ECON Mathematical Economics - ANSWERS FINAL EXAM. 1. (a)- The consumer's utility maximization problem is written as:

Similar documents
constraint. Let us penalize ourselves for making the constraint too big. We end up with a

Algebra Unpacked Content For the new Common Core standards that will be effective in all North Carolina schools in the school year.

PUTNAM TRAINING POLYNOMIALS. Exercises 1. Find a polynomial with integral coefficients whose zeros include

Lagrange Interpolation is a method of fitting an equation to a set of points that functions well when there are few points given.

Constrained optimization.

Linear and quadratic Taylor polynomials for functions of several variables.

Matrix Algebra. Some Basic Matrix Laws. Before reading the text or the following notes glance at the following list of basic matrix algebra laws.

BX in ( u, v) basis in two ways. On the one hand, AN = u+

Question 2: How do you solve a matrix equation using the matrix inverse?

This unit will lay the groundwork for later units where the students will extend this knowledge to quadratic and exponential functions.

1 Lecture: Integration of rational functions by decomposition

Linear Programming Notes V Problem Transformations

Lecture 5 Principal Minors and the Hessian

Name: ID: Discussion Section:

Solving simultaneous equations using the inverse matrix

In this section, we will consider techniques for solving problems of this type.

Math 53 Worksheet Solutions- Minmax and Lagrange

Practice with Proofs

DERIVATIVES AS MATRICES; CHAIN RULE

Operation Count; Numerical Linear Algebra

Study Guide 2 Solutions MATH 111

1 Solving LPs: The Simplex Algorithm of George Dantzig

Chapter 4, Arithmetic in F [x] Polynomial arithmetic and the division algorithm.

Some Lecture Notes and In-Class Examples for Pre-Calculus:

MA107 Precalculus Algebra Exam 2 Review Solutions

Representation of functions as power series

SOLUTIONS. f x = 6x 2 6xy 24x, f y = 3x 2 6y. To find the critical points, we solve

1 3 4 = 8i + 20j 13k. x + w. y + w

ALGEBRA REVIEW LEARNING SKILLS CENTER. Exponents & Radicals

Increasing for all. Convex for all. ( ) Increasing for all (remember that the log function is only defined for ). ( ) Concave for all.

Partial Fractions. (x 1)(x 2 + 1)

minimal polyonomial Example

8 Square matrices continued: Determinants

Homework # 3 Solutions

Alternative proof for claim in [1]

3. Let A and B be two n n orthogonal matrices. Then prove that AB and BA are both orthogonal matrices. Prove a similar result for unitary matrices.

TOPIC 4: DERIVATIVES

I. GROUPS: BASIC DEFINITIONS AND EXAMPLES

Undergraduate Notes in Mathematics. Arkansas Tech University Department of Mathematics

Factoring Polynomials

(a) We have x = 3 + 2t, y = 2 t, z = 6 so solving for t we get the symmetric equations. x 3 2. = 2 y, z = 6. t 2 2t + 1 = 0,

The Characteristic Polynomial

Introduction to Algebraic Geometry. Bézout s Theorem and Inflection Points

3.1. RATIONAL EXPRESSIONS

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS

3.3. Solving Polynomial Equations. Introduction. Prerequisites. Learning Outcomes

6 EXTENDING ALGEBRA. 6.0 Introduction. 6.1 The cubic equation. Objectives

MATH PROBLEMS, WITH SOLUTIONS

Nonlinear Programming Methods.S2 Quadratic Programming

Math 115A HW4 Solutions University of California, Los Angeles. 5 2i 6 + 4i. (5 2i)7i (6 + 4i)( 3 + i) = 35i + 14 ( 22 6i) = i.

1.5. Factorisation. Introduction. Prerequisites. Learning Outcomes. Learning Style

1 if 1 x 0 1 if 0 x 1

36 CHAPTER 1. LIMITS AND CONTINUITY. Figure 1.17: At which points is f not continuous?

Chapter 5. Rational Expressions

British Columbia Institute of Technology Calculus for Business and Economics Lecture Notes

Differentiation and Integration

Many algorithms, particularly divide and conquer algorithms, have time complexities which are naturally

Section 2.7 One-to-One Functions and Their Inverses

Taylor and Maclaurin Series

8.2. Solution by Inverse Matrix Method. Introduction. Prerequisites. Learning Outcomes

Factoring Trinomials: The ac Method

a 1 x + a 0 =0. (3) ax 2 + bx + c =0. (4)

THE FUNDAMENTAL THEOREM OF ALGEBRA VIA PROPER MAPS

Solutions Of Some Non-Linear Programming Problems BIJAN KUMAR PATEL. Master of Science in Mathematics. Prof. ANIL KUMAR

WARM UP EXERCSE. 2-1 Polynomials and Rational Functions

3. INNER PRODUCT SPACES

MATH APPLIED MATRIX THEORY

Factoring Polynomials and Solving Quadratic Equations

October 3rd, Linear Algebra & Properties of the Covariance Matrix

15 Kuhn -Tucker conditions

Vector and Matrix Norms

Chapter 12 Modal Decomposition of State-Space Models 12.1 Introduction The solutions obtained in previous chapters, whether in time domain or transfor

Determinants can be used to solve a linear system of equations using Cramer s Rule.

ab = c a If the coefficients a,b and c are real then either α and β are real or α and β are complex conjugates

CHAPTER SIX IRREDUCIBILITY AND FACTORIZATION 1. BASIC DIVISIBILITY THEORY

SECOND DERIVATIVE TEST FOR CONSTRAINED EXTREMA

Partial Fractions Examples

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 2. x n. a 11 a 12 a 1n b 1 a 21 a 22 a 2n b 2 a 31 a 32 a 3n b 3. a m1 a m2 a mn b m

26. Determinants I. 1. Prehistory

Notes on Determinant

Eigenvalues, Eigenvectors, Matrix Factoring, and Principal Components

= = 3 4, Now assume that P (k) is true for some fixed k 2. This means that

it is easy to see that α = a

Nonlinear Algebraic Equations. Lectures INF2320 p. 1/88

Linear Threshold Units

Calculus 1: Sample Questions, Final Exam, Solutions

QUADRATIC EQUATIONS EXPECTED BACKGROUND KNOWLEDGE

Constrained Optimisation

REVIEW EXERCISES DAVID J LOWRY

Answer Key for California State Standards: Algebra I

Matrices 2. Solving Square Systems of Linear Equations; Inverse Matrices

Continued Fractions and the Euclidean Algorithm

1.1 Practice Worksheet

Thnkwell s Homeschool Precalculus Course Lesson Plan: 36 weeks

Zeros of Polynomial Functions

Abstract: We describe the beautiful LU factorization of a square matrix (or how to write Gaussian elimination in terms of matrix multiplication).

MATH 425, PRACTICE FINAL EXAM SOLUTIONS.

The last three chapters introduced three major proof techniques: direct,

Orthogonal Projections

is identically equal to x 2 +3x +2

Transcription:

ECON 331 - Mathematical Economics - ANSWERS FINAL EXAM 1. a- The consumer's utility maximization problem is written as: max x,y [xy + y2 + 2x + 2y] s.t. 6x + 10y = m, x 0, y 0 - The associated Lagrangian function is dened as: Lx, y, λ = xy + y 2 + 2x + 2y λ [6x + 10y m] where λ is the Lagrange multiplier. - The Kuhn-Tucker conditions are written as: Lx, y, λ Lx, y, λ i x = 0, x 0, = y + 2 6λ 0 ii Lx, y, λ Lx, y, λ y = 0, y 0, = x + 2y + 2 10λ 0 iii Lx, y, λ = [6x + 10y m] = 0 λ - From iii: x = m/6 5/3y *. - Suppose y = 0. From * x = m/6 > 0 L = 0 λ = 1/3. Plug λ and x in ii: you nd L = m/6 4/3 0. However, remember that 0 < m < 40 0 < m/6 4/3 which is a contradiction. - Suppose y > 0 L = 0 x + 2y + 2 10λ = 0 **. Suppose also x = 0. Then from * y = m/10. And from ** λ = m/50 + 1/5. Plug y and λ in i. You nd L = m/50 + 4/5 0. However, remember that 8 < m < 40 0 < m/50 + 4/5 which is a contradiction. So suppose instead x > 0 L = 0 y = 6λ 2. And from * x = m/6 10λ+10/3. Plug x and y in ** and solve for λ. You nd: x = 40 m/24 > 0 b/c m < 40 y = m 8/8 > 0 b/c m > 8 λ = m + 8/48 NB: alternatively, you can directly plug the constraint into the objective function say by replacing y by a function of x. You end up with an optimization problem with one choice variable, x and one non-negativity constraint. b We have seen a result in class that states: Ux, y m = λ 1

We check by calculation that it works here: Ux, y = 40 m m 8 2 m 8 + + 40 m 24 8 8 12 Ux, y = 1 m 48 m + 1 = λ 6 Ux, y = 7 m m=20 12 + m 8 4 = 1 96 m2 + 1 6 m + 2 3 c - case where 0 m 8: previous solution does not work because it violates the positivity of x. So we need to study the behavior of U on the boundary.. x = 0 and y > 0: U0, y = y 2 + 2y with y > 0 and 10y = m. So y = m/10 and U 1 = m 2 /100 + m/5.. x > 0 and y = 0: Ux, 0 = 2x with x > 0 and 6x = m. So x = m/6 and U 2 = m/3 We can check that U 1 < U 2 when m 8, so the solution is: x = m/6 and y = 0. - case where m 40: similar study needs to be performed as the positivity of y found in b is violated:. same 2 cases and associated solutions! This time, U 1 > U 2 when m 40, hence the solution is x = 0 and y = m/10. 2. a I n + sm = I n + M 1 I n + smi n + M = I n I n + M + sm + sm 2 = I n 1 + sm + s3m = 0 b/c by assumption M 2 = 3M 1 + 4sM = 0 1 + 4s = 0 or M = 0 s = 1 4 or M = 0 To conclude, for any matrix M s.t. M 2 = 3M, I n M/4 is the inverse matrix of I n + M. b i AXB = XB + C AX XB = C A I n X = CB 1 b/c B is invertible by ass. X = A I 1 CB 1 b/c A I n is invertible by ass. ii A I n = 1 1 1 1 ; A I n 1 = 1/2 1/2 1/2 1/2 apply the inverse formula for 2,2-matrices 2

1 0 0 and B 1 = 0 2 0 0 0 4 apply the inverse formula for diagonal matrices X = = = 1 1 1 1 0 2 3/2 1 1 1/2 0 4 6 1 2 2 1 1 2 1 3 1 1 0 0 0 2 0 0 0 4 1 0 0 0 2 0 0 0 4 3. - Taylor's approximation: we dene a function and Taylor provides a polynomial approximation of this function around a point of interest. Here I dene fx = x 1/3 and I want to approximate it around x 0 = 8. The second-order approximation is: fx f8 + f 8x 8 + 1 2 f 8x 8 2 f8.1 2 + 1 3 8 2/3 8.1 8 1 2 2 8 5/3 3 3 = 2 + 1 12 0.1 1 144 2 0.12 = 2 + 0.008333 0.000035 = 2.0083 8.1 8 2 - Newton's algorithm: we dene an equation that has solution at x = 8.1 1/3 and Newton's provides an approximate solution. Here I dene gx = x 3 8.1 and the equation gx = 0 has solution at x = 8.1 1/3. The algorithm is given by: x i+1 = x i gx i g x i whenever g 0 x 1 = 2 8 8.1 3 2 2 = 241 120 2.00833 x 2 = 241 120 241/1203 8.1 3 241/120 2 2.0083 3

4. a - We can build up the following table of revenues for successive years: Y ear 0 A Revenue 1 A pa = 1 pa 2 1 pa p[1 pa] = 1 pa[1 p] = 1 p 2 A. n. 1 p n A So the revenue in year n can be expressed as R n = 1 p n A. formula can be proved by induction - not requested here. - The company operates as long as the yearly revenue exceeds the yearly cost, ie as long as 1 p n A > K. So we need to nd the largest integer n representing the largest number of years that satises this equation. 1 p n A > K ln [1 p n A] > lnk K > 0!! n ln1 p + lna > lnk n ln1 p > lnk lna K n ln1 p > ln A n < ln K A 0 < 1 p < 1, so ln1 p < 0, so inequality changes!! ln1 p So n is the largest integer satisfying the above equation. b The total prot from year 0 to n is simply the dierence between the total revenue and the total cost from year 0 to n. The total revenue is the sum of the yearly prots from year 0 to n, ie: R = n i=0 n R i = [1 p i A] = i=0 [ n ] 1 p i A = 1 1 +1 pn A use the hint with x = 1 p 1 1 p = 1 1 +1 pn A p The cost is the same each year, so the total cost is simply: C = n + 1 K Finally, the total prot is: i=0 π = 1 1 +1 pn A n + 1 K p c i Plug the given values in the equation found in a to get: ln K A ln1 p = ln 5000000 7000000 = 16.65 ln0.98 4

n is the largest integer that is smaller than 16.65, so n = 16. ii The associated prot is: π = 1 0.9817 0.02 with n = 14, we get: π = 6.5 10 6. 7000000 17 5000000 = 16.74 10 6 5. a The coecient matrix of the system is: 1 3 4 A = 2 2 1 3 3 9 and its determinant is calculated through an expansion in co-factors according to the rst column other expansions according to any line or column is acceptable - same nal answer: 2 1 deta = 3 9 2 3 4 3 9 + 3 3 4 2 1 = 18 + 3 2 27 + 12 + 33 8 = 0 bi From a, the coecients matrix associated with the system has a zero-determinant. Hence, the system does not have a unique solution: it may have no solution or an innite number of solutions, but we cannot tell yet. It cannot be solved by matrix-inversion and the Cramer's rule cannot be used. ii We solve the system using the general Gaussian elimination method: x + 3y + 4z = b 1 2x + 2y + z = b 2 3x 3y 9z = b 3 x + 3y + 4z = b 1 4y 7z = b 2 2b 1 L 2 2L 1 12y 21z = b 3 3b 1 L 3 3L 1 x + 3y + 4z = b 1 y + 7/4z = b 1 /2 b 2 /4 L 2 /4 0 = b 3 3b 1 3b 2 + 6b 1 L 3 3L 2 The system has solutions if and only if the coecients b 1, b 2, and b 3 satisfy the last equation: 3b 1 3b 2 + b 3 = 0. 5

6. a The domain is dened by couples x, y that satisfy 2 equations: i x 2 +y 2 1; ii x 0. To sketch the domain, we use the hint! We know that couples x, y satisfying x 2 + y 2 = 1 lie on a circle with radius 1 and centre at 0,0. Naturally, the couples x, y satisfying i lie inside the circle with radius 1 and centre 0,0 you can also take a test point, say 1/2,1/2: 1/2 2 + 1/2 2 = 1/2 1. We also need to consider ii. Finally, the domain is the semi-circle as displayed in the gure below. b To nd the stationary points, we need to solve the rst-order conditions: gx,y = 0 gx,y = 0 3x 2 2x = 0 2y = 0 x3x 2 = 0 y = 0 x = 0 x = 2/3 or y = 0 y = 0 We have found 2 stationary points: 0,0 and 2/3,0. To classify them, we need to consider the second-order conditions. We calculate the second-order derivatives: g xxx, y = 6x 2; g yyx, y = 2; g xyx, y = g yxx, y = 0; hx, y = g xxx, yg yyx, y [g xyx, y] 2 = Evaluate the SOC at the 2 stationary points: - at 0,0: g xx0, 0 = 2 < 0; g yy0, 0 = 2 < 0; and h0, 0 = 4 > 0. So 0,0 is a local maximum point. - at 2/3,0: g xx2/3, 0 = 0; g yy2/3, 0 = 2 < 0; and h2/3, 0 = 0. So 2/3,0 is a saddle point. c We need to consider the behavior of g on the boundary. We decompose the boundary into 2 zones: - zone 1: x = 0, 1 < y < 1. The function g is rewritten as: g0, y = 3 y 2 ; this is function a quadratic function for 1 y 1. Its minimum is reached when y = ±1: the associated points are 0,-1 and 0,1 and the function equals 2. Its maximum is reached when y = 0: the associated point is 0,0 and we already know it is a local 6

maximum function equals 3. - zone 2: x 2 + y 2 = 1 and 1 x 0. The function g can be rewritten as: gx, y = 3 + x 3 x 2 1 x 2 = 2 + x 3 ; this function is strictly increasing for 0 x 1. Its minimum is reached when x = 0: so the point is 0,1 and the function equals 2; and the maximum is reached when x = 1: so the point is 1,0 and the function equals to 3. Conclusion: global minimum at 0,1 and 0,-1; global maximum at 0,0 and 1,0. 7