Mathematical Foundations -1- Convexity and quasi-convexity. Convex set Convex function Concave function Quasi-concave function Supporting hyperplane

Similar documents
Mathematical Foundations -1- Supporting hyperplanes. SUPPORTING HYPERPLANES Key Ideas: Bounding hyperplane for a convex set, supporting hyperplane

14 March 2018 Module 1: Marginal analysis and single variable calculus John Riley. ( x, f ( x )) are the convex combinations of these two

1.4 FOUNDATIONS OF CONSTRAINED OPTIMIZATION

Mathematical Foundations II

Mathematical Foundations -1- Constrained Optimization. Constrained Optimization. An intuitive approach 2. First Order Conditions (FOC) 7

CHAPTER 2: CONVEX SETS AND CONCAVE FUNCTIONS. W. Erwin Diewert January 31, 2008.

Seminars on Mathematics for Economics and Finance Topic 5: Optimization Kuhn-Tucker conditions for problems with inequality constraints 1

Division of the Humanities and Social Sciences. Supergradients. KC Border Fall 2001 v ::15.45

Lecture 4: Optimization. Maximizing a function of a single variable

1 Convexity, concavity and quasi-concavity. (SB )

Solutions to Exercises, Section 2.5

. This matrix is not symmetric. Example. Suppose A =

Chapter 1 - Preference and choice

Mathematical Economics. Lecture Notes (in extracts)

Microeconomics I. September, c Leopold Sögner

Lesson 59 Rolle s Theorem and the Mean Value Theorem

Static Problem Set 2 Solutions

Summary Notes on Maximization

2019 Spring MATH2060A Mathematical Analysis II 1

Duality. for The New Palgrave Dictionary of Economics, 2nd ed. Lawrence E. Blume

September Math Course: First Order Derivative

5. Find the slope intercept equation of the line parallel to y = 3x + 1 through the point (4, 5).

CHAPTER 1-2: SHADOW PRICES

Trigonometrical identities and inequalities

5.1 Increasing and Decreasing Functions. A function f is decreasing on an interval I if and only if: for all x 1, x 2 I, x 1 < x 2 = f(x 1 ) > f(x 2 )

Convex Functions and Optimization

EC /11. Math for Microeconomics September Course, Part II Lecture Notes. Course Outline

1. f(β) 0 (that is, β is a feasible point for the constraints)

Math-2A Lesson 13-3 (Analyzing Functions, Systems of Equations and Inequalities) Which functions are symmetric about the y-axis?

Preliminary draft only: please check for final version

Student Study Session. Theorems

DIFFERENTIATION RULES

Basic mathematics of economic models. 3. Maximization

Convex Analysis and Economic Theory AY Elementary properties of convex functions

EC487 Advanced Microeconomics, Part I: Lecture 2

ARE202A, Fall 2005 CONTENTS. 1. Graphical Overview of Optimization Theory (cont) Separating Hyperplanes 1

ARE211, Fall 2005 CONTENTS. 5. Characteristics of Functions Surjective, Injective and Bijective functions. 5.2.

2-7 Solving Absolute-Value Inequalities

Essential Microeconomics : EQUILIBRIUM AND EFFICIENCY WITH PRODUCTION Key ideas: Walrasian equilibrium, first and second welfare theorems

Session: 09 Aug 2016 (Tue), 10:00am 1:00pm; 10 Aug 2016 (Wed), 10:00am 1:00pm &3:00pm 5:00pm (Revision)

Economics 205 Exercises

The Fundamental Welfare Theorems

Mathematical Preliminaries for Microeconomics: Exercises

Some Properties of the Augmented Lagrangian in Cone Constrained Optimization

Math 10b Ch. 8 Reading 1: Introduction to Taylor Polynomials

Economics 101A (Lecture 3) Stefano DellaVigna

CHAPTER 3: OPTIMIZATION

Maximum and Minimum Values (4.2)

Chapter 4: Production Theory

8. Constrained Optimization

Week 4: Calculus and Optimization (Jehle and Reny, Chapter A2)

Constrained Optimization and Lagrangian Duality

II. An Application of Derivatives: Optimization

AP Calculus Worksheet: Chapter 2 Review Part I

USING THE QUADRATIC FORMULA and 9.1.3

Properties of Walrasian Demand

Second Welfare Theorem

ARE211, Fall2015. Contents. 2. Univariate and Multivariate Differentiation (cont) Taylor s Theorem (cont) 2

Mathematical Economics: Lecture 16

A2 HW Imaginary Numbers

Calculus 2502A - Advanced Calculus I Fall : Local minima and maxima

Test 3 Review. y f(a) = f (a)(x a) y = f (a)(x a) + f(a) L(x) = f (a)(x a) + f(a)

3. THE EXCHANGE ECONOMY

Hicksian Demand and Expenditure Function Duality, Slutsky Equation

Optimization. A first course on mathematics for economists

Minimizing Cubic and Homogeneous Polynomials over Integers in the Plane

EconS 301. Math Review. Math Concepts

Chapter 8: Taylor s theorem and L Hospital s rule

Optimality Conditions for Constrained Optimization

Chapter 4: More Applications of Differentiation

Lecture 4 September 15

The general programming problem is the nonlinear programming problem where a given function is maximized subject to a set of inequality constraints.

UNDERGROUND LECTURE NOTES 1: Optimality Conditions for Constrained Optimization Problems

Differentiable Welfare Theorems Existence of a Competitive Equilibrium: Preliminaries

Consistent and Dependent

Maximum likelihood estimation

Mathematic 108, Fall 2015: Solutions to assignment #7

Econ 508-A FINITE DIMENSIONAL OPTIMIZATION - NECESSARY CONDITIONS. Carmen Astorne-Figari Washington University in St. Louis.

4. Convex Sets and (Quasi-)Concave Functions

x x implies that f x f x.

The Envelope Theorem

In view of (31), the second of these is equal to the identity I on E m, while this, in view of (30), implies that the first can be written

Module 7 : Applications of Integration - I. Lecture 20 : Definition of the power function and logarithmic function with positive base [Section 20.

2.4 Graphing Inequalities

Convex Analysis and Economic Theory Winter 2018

56 CHAPTER 3. POLYNOMIAL FUNCTIONS

PROFIT FUNCTIONS. 1. REPRESENTATION OF TECHNOLOGY 1.1. Technology Sets. The technology set for a given production process is defined as

Homework 5 Solutions

Differentiation - Important Theorems

EC9A0: Pre-sessional Advanced Mathematics Course. Lecture Notes: Unconstrained Optimisation By Pablo F. Beker 1

1 Overview. 2 A Characterization of Convex Functions. 2.1 First-order Taylor approximation. AM 221: Advanced Optimization Spring 2016

Computational Optimization. Convexity and Unconstrained Optimization 1/29/08 and 2/1(revised)

Positive Theory of Equilibrium: Existence, Uniqueness, and Stability

QUADRATIC FORMS AND DEFINITE MATRICES. a 11 a 1n. a n1 a nn a 1i x i

a = (a 1; :::a i )

is a maximizer. However this is not the case. We will now give a graphical argument explaining why argue a further condition must be satisfied.

Microeconomics, Block I Part 1

CONVEX FUNCTIONS AND OPTIMIZATION TECHINIQUES A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

Learning Target: I can sketch the graphs of rational functions without a calculator. a. Determine the equation(s) of the asymptotes.

Nonlinear Programming (NLP)

Transcription:

Mathematical Foundations -1- Convexity and quasi-convexity Convex set Convex function Concave function Quasi-concave function Supporting hyperplane

Mathematical Foundations -2- Convexity and quasi-convexity Convex combination x is a convex combination of n x, x, if for some (,1) x (1 ) x x Convex Set n S is convex if, for each x, x S every convex combination x (1 ) x x S Strictly convex set The function f is strictly convex if, for each of S ( x int S) x, x S every convex combination x lies in the interior

Mathematical Foundations -3- Convexity and quasi-convexity We consider functions f defined over a convex set S Strictly convex function n SC1: f is a strictly convex function if, for any x, x S and convex combination x, 1, ( ) (1 ) ( ) ( ) In Fig. 1-2, for any convex combination x, the point D on the chord connecting A and B lies above the point C on the graph C D B of f. A x Fig. 1-2: Graph of the function lies below the chord C1: f is a convex function if, for any x, x S and convex combination x, 1, ( ) (1 ) ( ) ( )

Mathematical Foundations -4- Convexity and quasi-convexity What is the relationship between a convex set and a convex function? n Definition: Graph of a function f : S y G x y xs y n1 {(, ), ( )} A C2: The set A {( x, y) y f ( x), x S} is a convex set Points on or above the graph of f In the figure, the set A is the set of points on or above the graph of the function. C1 C2 1 1 Suppose ( x, y ) and ( x, y ) are both in A. Then y f ( x ) and y f ( x ). We need to show that each convex combination ( x, y ) A. Since f is convex, f ( x ) (1 ) f ( x ) f ( x ) (1 ) y y y. Then ( x, y ) A. QED

Mathematical Foundations -5- Convexity and quasi-convexity C2 C1 Exercise: Prove this Proposition HINT: Consider ( x, y ) ( x, f ( x )) and 1 1 1 1 ( x, y ) ( x, f ( x )). By C2 the convex combination (, ) (,(1 ) ( ) ( )) x y x A C3: For 1 f and for any y, z f S, f ( y) f ( z) ( z) ( y z) x Exercise: Prove that C1 C3 Remark: We showed already that 1 f is concave on the convex set S only if for any x, y S f f ( y) f ( z) ( z) ( y z). The proof that C1 C3 is almost identical x

Mathematical Foundations -6- Convexity and quasi-convexity C3 C1 f For any y, z S f ( y) f ( z) ( z) ( y z). x Consider any x x S. Since S is convex, the convex combination x S. Set z x. Then 1 f 1 f ( x ) f ( x ) ( x ) ( x x ) x f f ( x ) f ( x ) ( x ) ( x x ) x (1 ) Multiply these inequalities as indicated and then add. f x x x x x x f 1 ( x ) [ x (1 ) x x ]. x 1 1 ( ) (1 ) ( ) ( ) ( ) [ ( ) (1 )( )]

Mathematical Foundations -7- Convexity and quasi-convexity C4: For 1 f and for any x, x S, ( )[ g( ) g( )] where g( ) f ( x ) and so 2 1 2 1 f g x x x x 1 ( ) ( ) ( ) Graphically, C4 is the statement that the slope of g( ) is increasing. C4 C3 g g 1 ( ) ( ) (1) () 1 1 1 g( ) d g() d g() d g() But f g x x x x Therefore 1 ( ) ( ).( ). f g x x x x 1 ( ) ( ) () ( ).( ) Graph of the function lies below the chord

Mathematical Foundations -8- Convexity and quasi-convexity C3 C4 Consider x( ), x( ). x, x and convex combinations 1 2 x( ) x ( x x ) and 2 2 x( ) x ( x x ). 1 1 Therefore x x x x 1 ( 2 ) ( 1 ) ( 2 1 )( ) By C3, for any y, z S,where S is convex, f f ( y) f ( z) ( z) ( y z). x Graph of the function lies below the chord Therefore 1 ( ( 2)) ( ( 1)) f ( ( 1)) ( ( 2) ( 1)) f x x x ( x( 1 )) ( 2 1 )( x x ) ( 2 1 ) f ( x( 1 )) ( x x ) x x x Also f 1 f 1 f ( x( 1 )) f ( x( 2)) ( 1 2) ( x( 2)) ( x x ) ( 2 1 ) ( x( 2)) ( x x ) x x Add the inequalities 1 ( f f 2 1)[ ( x( 1)) ( x x ) ( x( 2)) ( x x ) x x But f g x x x x 1 ( ) ( ( )) ( ) Therefore ( 2 1 )[ g( 1 ) g( 2)] and so ( 2 1 )[ g( 2 ) g( 1 )].

Mathematical Foundations -9- Convexity and quasi-convexity An equivalent way of writing this is that for any 1 and 2 1 g( 2) g( 1) 2 1 (*) That is, the second derivative of g( ) must be positive. Also if the second derivative is positive then (*) holds. Therefore if 2 f C4 and C5 are also equivalent. C5: For 2 f and any x, x S g() where g x ( ) ( ( )) ((1 ) ))

Mathematical Foundations -1- Convexity and quasi-convexity Restatement of C5 Suppose that f is twice differentiable on n Define g( ) f ( x ) f ( x ( x x )). 2 n n 2 2 d g 1 f 1 f () ( ) ( ) ( ) ( ) ( ) 2 xi xi x j x j x x x x x d i1 i1 xix j xix j Then we can rewrite C5 as follows: C5: For 2 f defined on S and any x, x S 2 1 f ( x x ) H( x )( x x ) where H( x ) [ ( x )] x x i j If n S then C5 is the statement that the Hessian Matrix H( x ) must be positive semi-definite for all n x

Mathematical Foundations -11- Convexity and quasi-convexity Strictly Concave Function f is a strictly concave function if, for any x, x S and convex combination x, 1, ( ) (1 ) ( ) ( ) Concave Function f is a concave function if, for any x, x S and convex combination x, 1, ( ) (1 ) ( ) ( ) Concave function Reverse all the inequalities in C1 C5 to obtain equivalent definitions of a concave function. Exercise: Show that a function f is concave on S if and only if f is convex on S.

Mathematical Foundations -12- Convexity and quasi-convexity Class exercises (in groups) 1. Prove that a concave function of a concave function is not necessarily concave. 2. Prove that a strictly concave function of a strictly concave function maybe strictly convex. 3. Show that the sum of concave functions is concave.

Mathematical Foundations -13- Convexity and quasi-convexity Shortcuts for checking whether a function is convex or concave With one variable, the easiest way to tell if a function is strictly convex is to see if it has a positive second derivative. For functions of more than one variable, the following propositions are often helpful. Proposition: The sum of convex functions is convex. Proposition: Convex function of a function h( x) g( f ( x)) is convex if (i) f is convex & g is increasing and convex, or (ii) f is linear and g is convex. Proof of (i) Since f is convex, Since g is increasing Define then Since g is convex Appealing to (*) f ( x ) (1 ) f ( x ) f ( x ). g g ( ( )) ((1 ) ( ) ( )) 1 y f ( x ) y f ( x ) g( f ( x )) g((1 ) y y ) (*) g((1 ) y y ) (1 ) g( y ) g( y ) (1 ) g( f ( x )) g( f ( x )). g g g ( ( )) (1 ) ( ( )) ( ( ))

Mathematical Foundations -14- Convexity and quasi-convexity Quasi-concave Function A function f is quasi-concave on the convex set n S if, for any x, x in S, such that 1 ( ) f ( x ) and any convex combination x x x (1 ), 1, ( ) f ( x ). Strictly Quasi-concave Function A function f is quasi-concave on the convex set n S if, for any x, x in S, such that 1 ( ) f ( x ) and any convex combination x x x (1 ), 1, ( ) f ( x ). Example: U( x) x1x 2is quasi-concave on 2 and is strictly quasi-concave on 2 2 { x x, x } But why! Read on.

Mathematical Foundations -15- Convexity and quasi-convexity Proposition: A function is quasi-concave if and only if the upper contour sets of the function are convex Proof of equivalence: Without loss of generality we may assume that 1 ( ) f ( x ). Suppose f is quasi-concave and for any ˆx, consider vectors x and 1 x which lie in the upper contour set U C ( x ˆ) of this function. That is ˆ and ( ) f ( x) ˆ 1 ( ) f ( x). From the definition of quasiconcavity, for any convex combination, x x x (1 ), ( ) f ( x ). Since ˆ, it follows that for all convex combinations f ( x ) f ( xˆ ). Hence all convex ( ) f ( x) combinations lie in the upper contour set. Thus C ( ˆ ) U x is convex. Conversely, if the upper contour sets are convex, then C ( ) U x is convex. Since 1 ( ) f ( x ). Then x and 1 x are both in C ( ) U x therefore, by the convexity of C ( ) U x, all convex combination lie in this set. That is ( ) f ( x ) for all, 1. Q.E.D.

Mathematical Foundations -16- Convexity and quasi-convexity Proposition: If there is some strictly increasing function g such that h( x) g( f ( x)) is concave then f(x) is quasi-concave. Proof: We need to establish that if 1 ( ) f ( x ) then ( ) f ( x ), 1 By concavity h( x ) (1 ) h( x ) h( x ), that is g( f ( x )) (1 ) g( f ( x )) g( f ( x )). (*) Since g is increasing and g ( ( )) g( f ( x )). 1 ( ) f ( x ), g 1 ( ( )) g( f ( x )). Then substituting into (*), By hypothesis, g is a strictly increasing function. Then ( ) f ( x ). Q.E.D. Remark: It is in this last line of the proof that we require that g is strictly increasing. For otherwise g could be constant over some neighborhood g 1 ( ( )) g( f ( x )). Nx (, ) and so we could have 1 ( ) f ( x ) and

Mathematical Foundations -17- Convexity and quasi-convexity Proposition B.2-9: If : n f f ( z) f ( z) ) and quasi-concave then f is concave. is positive, homogeneous of degree 1 (i.e. for all Proof: Because f is homogeneous of degree 1 it follows that for any x, x and (,1) (1 ) f ( x ) f ( x ) f ((1 ) x ) f ( x ), Since f, there exists such that f ((1 ) x ) f ( x ). (1) Then 1 (1 ) f ( x ) f ( x ) (1 ) f ( x ). (2) Because f is homogeneous of degree 1 it follows from (1) that f ((1 ) x ) f ( x ). Note that 1 ((1 ) x x ) (1 ) x x. 1 1 1 Therefore 1 ((1 ) x x ) is a convex combination of (1 ) and x x.

Mathematical Foundations -18- Convexity and quasi-convexity We showed that 1 ((1 ) x x ) Therefore, by the quasi-concavity of f is a convex combination of (1 ) and x x. x 1 ( ((1 ) )) ( ) Because f is homogeneous of degree 1 it follows that. (3) x 1 and hence that ((1 ) ) ( ) f ((1 ) x x ) (1 ) f ( x ). Appealing to (2), f ((1 ) x x ) (1 ) f ( x ) f ( x ). QED

Mathematical Foundations -19- Convexity and quasi-convexity Class examples: In each case is f (i) quasi-concave (ii) strictly quasi-concave (iii) concave (a) (b) (c) (d) (e) (f) (g) 2 f ( x) (1 x1)(2 x2), x 2 f ( x) x1x 2, x 2 f ( x) x1x 2, x 3 f ( x) x1x 2 x1x 3, x f ( x) x x x 1/2 1/2 1/2 1 2 3 f ( x) ( x x x ) 1/2 1/2 1/2 2 1 2 3 f ( x) ( x x x ) 1/2 1/2 1/2 3/2 1 2 3

Mathematical Foundations -2- Convexity and quasi-convexity Class Exercise: Are the following statements true or false? 1. For any convex set then f takes on its global maximum at 2. For any set n S if the quasi-concave function f : S has a local maximum at * x. n S if the strictly quasi-concave function f : S has a local maximum at then f takes on its global maximum at * x. * x, * x,

Mathematical Foundations -21- Convexity and quasi-convexity Supporting hyperplane for a convex set Suppose that for some x, the upper contour set S x { ( ) ( )} is convex. (This will be true if f is quasi-concave.) The linear approximation of f at x is f l x x x x x ( ) ( ) ( ) ( ) Consider the upper contour set of l. This is the set f T x l x l x x x x x x { ( ) ( )} { ( ) ( ) } We will argue that S T..

Mathematical Foundations -22- Convexity and quasi-convexity Proposition: Supporting hyperplane If the set S x { ( ) ( )} is convex, then f S T x x x x x { ( ) ( ) }. The hyperplane hyperplane. f x ( x ) ( x x ) touching the boundary of S at x is called a supporting Proof: For 1 x S define g( ) f ( x( )) f ( x ) f ( x ( x x )). dg Since S is convex, x( ) is in S. Therefore g( ). Since g() it follows that (). d But as we have previously shown, dg d f x () ( x ) ( x x ). Then for all 1 x S, f x ( x ) ( x x ). Q.E.D.

Mathematical Foundations -23- Convexity and quasi-convexity Example 1: Consider the contour set of f ( x) ( x 2 x ) 1/2 2 1 2 through (1,1). The upper contour set is S { x ( x 2 x ) 9} 1/2 2 1 2 Confirm that f ( x) ( x 2 x ) is quasi-concave. 1/2 2 1 2 The linear approximation of f at x is f l x x x x x x x ( ) ( ) ( ) ( ) 9 6( 11) 6( 2 1) Consider the upper contour set of l. This is the set T { x l( x) l( x )} { x 6( x x ) 6( x x ) } 1 1 2 2. Example 2: Production set of a firm Y {( z, q) 3z q } where (,1]. Consider the tangent line through (1,3)