Mid-Term Examination - Spring 2014 Mathematical Programming with Applications to Economics Total Score: 45; Time: 3 hours

Similar documents
CS 491G Combinatorial Optimization Lecture Notes

Solutions for HW9. Bipartite: put the red vertices in V 1 and the black in V 2. Not bipartite!

CIT 596 Theory of Computation 1. Graphs and Digraphs

Graph Theory. Simple Graph G = (V, E). V={a,b,c,d,e,f,g,h,k} E={(a,b),(a,g),( a,h),(a,k),(b,c),(b,k),...,(h,k)}

Counting Paths Between Vertices. Isomorphism of Graphs. Isomorphism of Graphs. Isomorphism of Graphs. Isomorphism of Graphs. Isomorphism of Graphs

Solutions to Problem Set #1

18.06 Problem Set 4 Due Wednesday, Oct. 11, 2006 at 4:00 p.m. in 2-106

Logic, Set Theory and Computability [M. Coppenbarger]

Graph Algorithms. Vertex set = { a,b,c,d } Edge set = { {a,c}, {b,c}, {c,d}, {b,d}} Figure 1: An example for a simple graph

1 PYTHAGORAS THEOREM 1. Given a right angled triangle, the square of the hypotenuse is equal to the sum of the squares of the other two sides.

On a Class of Planar Graphs with Straight-Line Grid Drawings on Linear Area

Section 1.3 Triangles

Algebra 2 Semester 1 Practice Final

22: Union Find. CS 473u - Algorithms - Spring April 14, We want to maintain a collection of sets, under the operations of:

CS311 Computational Structures Regular Languages and Regular Grammars. Lecture 6

Section 2.3. Matrix Inverses

Lecture 6: Coding theory

arxiv: v2 [math.co] 31 Oct 2016

Exercise sheet 6: Solutions

Lecture 2: Cayley Graphs

Discrete Structures Lecture 11

Answers and Solutions to (Some Even Numbered) Suggested Exercises in Chapter 11 of Grimaldi s Discrete and Combinatorial Mathematics

On the Spectra of Bipartite Directed Subgraphs of K 4

Probability. b a b. a b 32.

Connectivity in Graphs. CS311H: Discrete Mathematics. Graph Theory II. Example. Paths. Connectedness. Example

Numbers and indices. 1.1 Fractions. GCSE C Example 1. Handy hint. Key point

Part 4. Integration (with Proofs)

April 8, 2017 Math 9. Geometry. Solving vector problems. Problem. Prove that if vectors and satisfy, then.

CS261: A Second Course in Algorithms Lecture #5: Minimum-Cost Bipartite Matching

Monochromatic Plane Matchings in Bicolored Point Set

T b a(f) [f ] +. P b a(f) = Conclude that if f is in AC then it is the difference of two monotone absolutely continuous functions.

Lecture 3 ( ) (translated and slightly adapted from lecture notes by Martin Klazar)

Maximum size of a minimum watching system and the graphs achieving the bound

Lecture 4: Graph Theory and the Four-Color Theorem

QUADRATIC EQUATION. Contents

50 AMC Lectures Problem Book 2 (36) Substitution Method

AP Calculus AB Unit 4 Assessment

Lecture 11 Binary Decision Diagrams (BDDs)

6.5 Improper integrals

September 30, :24 WSPC/Guidelines Y4Spanner

Green s Theorem. (2x e y ) da. (2x e y ) dx dy. x 2 xe y. (1 e y ) dy. y=1. = y e y. y=0. = 2 e

MATH 409 Advanced Calculus I Lecture 22: Improper Riemann integrals.

Nondeterministic Automata vs Deterministic Automata

The vertex leafage of chordal graphs

Section 4.4. Green s Theorem

If the numbering is a,b,c,d 1,2,3,4, then the matrix representation is as follows:

Proportions: A ratio is the quotient of two numbers. For example, 2 3

Lecture 8: Abstract Algebra

Mathematical Proofs Table of Contents

APPENDIX. Precalculus Review D.1. Real Numbers and the Real Number Line

First Midterm Examination

Discrete Structures, Test 2 Monday, March 28, 2016 SOLUTIONS, VERSION α

Vidyalankar S.E. Sem. III [CMPN] Discrete Structures Prelim Question Paper Solution

Prefix-Free Regular-Expression Matching

Bisimulation, Games & Hennessy Milner logic

Project 6: Minigoals Towards Simplifying and Rewriting Expressions

NON-DETERMINISTIC FSA

16z z q. q( B) Max{2 z z z z B} r z r z r z r z B. John Riley 19 October Econ 401A: Microeconomic Theory. Homework 2 Answers

QUADRATIC EQUATION EXERCISE - 01 CHECK YOUR GRASP

Graph width-parameters and algorithms

First Midterm Examination

Linear choosability of graphs

CSE 332. Sorting. Data Abstractions. CSE 332: Data Abstractions. QuickSort Cutoff 1. Where We Are 2. Bounding The MAXIMUM Problem 4

MAT 403 NOTES 4. f + f =

Comparing the Pre-image and Image of a Dilation

The University of Nottingham SCHOOL OF COMPUTER SCIENCE A LEVEL 2 MODULE, SPRING SEMESTER MACHINES AND THEIR LANGUAGES ANSWERS

New and Improved Spanning Ratios for Yao Graphs

MULTIPLE CHOICE QUESTIONS

a) Read over steps (1)- (4) below and sketch the path of the cycle on a P V plot on the graph below. Label all appropriate points.

Subsequence Automata with Default Transitions

Surds and Indices. Surds and Indices. Curriculum Ready ACMNA: 233,

I 3 2 = I I 4 = 2A

y = c 2 MULTIPLE CHOICE QUESTIONS (MCQ's) (Each question carries one mark) is...

System Validation (IN4387) November 2, 2012, 14:00-17:00

Introduction to Olympiad Inequalities

A Lower Bound for the Length of a Partial Transversal in a Latin Square, Revised Version

Graph States EPIT Mehdi Mhalla (Calgary, Canada) Simon Perdrix (Grenoble, France)

CHAPTER 4: DETERMINANTS

SOME COPLANAR POINTS IN TETRAHEDRON

for all x in [a,b], then the area of the region bounded by the graphs of f and g and the vertical lines x = a and x = b is b [ ( ) ( )] A= f x g x dx

A Primer on Continuous-time Economic Dynamics

CS 330 Formal Methods and Models Dana Richards, George Mason University, Spring 2016 Quiz Solutions

Directed acyclic graphs with the unique dipath property

Necessary and sucient conditions for some two. Abstract. Further we show that the necessary conditions for the existence of an OD(44 s 1 s 2 )

arxiv: v1 [cs.cg] 28 Apr 2009

Can one hear the shape of a drum?

A Study on the Properties of Rational Triangles

Boolean Algebra cont. The digital abstraction

Harvard University Computer Science 121 Midterm October 23, 2012

Technische Universität München Winter term 2009/10 I7 Prof. J. Esparza / J. Křetínský / M. Luttenberger 11. Februar Solution

Graph Theory. Dr. Saad El-Zanati, Faculty Mentor Ryan Bunge Graduate Assistant Illinois State University REU. Graph Theory

CHENG Chun Chor Litwin The Hong Kong Institute of Education

n f(x i ) x. i=1 In section 4.2, we defined the definite integral of f from x = a to x = b as n f(x i ) x; f(x) dx = lim i=1

Solutions to Assignment 1

CS 573 Automata Theory and Formal Languages

Data Structures LECTURE 10. Huffman coding. Example. Coding: problem definition

Section 6.1 Definite Integral

On the properties of the two-sided Laplace transform and the Riemann hypothesis

Linear Algebra Introduction

Suppose we want to find the area under the parabola and above the x axis, between the lines x = 2 and x = -2.

Transcription:

Mi-Term Exmintion - Spring 0 Mthemtil Progrmming with Applitions to Eonomis Totl Sore: 5; Time: hours. Let G = (N, E) e irete grph. Define the inegree of vertex i N s the numer of eges tht re oming into i. Formlly, inegree(i) = {j N \ {i} : (j, i) E}. Similrly, efine the outegree of vertex i N s the numer of eges tht re oming out of i. Formlly, outegree(i) = {j N \ {i} : (i, j) E}. Suppose G stisfies the property tht for every vertex i N, outegree(i) = inegree(i). Tke pir of verties s, t N n suppose there re k ege-isjoint pths from s to t in G. Does G lso ontin k ege-isjoint pths from t to s? Prove this or provie ounterexmple. (5 mrks) Answer. We onsier ny s t ut (S, N \ S) of the grph G. Let C + = {(i, j) E : i S, j N \ S} n C = {(i, j) E : i N \ S, j S}. Now, inegree(k) = {i N : (i, k) E} = {i S : (i, k) E} + C. Similrly, outegree(k) = {i N : (k, i) E} = {i S : (k, i) E} + C +. Using the ft tht {i S : (i, k) E} = {i S : (k, i) E} n inegree(k) = outegree(k), we get C+ = C. Now, sine there re k ege-isjoint pths from s to t, there is ut suh tht C + = k. But for the sme ut C = k. This implies tht we n remove this set of k eges in C n there will e no pth from t to s. By Menger s theorem, the numer of isjoint pths from t to s is k.

. Consier the following system of inequlities. x x x x x x x x x x x x x x x x. () Construt the unerlying weighte irete grph of this system of inequlities suh tht the potentils of this grph orrespons to solution of this system. ( mrks) Answer. The unerlying irete grph hs four noes orresponing to eh of the four vriles. This is shown in Figure. Figure : Direte grph () Use this to onlue if this system of inequlities hs solution. If yes, fin solution or rgue why it oes not hve solution. (7 mrks) Answer. Sine there re no yles of negtive length in the unerlying irete grph, the system of inequlities hs solution. One solution n e foun y setting, sy, x = 0, n tking shortest pth from the orresponing to x to every other noe. In prtiulr, x =, x =, x = 5.

. Suppose G = (N, E, w) is weighte irete omplete grph. Further, suppose tht the weights in G stisfy the following tringle inequlity: for every i, j, k N we hve w(i, j) + w(j, k) w(i, k). Show tht G stisfies potentil equivlene if n only if w(i, j) = w(j, i) for every i, j N. (5 mrks) Answer. Fix ny pir of noes i, j N. Let shortest pth from i to j e (i, i,..., i k, j). Then, pplying tringle inequlity suessively, we get s(i, j) = w(i, i ) + w(i, i ) +... + w(i k, i k ) + w(i k, j) w(i, i ) + w(i, i ) +... + w(i k, i k ) + w(i k, j)... w(i, j). But w(i, j) s(i, j) implies tht w(i, j) = s(i, j). Now, potentil equivlene is equivlent to requiring s(i, j)+s(j, i) = 0 for ll i, j N. Hene, potentil equivlene hols if n only if w(i, j) + w(j, i) = 0 for ll i, j N.. Let G = (N, E) e n unirete iprtite grph, i.e., there exists prtition B H of N suh tht for every {i, j} E, we hve i B n j H. We sy iprtite grph G is k-regulr, where k is positive integer, if egree of every vertex is k. () Show tht if G is k-regulr iprtite grph, then B = H. ( mrks) Answer. Sine G is k-regulr, the sum of egree of verties on B sie is k B, whih is equl to the sum of egree of verties on H sie. Hene, k B = k H. This implies tht B = H. () Show tht if G is k-regulr iprtite grph, then the size of mximum mthing in G is B. (8 mrks) Answer. Let C e minimum vertex over. Clerly, B is vertex over. Hene, C B. Sine eh vertex hs egree k, y efinition of vertex over, k C k B. Hene, C = B. So, B is minimum vertex over. By Konig s theorem, the size of mximum mthing in G is B. 5. Let G = (N, E, w) e weighte unirete grph tht is onnete. Let C = (i, i,...,i k, i ) e yle of G. Let {i, i } e the unique mximum weight ege in yle C. Similrly, let {i, i } e the unique minimum weight ege in yle C. () Show tht {i, i } will not elong to ny MCST of G or give ounterexmple. (5 mrks)

Answer. Assume for ontrition tht {i, i } elongs to some MCST T of G. Hene, some ege {i j, i j+ } in C oes not elong to this MCST. Aing {i j, i j+ ) retes unique yle whih is roken y removing {i, i }. Sine the new grph ontins N eges, this is lso spnning tree. But the weight of {i j, i j+ ) is stritly less thn {i, i } implies tht T is not n MCST, ontrition. () Show tht {i, i } will elong to every MCST of G or give ounterexmple. (5 mrks) Answer. Figure gives ounterexmple, where the MCST is shown in rk eges ut the ege {, } is not prt of the MCST. Notie tht {, } is the minimum weight ege of the yle (,,, ). Figure : A ounterexmple 6. Fin mximum mthing n minimum vertex over of the unirete grph in Figure. (5 mrks) e f g Figure : An unirete grph Answer. The mximum mthing n minimum vertex over for the grph in Figure is shown in Figure in rk. Clerly, the shown eges onstitute mthing. It is

lso mximum mthing sine the mximum mthing nnot e of size greter thn. Further, the shown verties ( in numer) is minimum vertex over euse of Konig s theorem. f e g Figure : Mximum mthing n minimum vertex over 5