturwissenschaften Dr. rer. nat. genehmigte Dissertation von Dipl. Math. Michael Andres geboren am 28. 12. 1980 in Siemianowice (Polen) 2011 Referent: Prof. Dr. Ernst P. Stephan, Leibniz Universität Hannover Korreferent: Prof. Dr. Gabriel N. Gatica, Universidad de Concepción, Chile Korreferent: PD Dr. Matthias Maischak, Brunel University, Uxbridge, UK Tag der Promotion: 17. 12. 2010 ii To my Mum. Abstract This thesis deals with unilateral contact problems with Coulomb friction. The main focus of this work lies on the derivation of the dual-dual formulation for a frictional contact problem. First, we regard the complementary energy minimization problem and apply Fenchel’s duality theory. The result is a saddle point formulation of dual type involving Lagrange multipliers for the governing equation, the symmetry of the stress tensor as well as the boundary conditions on the Neumann boundary and the contact boundary, respectively. For the saddle point problem an equivalent variational inequality problem is presented. Both formulations include a nondiffer- entiable functional arising from the frictional boundary condition. Therefore, we introduce an additional dual Lagrange multiplier denoting the friction force. This procedure yields a dual-dual formulation of a two-fold saddle point structure. For the corresponding variational inequality problem we show the unique solvability. Two different inf-sup conditions are introduced that allow an a priori error analysis of the dual-dual variational inequality problem.
    [Show full text]
  • 18.02SC Notes: Lagrange Multipliers
    Lagrange Multipliers We will give the argument for why Lagrange multipliers work later. Here, we’ll look at where and how to use them. Lagrange multipliers are used to solve constrained optimization problems. That is, suppose you have a function, say f(x; y), for which you want to find the maximum or minimum value. But, you are not allowed to consider all (x; y) while you look for this value. Instead, the (x; y) you can consider are constrained to lie on some curve or surface. There are lots of examples of this in science, engineering and economics, for example, optimizing some utility function under budget constraints. Lagrange multipliers problem: Minimize (or maximize) w = f(x; y; z) constrained by g(x; y; z) = c. Lagrange multipliers solution: Local minima (or maxima) must occur at a critical point. This is a point where rf = λrg, and g(x; y; z) = c. Example: Making a box using a minimum amount of material. A box is made of cardboard with double thick sides, a triple thick bottom, single thick front and back and no top. It’s volume is fixed at 3. What dimensions use the least amount of cardboard? Answer: We did this problem once before by solving for z in terms of x and y and substi­ tuting for it. That led to an unconstrained optimization problem in x and y. Here we will do it as a constrained problem. It is important to be able to do this because eliminating one variable is not always easy. The box shown has dimensions x, y, and z.
    [Show full text]
  • Math 241: Multivariable Calculus, Lecture 15 Lagrange Multipliers
    Math 241: Multivariable calculus, Lecture 15 Lagrange Multipliers. go.illinois.edu/math241fa17 Wednesday, October 4th, 2017 go.illinois.edu/math241fa17. Math 241: Problem of the day Problem: Find the absolute maximum and minimum value of the function f (x; y) = x3 − x + y 2x + y + 2 on the unit disk D = f(x; y) j x2 + y 2 ≤ 1g. What guarantees that the max and min exist? go.illinois.edu/math241fa17. rf (x; y) = h3x2 − 1 + y 2; 2yx + 1i −1 If this is the zero vector we get 2xy + 1 = 0, or y = 2x . (Note that x nor y can be zero, so dividing by x or y is no problem.) Substitute in the first component to get 1 1 3x2 − 1 + = 0 =) 3x4 − x2 + = 0: 4x2 4 2 2 1 Put z = x , get 3z − z + 4 = 0. The discriminant b2 − 4ac = 1 − 3 = −2, so there are NO critical points. So we need to look at the boundary! Solution Part 1: Critical points If f (x; y) = x3 − x + y 2x + y + 2 the gradient vector is go.illinois.edu/math241fa17. −1 If this is the zero vector we get 2xy + 1 = 0, or y = 2x . (Note that x nor y can be zero, so dividing by x or y is no problem.) Substitute in the first component to get 1 1 3x2 − 1 + = 0 =) 3x4 − x2 + = 0: 4x2 4 2 2 1 Put z = x , get 3z − z + 4 = 0. The discriminant b2 − 4ac = 1 − 3 = −2, so there are NO critical points. So we need to look at the boundary! Solution Part 1: Critical points If f (x; y) = x3 − x + y 2x + y + 2 the gradient vector is rf (x; y) = h3x2 − 1 + y 2; 2yx + 1i go.illinois.edu/math241fa17.
    [Show full text]
  • Notes on Calculus and Optimization
    Economics 101A Section Notes GSI: David Albouy Notes on Calculus and Optimization 1 Basic Calculus 1.1 Definition of a Derivative Let f (x) be some function of x, then the derivative of f, if it exists, is given by the following limit df (x) f (x + h) f (x) = lim − (Definition of Derivative) dx h 0 h → df although often this definition is hard to apply directly. It is common to write f 0 (x),or dx to be shorter, or dy if y = f (x) then dx for the derivative of y with respect to x. 1.2 Calculus Rules Here are some handy formulas which can be derived using the definitions, where f (x) and g (x) are functions of x and k is a constant which does not depend on x. d df (x) [kf (x)] = k (Constant Rule) dx dx d df (x) df (x) [f (x)+g (x)] = + (Sum Rule) dx dx dy d df (x) dg (x) [f (x) g (x)] = g (x)+f (x) (Product Rule) dx dx dx d f (x) df (x) g (x) dg(x) f (x) = dx − dx (Quotient Rule) dx g (x) [g (x)]2 · ¸ d f [g (x)] dg (x) f [g (x)] = (Chain Rule) dx dg dx For specific forms of f the following rules are useful in economics d xk = kxk 1 (Power Rule) dx − d ex = ex (Exponent Rule) dx d 1 ln x = (Logarithm Rule) dx x 1 Finally assuming that we can invert y = f (x) by solving for x in terms of y so that x = f − (y) then the following rule applies 1 df − (y) 1 = 1 (Inverse Rule) dy df (f − (y)) dx Example 1 Let y = f (x)=ex/2, then using the exponent rule and the chain rule, where g (x)=x/2,we df (x) d x/2 d x/2 d x x/2 1 1 x/2 get dx = dx e = d(x/2) e dx 2 = e 2 = 2 e .
    [Show full text]
  • Unit #23 : Lagrange Multipliers Goals: • to Study Constrained Optimization
    Unit #23 : Lagrange Multipliers Goals: • To study constrained optimization; that is, the maximizing or minimizing of a function subject to a constraint (or side condition). Constrained Optimization - Examples - 1 In the previous section, we saw some of the difficulties of working with optimization when there are multiple variables. Many of those problems can be cast into an important class of problems called constrained optimization problems, which can be solved in an alternative way. Examples of Problems with Constraints 1. Show that the rectangle of maximum area that has a given perimeter p is a square. The function to be maximized: A(x; y) = xy The constraint: 2x + 2y = p Constrained Optimization - Examples - 2 2. Find the point on the sphere x2 + y2 + z2 = 4 that is closest to the point (3; 3; 5). Here we want to minimize the distance D(x; y; z) = p(x − 3)2 + (y − 3)2 + (z − 5)2 subject to the constraint x2 + y2 + z2 = 4 Lagrange Multipliers - 1 Lagrange Multipliers To solve optimization problems when we have constraints on our choice of x and y, we can use the method of Lagrange multipliers. Suppose we want to maximize the function f(x; y) subject to the constraint g(x; y) = k. We consider the relative positions of some sample level curves on the next page. Lagrange Multipliers - 2 Black lines: contours of f(x; y). Blue lines: the constraint g(x; y) = k. f = 10 f = 20 f = 30 g(x,y)= k f = 40 f = 50 f = 60 On the diagram, locate the maximum and minimum of f(x; y) (label with M and m respectively) on the level curve g(x; y) = k.
    [Show full text]
  • A Global Divergence Conforming DG Method for Hyperbolic Conservation Laws with Divergence Constraint
    A global divergence conforming DG method for hyperbolic conservation laws with divergence constraint Praveen Chandrashekar∗ Abstract We propose a globally divergence conforming discontinuous Galerkin (DG) method on Cartesian meshes for curl-type hyperbolic conservation laws based on directly evolving the face and cell moments of the Raviart-Thomas approximation polyno- mials. The face moments are evolved using a 1-D discontinuous Gakerkin method that uses 1-D and multi-dimensional Riemann solvers while the cell moments are evolved using a standard 2-D DG scheme that uses 1-D Riemann solvers. The scheme can be implemented in a local manner without the need to solve a global mass matrix which makes it a truly DG method and hence useful for explicit time stepping schemes for hyperbolic problems. The scheme is also shown to exactly pre- serve the divergence of the vector field at the discrete level. Numerical results using second and third order schemes for induction equation are presented to demonstrate the stability, accuracy and divergence preservation property of the scheme. Keywords: Hyperbolic conservation laws; curl-type equations; discontinuous Galerkin; constraint-preserving; divergence-free; induction equation. 1 Introduction Constraint-preserving approximations are important in the numerical simulation of prob- lems in computational electrodynamics (CED) and magnetohydrodynamics (MHD). The time domain Maxwell equations used in CED for the electric and magnetic fields may be written in non-dimensional units as @E @B B = J; + E = 0 @t − r × − @t r × with the constraint that @ρ B = 0; E = ρ, + J = 0 r · r · @t r · where ρ is the electric charge density and J is the current which can be related to E, arXiv:1809.03294v1 [math.NA] 10 Sep 2018 B through Ohm's Law.
    [Show full text]
  • Calculus III - Lagrange Multipliers
    Calculus III - Lagrange Multipliers http://tutorial.math.lamar.edu/Classes/CalcIII/LagrangeMultipliers.aspx Paul's Online Math Notes Calculus III (Notes) / Applications of Partial Derivatives / Lagrange Multipliers [Notes][Practice Problems][Assignment Problems] Calculus III - Notes Lagrange Multipliers In the previous section we optimized (i.e. found the absolute extrema) a function on a region that contained its boundary. Finding potential optimal points in the interior of the region isn’t too bad in general, all that we needed to do was find the critical points and plug them into the function. However, as we saw in the examples finding potential optimal points on the boundary was often a fairly long and messy process. In this section we are going to take a look at another way of optimizing a function subject to given constraint(s). The constraint(s) may be the equation(s) that describe the boundary of a region although in this section we won’t concentrate on those types of problems since this method just requires a general constraint and doesn’t really care where the constraint came from. So, let’s get things set up. We want to optimize (i.e. find the minimum and maximum value of) a function, , subject to the constraint . Again, the constraint may be the equation that describes the boundary of a region or it may not be. The process is actually fairly simple, although the work can still be a little overwhelming at times. Method of Lagrange Multipliers 1. Solve the following system of equations. 2. Plug in all solutions, , from the first step into and identify the minimum and maximum values, provided they exist.
    [Show full text]