Mathematical Economics: Lecture 2

Similar documents
Mathematical Economics: Lecture 3

Mathematical Economics: Lecture 9

Copyright c 2007 Jason Underdown Some rights reserved. quadratic formula. absolute value. properties of absolute values

DIFFERENTIATION RULES

1 Functions and Graphs

DRAFT - Math 101 Lecture Note - Dr. Said Algarni

Lecture 32: Taylor Series and McLaurin series We saw last day that some functions are equal to a power series on part of their domain.

MAT137 Calculus! Lecture 6

MATH The Derivative as a Function - Section 3.2. The derivative of f is the function. f x h f x. f x lim

f (x) = k=0 f (0) = k=0 k=0 a k k(0) k 1 = a 1 a 1 = f (0). a k k(k 1)x k 2, k=2 a k k(k 1)(0) k 2 = 2a 2 a 2 = f (0) 2 a k k(k 1)(k 2)x k 3, k=3

Tangent Lines Sec. 2.1, 2.7, & 2.8 (continued)

A Library of Functions

Chapter 2: Differentiation

2 2 + x =

MAT 107 College Algebra Fall 2013 Name. Final Exam, Version X

MA4001 Engineering Mathematics 1 Lecture 15 Mean Value Theorem Increasing and Decreasing Functions Higher Order Derivatives Implicit Differentiation

Comparative Statics. Autumn 2018

Lecture 9: Taylor Series

Chapter 2: Differentiation

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

1) The line has a slope of ) The line passes through (2, 11) and. 6) r(x) = x + 4. From memory match each equation with its graph.

Ma 530 Power Series II

Math Placement Test Review Sheet Louisburg College _ Summer = c = d. 5

Problem set 5, Real Analysis I, Spring, otherwise. (a) Verify that f is integrable. Solution: Compute since f is even, 1 x (log 1/ x ) 2 dx 1

2.12: Derivatives of Exp/Log (cont d) and 2.15: Antiderivatives and Initial Value Problems

Convergence of sequences and series

Advanced Calculus Math 127B, Winter 2005 Solutions: Final. nx2 1 + n 2 x, g n(x) = n2 x

Calculus (Math 1A) Lecture 6

Partial Derivatives October 2013

Differentiation. 1. What is a Derivative? CHAPTER 5

Continuity, Intermediate Value Theorem (2.4)

Tvestlanka Karagyozova University of Connecticut

Review of Section 1.1. Mathematical Models. Review of Section 1.1. Review of Section 1.1. Functions. Domain and range. Piecewise functions

3.9 Derivatives of Exponential and Logarithmic Functions

Integration - Past Edexcel Exam Questions

Calculus. Central role in much of modern science Physics, especially kinematics and electrodynamics Economics, engineering, medicine, chemistry, etc.

2.2 The derivative as a Function

1. Use the properties of exponents to simplify the following expression, writing your answer with only positive exponents.

Mathematical Economics: Lecture 6

Section 0.2 & 0.3 Worksheet. Types of Functions

Calculus I Exam 1 Review Fall 2016

Example: Limit definition. Geometric meaning. Geometric meaning, y. Notes. Notes. Notes. f (x, y) = x 2 y 3 :

Lecture 5 - Logarithms, Slope of a Function, Derivatives

Definition: If y = f(x), then. f(x + x) f(x) y = f (x) = lim. Rules and formulas: 1. If f(x) = C (a constant function), then f (x) = 0.

Chapter 6: Sections 6.1, 6.2.1, Chapter 8: Section 8.1, 8.2 and 8.5. In Business world the study of change important

Review: Power series define functions. Functions define power series. Taylor series of a function. Taylor polynomials of a function.

MATHEMATICS LEARNING AREA. Methods Units 1 and 2 Course Outline. Week Content Sadler Reference Trigonometry

Chapter 11. Taylor Series. Josef Leydold Mathematical Methods WS 2018/19 11 Taylor Series 1 / 27

Chapter 7: Techniques of Integration

MATH 1902: Mathematics for the Physical Sciences I

1. Use the properties of exponents to simplify the following expression, writing your answer with only positive exponents.

Section 4.8 Anti Derivative and Indefinite Integrals 2 Lectures. Dr. Abdulla Eid. College of Science. MATHS 101: Calculus I

King Fahd University of Petroleum and Minerals Prep-Year Math Program Math Term 161 Recitation (R1, R2)

Math 110 Final Exam General Review. Edward Yu

Power series solutions for 2nd order linear ODE s (not necessarily with constant coefficients) a n z n. n=0

MATH 2554 (Calculus I)

COMMON CORE STATE STANDARDS TO BOOK CORRELATION

Math 1431 Final Exam Review

Math Camp II. Calculus. Yiqing Xu. August 27, 2014 MIT

Calculus (Math 1A) Lecture 5

CALCULUS. Berkant Ustaoğlu CRYPTOLOUNGE.NET

function independent dependent domain range graph of the function The Vertical Line Test

MTAEA Differentiation

Algebra Performance Level Descriptors

Taylor Series. Math114. March 1, Department of Mathematics, University of Kentucky. Math114 Lecture 18 1/ 13

Derivatives and the Product Rule

8.7 MacLaurin Polynomials

CALCULUS ASSESSMENT REVIEW

Physics 250 Green s functions for ordinary differential equations

3.1 Day 1: The Derivative of a Function

Math 106 Fall 2014 Exam 2.1 October 31, ln(x) x 3 dx = 1. 2 x 2 ln(x) + = 1 2 x 2 ln(x) + 1. = 1 2 x 2 ln(x) 1 4 x 2 + C

Math Review ECON 300: Spring 2014 Benjamin A. Jones MATH/CALCULUS REVIEW

ALGEBRA II WITH TRIGONOMETRY EXAM

PhysicsAndMathsTutor.com

MATH1190 CALCULUS 1 - NOTES AND AFTERNOTES

1 Lecture 18: The chain rule

Chapter 8: Taylor s theorem and L Hospital s rule

Math 106 Calculus 1 Topics for first exam

Math 240 (Driver) Qual Exam (9/12/2017)

Summer Review for Students Taking Calculus in No calculators allowed. To earn credit: Be sure to show all work in the area provided.

Section Taylor and Maclaurin Series

Ch 7 Summary - POLYNOMIAL FUNCTIONS

1 + lim. n n+1. f(x) = x + 1, x 1. and we check that f is increasing, instead. Using the quotient rule, we easily find that. 1 (x + 1) 1 x (x + 1) 2 =

FLORIDA STANDARDS TO BOOK CORRELATION

Topic # /31 Feedback Control Systems. Analysis of Nonlinear Systems Lyapunov Stability Analysis

Limits at Infinity. Horizontal Asymptotes. Definition (Limits at Infinity) Horizontal Asymptotes

1S11: Calculus for students in Science

Analytic Geometry and Calculus I Exam 1 Practice Problems Solutions 2/19/7

Mathematical Economics: Lecture 16

Lecture Notes for Math 1000

Announcements. Topics: Homework: - sections 4.5 and * Read these sections and study solved examples in your textbook!

Math 115 Section 018 Course Note

Learning Objectives for Math 165

The function graphed below is continuous everywhere. The function graphed below is NOT continuous everywhere, it is discontinuous at x 2 and

7.1. Calculus of inverse functions. Text Section 7.1 Exercise:

MAT01A1: Functions and Mathematical Models

Algebra I Unit Report Summary

MATH 116, LECTURE 13, 14 & 15: Derivatives

Georgia Department of Education Common Core Georgia Performance Standards Framework CCGPS Advanced Algebra Unit 2

MAT137 Calculus! Lecture 5

Transcription:

Mathematical Economics: Lecture 2 Yu Ren WISE, Xiamen University September 25, 2012

Outline 1

Number Line The number line, origin (Figure 2.1 Page 11)

Number Line Interval (a, b) = {x R 1 : a < x < b} [a, b] = {x R 1 : a x b} (a, b] = {x R 1 : a < x b} [a, b) = {x R 1 : a x < b}

Function on R 1 Function on R 1 : a rule which assigns a number in R 1 to a number in R 1 Example in the book: f (x) = x + 1

Function on R 1 Function on R 1 : a rule which assigns a number in R 1 to a number in R 1 Example in the book: f (x) = x + 1

Function on R 1 x = 0 f (x) = 0.5 x = 3.25 f (x) = 0.98 x = π f (x) = 2 Is f (x) a function about x?

Function on R 1 Domain: the set of numbers at which the function is defined Example: f (x) = 1 x

Function on R 1 Domain: the set of numbers at which the function is defined Example: f (x) = 1 x

Function on R 1 x: independent variable, exogenous variable y = f (x): dependent variable, endogenous variable

Function Categories monomials: f 1 (x) = 4x 3, f 1 (x) = 5x 2, f 1 (x) = 2x f (x) = ax b b: positive integer

Function Categories monomials: f 1 (x) = 4x 3, f 1 (x) = 5x 2, f 1 (x) = 2x f (x) = ax b b: positive integer

Function Categories polynomials: f (x) = 4x 3 + 5x 2 + 2x + 1 f (x) = a 1 x b 1 + a 2x b 2 + + a nx b n b i : positive integer

Function Categories polynomials: f (x) = 4x 3 + 5x 2 + 2x + 1 f (x) = a 1 x b 1 + a 2x b 2 + + a nx b n b i : positive integer

Function Categories rational function f (x) = polynomial polynomial,

Function Categories exponential function f (x) = a x,

Function Categories trigonometric function f (x) = F (sin(x), cos(x), ),

Function Categories increasing function, decreasing function

Minimum local minimum: a minimum within a local neighborhood For example: if x 1 is a local minimum of f (x), then f (x 1 ) is the lowest value within (x 1 ɛ, x 1 + ɛ), where ɛ is a small positive number

Minimum local minimum: a minimum within a local neighborhood For example: if x 1 is a local minimum of f (x), then f (x 1 ) is the lowest value within (x 1 ɛ, x 1 + ɛ), where ɛ is a small positive number

Minimum global minimum: a minimum within the whole domain For example: if x 1 is a local minimum of f (x) and x 2 can be another local minimum of f (x), but only one of them can be the global minimum.

Minimum global minimum: a minimum within the whole domain For example: if x 1 is a local minimum of f (x) and x 2 can be another local minimum of f (x), but only one of them can be the global minimum.

Linear Function linear function: f (x) = mx + b m is the slope, b is the intercept

Linear Function Example 2.2 The slope of the line joining the points (4,6) and (0,7) is m = 7 6 0 4 = 1 4.

Economic Interpretation: Marginal Slope in economics: marginal cost, marginal utility, marginal product marginal cost is the change in total cost that arises when the quantity produced changes by one unit. marginal product is the extra output produced by one more unit of an input

Economic Interpretation: Marginal Slope in economics: marginal cost, marginal utility, marginal product marginal cost is the change in total cost that arises when the quantity produced changes by one unit. marginal product is the extra output produced by one more unit of an input

Economic Interpretation: Marginal Slope in economics: marginal cost, marginal utility, marginal product marginal cost is the change in total cost that arises when the quantity produced changes by one unit. marginal product is the extra output produced by one more unit of an input

Slope of Nonlinear Function nonlinear function derivative: f (x 0 ) = lim h 0 f (x 0 +h) f (x 0 ) h

Slope of Nonlinear Function computing rules: Theorem 2.4: (f ± g) (x 0 ) = f (x 0 ) ± g (x 0 ) kf (x 0 ) = k(f (x 0 )) (f ( ) g) (x 0 ) = f (x 0 )g(x 0 ) + f (x 0 )g (x 0 ) f (x0 ) = f (x 0 )g(x 0 ) f (x 0 )g (x 0 ) g g(x 0 ) 2 (f (x) n ) = n(f (x)) n 1 f (x) (x k ) = kx k 1

Slope of Nonlinear Function computing rules: Theorem 2.4: (f ± g) (x 0 ) = f (x 0 ) ± g (x 0 ) kf (x 0 ) = k(f (x 0 )) (f ( ) g) (x 0 ) = f (x 0 )g(x 0 ) + f (x 0 )g (x 0 ) f (x0 ) = f (x 0 )g(x 0 ) f (x 0 )g (x 0 ) g g(x 0 ) 2 (f (x) n ) = n(f (x)) n 1 f (x) (x k ) = kx k 1

Slope of Nonlinear Function computing rules: Theorem 2.4: (f ± g) (x 0 ) = f (x 0 ) ± g (x 0 ) kf (x 0 ) = k(f (x 0 )) (f ( ) g) (x 0 ) = f (x 0 )g(x 0 ) + f (x 0 )g (x 0 ) f (x0 ) = f (x 0 )g(x 0 ) f (x 0 )g (x 0 ) g g(x 0 ) 2 (f (x) n ) = n(f (x)) n 1 f (x) (x k ) = kx k 1

Slope of Nonlinear Function computing rules: Theorem 2.4: (f ± g) (x 0 ) = f (x 0 ) ± g (x 0 ) kf (x 0 ) = k(f (x 0 )) (f ( ) g) (x 0 ) = f (x 0 )g(x 0 ) + f (x 0 )g (x 0 ) f (x0 ) = f (x 0 )g(x 0 ) f (x 0 )g (x 0 ) g g(x 0 ) 2 (f (x) n ) = n(f (x)) n 1 f (x) (x k ) = kx k 1

Slope of Nonlinear Function computing rules: Theorem 2.4: (f ± g) (x 0 ) = f (x 0 ) ± g (x 0 ) kf (x 0 ) = k(f (x 0 )) (f ( ) g) (x 0 ) = f (x 0 )g(x 0 ) + f (x 0 )g (x 0 ) f (x0 ) = f (x 0 )g(x 0 ) f (x 0 )g (x 0 ) g g(x 0 ) 2 (f (x) n ) = n(f (x)) n 1 f (x) (x k ) = kx k 1

Slope of Nonlinear Function computing rules: Theorem 2.4: (f ± g) (x 0 ) = f (x 0 ) ± g (x 0 ) kf (x 0 ) = k(f (x 0 )) (f ( ) g) (x 0 ) = f (x 0 )g(x 0 ) + f (x 0 )g (x 0 ) f (x0 ) = f (x 0 )g(x 0 ) f (x 0 )g (x 0 ) g g(x 0 ) 2 (f (x) n ) = n(f (x)) n 1 f (x) (x k ) = kx k 1

Slope of Nonlinear Function computing rules: Theorem 2.4: (f ± g) (x 0 ) = f (x 0 ) ± g (x 0 ) kf (x 0 ) = k(f (x 0 )) (f ( ) g) (x 0 ) = f (x 0 )g(x 0 ) + f (x 0 )g (x 0 ) f (x0 ) = f (x 0 )g(x 0 ) f (x 0 )g (x 0 ) g g(x 0 ) 2 (f (x) n ) = n(f (x)) n 1 f (x) (x k ) = kx k 1

Example Example 2.5: For f (x) = x 2, in order to prove that f (3) = 6, we need to show that (3 + h n ) 2 3 2 h n 6, as h n 0 for every sequence {h n } which approaches zero.

Example For any h, (3 + h) 2 3 2 h = 9 + 6h + h2 9 h which clearly converges to 6 as h 0. f (3) = 6. = h(6 + h) h = 6+h

Differentiability and Continuity f (x differentiable: lim 0 +h) f (x 0 ) h 0 h exists and same h Is f (x) = x differentiable at x 0 = 0?

Differentiability and Continuity f (x differentiable: lim 0 +h) f (x 0 ) h 0 h exists and same h Is f (x) = x differentiable at x 0 = 0?

Differentiability and Continuity lim h 0 f (x 0 ) = lim h 0 + f (x 0 ) left limit: lim h 0 f (x 0 ) right limit: lim h 0 + f (x 0 )

Differentiability and Continuity lim h 0 f (x 0 ) = lim h 0 + f (x 0 ) left limit: lim h 0 f (x 0 ) right limit: lim h 0 + f (x 0 )

Differentiability and Continuity continuous: f (x 0 ) = lim h 0 f (x 0 + h) when the limit of x 0 exists, f may or may not be continuous at x 0 f (x) = x when (, 3) (3, + ) and f (3) = 9. f (x) is not continuous at 3.

Differentiability and Continuity continuous: f (x 0 ) = lim h 0 f (x 0 + h) when the limit of x 0 exists, f may or may not be continuous at x 0 f (x) = x when (, 3) (3, + ) and f (3) = 9. f (x) is not continuous at 3.

Differentiability and Continuity continuous: f (x 0 ) = lim h 0 f (x 0 + h) when the limit of x 0 exists, f may or may not be continuous at x 0 f (x) = x when (, 3) (3, + ) and f (3) = 9. f (x) is not continuous at 3.

Higher-Order Derivatives f (x 0 ) or d dx ( df dx )(x 0) = d 2 f dx 2 (x 0 ) Example 2.9: the derivative of the function f (x) = x 3 + 3x 2 + 3x + 1 is f (x) = 3x 2 + 6x + 3 f (x) = 6x + 6

Higher-Order Derivatives f (x 0 ) or d dx ( df dx )(x 0) = d 2 f dx 2 (x 0 ) Example 2.9: the derivative of the function f (x) = x 3 + 3x 2 + 3x + 1 is f (x) = 3x 2 + 6x + 3 f (x) = 6x + 6

Higher-Order Derivatives f (x 0 ) or d dx ( df dx )(x 0) = d 2 f dx 2 (x 0 ) Example 2.9: the derivative of the function f (x) = x 3 + 3x 2 + 3x + 1 is f (x) = 3x 2 + 6x + 3 f (x) = 6x + 6

Higher-Order Derivatives twice continuously differentiable: If f has a second derivative everywhere, then f is a well defined function of x. And if f is a continuous function of x, then f is twice continuously differentiable, denoted by C 2 C 1, C n, C

Higher-Order Derivatives twice continuously differentiable: If f has a second derivative everywhere, then f is a well defined function of x. And if f is a continuous function of x, then f is twice continuously differentiable, denoted by C 2 C 1, C n, C

Approximation by Differentials f (x 0 ) = dy dx y x y = f (x 0 + x) f (x 0 ) f (x 0 ) x f (x 0 + x) f (x 0 ) + f (x 0 ) x Figure 2.15 (page 37)

Approximation by Differentials f (x 0 ) = dy dx y x y = f (x 0 + x) f (x 0 ) f (x 0 ) x f (x 0 + x) f (x 0 ) + f (x 0 ) x Figure 2.15 (page 37)

Approximation by Differentials f (x 0 ) = dy dx y x y = f (x 0 + x) f (x 0 ) f (x 0 ) x f (x 0 + x) f (x 0 ) + f (x 0 ) x Figure 2.15 (page 37)

Approximation by Differentials f (x 0 ) = dy dx y x y = f (x 0 + x) f (x 0 ) f (x 0 ) x f (x 0 + x) f (x 0 ) + f (x 0 ) x Figure 2.15 (page 37)

Example Example 2.11 Production function F(x) = 1 2 x 100 units of labor input x its output is 5 units. The derivative of the production F at x = 100, F (100) = 1 4 100 1/2 = 1 40 = 0.025,

Example F (100) is a good measure of the marginal product of labor the actual increase in output is F (101) F (100) = 0.02494, pretty close to 0.025.