IMP 2007 Introductory math course. 5. Optimization. Antonio Farfán Vallespín

Similar documents
Basic mathematics of economic models. 3. Maximization

September Math Course: First Order Derivative

Example: When describing where a function is increasing, decreasing or constant we use the x- axis values.

EconS 301. Math Review. Math Concepts

Tutorial 3: Optimisation

Rules of Differentiation

Review of Optimization Methods

ECON2285: Mathematical Economics

Section 3.4: Concavity and the second Derivative Test. Find any points of inflection of the graph of a function.

EC5555 Economics Masters Refresher Course in Mathematics September 2013

PROBLEM SET 1 (Solutions) (MACROECONOMICS cl. 15)

II. An Application of Derivatives: Optimization

INTRODUCTORY MATHEMATICAL ANALYSIS

Exponential and Logarithmic. Functions CHAPTER The Algebra of Functions; Composite

0,0 B 5,0 C 0, 4 3,5. y x. Recitation Worksheet 1A. 1. Plot these points in the xy plane: A

Differentiation. 1. What is a Derivative? CHAPTER 5

Roberto s Notes on Differential Calculus Chapter 8: Graphical analysis Section 1. Extreme points

CHAPTER 4: HIGHER ORDER DERIVATIVES. Likewise, we may define the higher order derivatives. f(x, y, z) = xy 2 + e zx. y = 2xy.

Universidad Carlos III de Madrid

Extreme Values of Functions

2. ETA EVALUATIONS USING WEBER FUNCTIONS. Introduction

Definition: Let f(x) be a function of one variable with continuous derivatives of all orders at a the point x 0, then the series.

MEAN VALUE THEOREM. Section 3.2 Calculus AP/Dual, Revised /30/2018 1:16 AM 3.2: Mean Value Theorem 1

Differentiation. The main problem of differential calculus deals with finding the slope of the tangent line at a point on a curve.

ELEG 3143 Probability & Stochastic Process Ch. 4 Multiple Random Variables

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

Web Appendix for The Value of Switching Costs

Curve Sketching. The process of curve sketching can be performed in the following steps:

WorkSHEET 2.1 Applications of differentiation Name:

Answer Key-Math 11- Optional Review Homework For Exam 2

where u is the decision-maker s payoff function over her actions and S is the set of her feasible actions.

Final Exam Review Math Determine the derivative for each of the following: dy dx. dy dx. dy dx dy dx. dy dx dy dx. dy dx

Math Review and Lessons in Calculus

Roles of Convexity in Optimization Theory. Efor, T. E and Nshi C. E

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

Marginal Functions and Approximation

Review of Prerequisite Skills for Unit # 2 (Derivatives) U2L2: Sec.2.1 The Derivative Function

Math 1314 Lesson 23 Partial Derivatives

RATIONAL FUNCTIONS. Finding Asymptotes..347 The Domain Finding Intercepts Graphing Rational Functions

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

y,z the subscript y, z indicating that the variables y and z are kept constant. The second partial differential with respect to x is written x 2 y,z

Chapter 1 - Preference and choice

9.3 Graphing Functions by Plotting Points, The Domain and Range of Functions

z = f (x; y) f (x ; y ) f (x; y) f (x; y )

Tvestlanka Karagyozova University of Connecticut

Week 3: Sets and Mappings/Calculus and Optimization (Jehle September and Reny, 20, 2015 Chapter A1/A2)

YURI LEVIN AND ADI BEN-ISRAEL

4.1 & 4.2 Student Notes Using the First and Second Derivatives. for all x in D, where D is the domain of f. The number f()

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

EC /11. Math for Microeconomics September Course, Part II Problem Set 1 with Solutions. a11 a 12. x 2

Chapter 3 - The Concept of Differentiation

Part 2A. 3. Indifference Curves

INTRODUCTORY MATHEMATICS FOR ECONOMICS MSCS. LECTURE 3: MULTIVARIABLE FUNCTIONS AND CONSTRAINED OPTIMIZATION. HUW DAVID DIXON CARDIFF BUSINESS SCHOOL.

3 Geometrical Use of The Rate of Change

Paul Schrimpf. October 18, UBC Economics 526. Unconstrained optimization. Paul Schrimpf. Notation and definitions. First order conditions

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

Winter Lecture 10. Convexity and Concavity

Mathematical Economics: Lecture 16

School of Business. Blank Page

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

Feedback Linearization

Chiang/Wainwright: Fundamental Methods of Mathematical Economics

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

Online Appendix: The Continuous-type Model of Competitive Nonlinear Taxation and Constitutional Choice by Massimo Morelli, Huanxing Yang, and Lixin Ye

STUDY MATERIALS. (The content of the study material is the same as that of Chapter I of Mathematics for Economic Analysis II of 2011 Admn.

Maximum and Minimum Values

Abstract. 1. Introduction

Person-Specific Labor Costs and the Employment Effects of. Minimum Wage Policy

Econ 101A Problem Set 1 Solution

1 Lecture 25: Extreme values

Properties of Walrasian Demand

Partial Differentiation

Lecture 6: Contraction mapping, inverse and implicit function theorems

6.2 Important Theorems

Economics 101A (Lecture 3) Stefano DellaVigna

8. Complex Numbers. sums and products. basic algebraic properties. complex conjugates. exponential form. principal arguments. roots of complex numbers

3.5 Quadratic Approximation and Convexity/Concavity

MHF 4U Unit 7: Combining Functions May 29, Review Solutions

MATH 5720: Unconstrained Optimization Hung Phan, UMass Lowell September 13, 2018

Part I Analysis in Economics

Chapter 2 Section 3. Partial Derivatives

Linear Algebra: Linear Systems and Matrices - Quadratic Forms and Deniteness - Eigenvalues and Markov Chains

Pre-Test Developed by Sean Moroney and James Petersen UNDERSTANDING THE VELOCITY CURVE. The Velocity Curve in Calculus

Nonlinear Programming (NLP)

MODULE - 2 LECTURE NOTES 3 LAGRANGE MULTIPLIERS AND KUHN-TUCKER CONDITIONS

The concept of limit

Calculus and optimization

Microeconomic Theory I Midterm October 2017

f( x) f( y). Functions which are not one-to-one are often called many-to-one. Take the domain and the range to both be all the real numbers:

THE GAMMA FUNCTION THU NGỌC DƯƠNG

ECON 186 Class Notes: Optimization Part 2

Published in the American Economic Review Volume 102, Issue 1, February 2012, pages doi: /aer

One Variable Calculus. Izmir University of Economics Econ 533: Quantitative Methods and Econometrics

m = Average Rate of Change (Secant Slope) Example:

STAT 801: Mathematical Statistics. Hypothesis Testing

Multi Variable Calculus

Optimization. Sherif Khalifa. Sherif Khalifa () Optimization 1 / 50

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

(One Dimension) Problem: for a function f(x), find x 0 such that f(x 0 ) = 0. f(x)

Preliminaries Lectures. Dr. Abdulla Eid. Department of Mathematics MATHS 101: Calculus I

Transcription:

IMP 007 Introductory math course 5. Optimization Antonio Farán Vallespín Toniaran@hotmail.com

Derivatives Why are derivatives so important in economics? Derivatives inorm us o the eect o changes o the independent variable on the dependent variable. e.g. what happens with GDP i salaries increase? and with demand? Derivatives are a tool or solving optimisation problems. Humans are assumed to behave optimising their utility.

Economics is a science o choice: How do individuals choose their consumption bundles? How do individuals choose among what to consume today and tomorrow? How do irms choose how many units should they produce? How do irms choose how many workers should they hire? How do you choose how many hours should you study and how many hours should you spend in the disco? 3

We assume: Agents have an objective unction. (e.g. utility, costs, proits ) Agents can choose the value o some variables argument o the objective unction: Choice variables Agents attempt either to: Maximize: choose the values o the choice variables that yield the maximum possible value o the objective unction. (e.g. maximize proit or utility), or to: Minimize: choose the values o the choice variables that yield the minimum possible value o the objective unction. (e.g. minimize total cost) 4

Optimization: Given an objective unction rom set A to the real numbers, it is sought: I minimizing: an element x 0 in A such that ( x 0 ) ( x) or all x in A. Then we call x 0 a minimum. I maximizing: an element x 0 in A such that ( x 0 ) ( x) or all x in A. Then we call x 0 a maximum. : A R 5

Relative versus absolute extremum An extremum (a maximum or a minimum) can be: Absolute (or global): i it is maximum or minimum in all the range o the unction. Relative (or local): i it is only maximum or minimum in the immediate neighbourhood o the point only. SEE EXAMPLES 6

What does this have to do with derivatives???? Increasing and decreasing unctions Stationary points Convexity and concavity 7

Increasing unctions:. Optimization A unction is strictly increasing in an interval when or any two points in this interval, x and (x+h) it can be veriied that: x x h ( x) ( x h) We can express this by using the dierence quotient: y x ( x h) h ( x) I this true or any value o h in the interval, then it is also true or values o h approaching zero. Thereore, the derivative o any point at an interval where the unction is increasing must be positive. Necessary condition. 0 8

Decreasing unctions:. Optimization A unction is strictly decreasing in an interval when or any two points in this interval, x and (x+h) it can be veriied that: x x h ( x) ( x h) We can express this by using the dierence quotient: y x ( x h) h ( x) I this true or any value o h in the interval, then it is also true or values o h approaching zero. Thereore, the derivative o any point at an interval where the unction is decreasing must be negative. Necessary condition. 0 9

Summarizing:. Optimization I the derivative at one point is positive: The unction is increasing at that point. I the derivative at one point is negative: The unction is decreasing at that point. But, what happens when the derivative is neither positive nor negative? this is to say, when it is equal to zero. 10

Case when derivative is zero: What does it mean that the derivative is zero? For being zero it must be that (x+h)=(x). It implies that at this point the unction is neither increasing nor decreasing. Thus we call these points where =0: stationary points. 11

What can a stationary point be? A local maximum: I the neighbouring points have a smaller (x). This also implies that the unction is: Increasing to the right o this point. >0 Decreasing to the let o this point. <0 A local minimum: I the neighbouring points have a larger (x). This also implies that the unction is: Decreasing to the right o this point: <0 Increasing to the let o this point: >0 A point o inlection I the unction is increasing or decreasing on both sides. See Exercise 1. 1

How can be know whether a stationary point is a local maximum, a local minimum or a point o inlection? a) Evaluating whether the unction is increasing or decreasing to the let and to the right. b) Evaluating the second derivative. 13

The second derivative: It is obtained by taking the derivative o the derivative I the irst derivative tells us about the rate o change o the dependent variable, the second derivative tells us about the rate o change o the rate o change The second derivative is denoted by: (x) d y d dy 14

Second derivative being positive means that the irst derivative is increasing: I the irst derivative was positive, then the unction increases at increasing rates. I the irst derivative was negative, then the unction decreases at increasing rate. Second derivative being negative means that the irst derivative is decreasing: I the irst derivative was positive, then the unction increases at decreasing rates. I the irst derivative was negative, then the unction decreases at decreasing rates. 15

Assuming an ininitesimal increase in the independent variable rom a point x=x 0 (x 0 )>0 means the value o the unction tends to increase. (x 0 )<0 means the value o the unction tends to decrease. (x 0 )>0 means the slope o the curve tends to increase. (x 0 )<0 means the slope o the curve tends to decrease. 16

Dierentiability:. Optimization I second derivative exists or all values o an interval, then the unction is twice dierentiable in that interval. I (x) is continuous in an interval, then (x) is said to be twice continuously dierentiable. 17

Curvature o a unction: Second derivative also inorm us o the curvature o a unction. Inormally, curvature: how the curve bends. A curve can be: Convex: i shape is a valley, a U, happy. Concave: i shape is a hill, an inverse U, sad. 18

Concave: I we pick any two pair o points M and N on its curve. and join them by a straight line. the line segment MN must lie entirely below the curve, except points M and N. Convex: I we pick any two pair o points M and N on its curve. and join them by a straight line. the line segment MN must lie entirely above the curve, except points M and N. 19

Concave: (x 0 )<0. Optimization Start with positive slope and it reduces becoming negative ater a maximum. Convex: (x 0 )>0 Start with negative slope and it increases becoming positive ater a minimum. I (x 0 )=0 (x 0 )>0 Inlection point rom concavity to convexity. (x 0 )<0 Inlection point rom convexity to concavity 0

Necessary and suicient conditions: First-order condition: =0: necessary condition or a point x=x 0 to be a local extremum. But =0 is not suicient condition or a point x=x 0 to be a local extremum. Second order condition, ater =0: >0 and <0 suicient condition or being a local maximum or minimum But it is not necessary, you can have extremum with =0. See y=x 4 at x=0. 1

>0 in an interval: unction strictly increasing in the interval. <0 in an interval: unction strictly decreasing in the interval. =0: stationary >0, local minimum <0, local maximum =0, inlection point >0 concave-convex <0 convex-concave See Exercise and 3.

Objective unctions with more than one choice variable: z=(x 1,x,,x n ) At a extremum, it must be that dz=0 or ininitesimal values o 1,,., n. Since we know that: dz 11 33 Then, the necessary condition (irst-order) or extremum is that: 1 3 0 3

Second order condition: Ater the irst-order condition is met, A stationary value will be a maximum i: the second total derivative o z (d z) is positive deinite. A stationary value will be a minimum i: The second total derivative o z (d z) 4

What is d z? d Substituting dz by dz. Optimization ( dz) ( dz) ( dz) z d dz) 1 x1 x x3 ( 11 33 3 We obtain: d z 1 31 1 11 1 3 1 3 1 3 1 3 33 13 1 3 3 3 5

How do we know i that is positive or negative deinite? We use the Hessian: 11 1 31 Whose succesive principal minors are: H1 11 H 1 3 13 3 33 11 1 H H3 H 1 6

Negative d z deinite i: H1 0; H 0; H 3 0 Positive d z deinite i: H1 0; H 0; H 3 0 In using this condition we must evaluate all the principal minors at the stationary points where 1 3 0 7

Advance o matrix algebra: How to calculate the determinant? Given the matrix: a11 a1 a13 a1 a a3 a31 a3 a33 The determinant det(a) or is: A a 13 a 11 a a a a 31 33 a 11 a 13 a 3 a 1 a 3 a A 3 a 33 a 31 a 1 a A1 a 11 A a11 a a1 a1 1 a 1 a 3 8

BIBLIOGRAPHY:. Optimization Alpha C. Chiang (1984) Fundamental Methods o Mathematical Economics Third edition. McGraw-Hill, Inc. Ch. 9,11,1 9