Review Answers for E&CE 700T02

Similar documents
Extremal graph theory II: K t and K t,t

Supplement for SADAGRAD: Strongly Adaptive Stochastic Gradient Methods"

CSE 202: Design and Analysis of Algorithms Lecture 16

Lecture 9: Polynomial Approximations

Calculus BC 2015 Scoring Guidelines

Big O Notation for Time Complexity of Algorithms

Moment Generating Function

ECE-314 Fall 2012 Review Questions

Ideal Amplifier/Attenuator. Memoryless. where k is some real constant. Integrator. System with memory

Lecture 8 April 18, 2018

1 Notes on Little s Law (l = λw)

BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS

STK4080/9080 Survival and event history analysis

Union-Find Partition Structures

Section 8 Convolution and Deconvolution

Union-Find Partition Structures Goodrich, Tamassia Union-Find 1

L-functions and Class Numbers

MATH 507a ASSIGNMENT 4 SOLUTIONS FALL 2018 Prof. Alexander. g (x) dx = g(b) g(0) = g(b),

Online Supplement to Reactive Tabu Search in a Team-Learning Problem

Lecture 15: Three-tank Mixing and Lead Poisoning

An interesting result about subset sums. Nitu Kitchloo. Lior Pachter. November 27, Abstract

Outline. simplest HMM (1) simple HMMs? simplest HMM (2) Parameter estimation for discrete hidden Markov models

Department of Mathematical and Statistical Sciences University of Alberta

Calculus Limits. Limit of a function.. 1. One-Sided Limits...1. Infinite limits 2. Vertical Asymptotes...3. Calculating Limits Using the Limit Laws.

The Eigen Function of Linear Systems

λiv Av = 0 or ( λi Av ) = 0. In order for a vector v to be an eigenvector, it must be in the kernel of λi

Notes 03 largely plagiarized by %khc

S n. = n. Sum of first n terms of an A. P is

INVESTMENT PROJECT EFFICIENCY EVALUATION

OLS bias for econometric models with errors-in-variables. The Lucas-critique Supplementary note to Lecture 17

th m m m m central moment : E[( X X) ] ( X X) ( x X) f ( x)

10.3 Autocorrelation Function of Ergodic RP 10.4 Power Spectral Density of Ergodic RP 10.5 Normal RP (Gaussian RP)

2 f(x) dx = 1, 0. 2f(x 1) dx d) 1 4t t6 t. t 2 dt i)

ACCESS TO SCIENCE, ENGINEERING AND AGRICULTURE: MATHEMATICS 1 MATH00030 SEMESTER / Statistics

Actuarial Society of India

Electrical Engineering Department Network Lab.

F.Y. Diploma : Sem. II [AE/CH/FG/ME/PT/PG] Applied Mathematics

F.Y. Diploma : Sem. II [CE/CR/CS] Applied Mathematics

Fresnel Dragging Explained

FORBIDDING HAMILTON CYCLES IN UNIFORM HYPERGRAPHS

Section 5.5. Infinite Series: The Ratio Test

# fixed points of g. Tree to string. Repeatedly select the leaf with the smallest label, write down the label of its neighbour and remove the leaf.

Some Properties of Semi-E-Convex Function and Semi-E-Convex Programming*

Taylor Series (BC Only)

A Note on Random k-sat for Moderately Growing k

Comparison between Fourier and Corrected Fourier Series Methods

xp (X = x) = P (X = 1) = θ. Hence, the method of moments estimator of θ is

Pure Math 30: Explained!

Economics 8723 Macroeconomic Theory Problem Set 2 Professor Sanjay Chugh Spring 2017

Lecture 15 First Properties of the Brownian Motion

CSE 241 Algorithms and Data Structures 10/14/2015. Skip Lists

Solution. 1 Solutions of Homework 6. Sangchul Lee. April 28, Problem 1.1 [Dur10, Exercise ]

N! AND THE GAMMA FUNCTION

AP Calculus BC Review Applications of Derivatives (Chapter 4) and f,

COS 522: Complexity Theory : Boaz Barak Handout 10: Parallel Repetition Lemma

Review Exercises for Chapter 9

FIXED FUZZY POINT THEOREMS IN FUZZY METRIC SPACE

Edge-disjoint rainbow spanning trees in complete graphs

ECE 350 Matlab-Based Project #3

Solutions to selected problems from the midterm exam Math 222 Winter 2015

C(p, ) 13 N. Nuclear reactions generate energy create new isotopes and elements. Notation for stellar rates: p 12

K3 p K2 p Kp 0 p 2 p 3 p

Section 11.8: Power Series

Mathematical Statistics. 1 Introduction to the materials to be covered in this course

Energy Density / Energy Flux / Total Energy in 1D. Key Mathematics: density, flux, and the continuity equation.

ODEs II, Supplement to Lectures 6 & 7: The Jordan Normal Form: Solving Autonomous, Homogeneous Linear Systems. April 2, 2003

Introduction to Mobile Robotics Mapping with Known Poses

Towards Efficiently Solving Quantum Traveling Salesman Problem

Power Bus Decoupling Algorithm

Institute of Actuaries of India

EGR 544 Communication Theory

NEWTON METHOD FOR DETERMINING THE OPTIMAL REPLENISHMENT POLICY FOR EPQ MODEL WITH PRESENT VALUE

6/10/2014. Definition. Time series Data. Time series Graph. Components of time series. Time series Seasonal. Time series Trend

FOR 496 / 796 Introduction to Dendrochronology. Lab exercise #4: Tree-ring Reconstruction of Precipitation

Sampling Example. ( ) δ ( f 1) (1/2)cos(12πt), T 0 = 1

A TAUBERIAN THEOREM FOR THE WEIGHTED MEAN METHOD OF SUMMABILITY

6.003: Signals and Systems Lecture 20 April 22, 2010

Math 6710, Fall 2016 Final Exam Solutions

e to approximate (using 4

Optimization Methods MIT 2.098/6.255/ Final exam

King Fahd University of Petroleum & Minerals Computer Engineering g Dept

LIMITS OF FUNCTIONS (I)

Inventory Optimization for Process Network Reliability. Pablo Garcia-Herreros

Week 8 Lecture 3: Problems 49, 50 Fourier analysis Courseware pp (don t look at French very confusing look in the Courseware instead)

More Digital Logic. t p output. Low-to-high and high-to-low transitions could have different t p. V in (t)

Edge-disjoint rainbow spanning trees in complete graphs

Procedia - Social and Behavioral Sciences 230 ( 2016 ) Joint Probability Distribution and the Minimum of a Set of Normalized Random Variables

Solutions for the Exam 9 January 2012

Continuous Functions

f x x c x c x c... x c...

Order Determination for Multivariate Autoregressive Processes Using Resampling Methods

Chapter 6 - Work and Energy

RCT Worksheets/Quizzes 1.06 Radioactivity and Radioactive Decay

IP Reference guide for integer programming formulations.

A note on deviation inequalities on {0, 1} n. by Julio Bernués*

It is often useful to approximate complicated functions using simpler ones. We consider the task of approximating a function by a polynomial.

3.1.3 INTRODUCTION TO DYNAMIC OPTIMIZATION: DISCRETE TIME PROBLEMS. A. The Hamiltonian and First-Order Conditions in a Finite Time Horizon

1. Solve by the method of undetermined coefficients and by the method of variation of parameters. (4)

Problems and Solutions for Section 3.2 (3.15 through 3.25)

Economics 8723 Macroeconomic Theory Problem Set 3 Sketch of Solutions Professor Sanjay Chugh Spring 2017

Transcription:

Review Aswers for E&CE 700T0

. Deermie he curre soluio, all possible direcios, ad sepsizes wheher improvig or o for he simple able below: 4 b ma c 0 0 0-4 6 0 - B N B N ^0 0 0 curre sol =, = Ch for - - 0 * Ch for 4 0-5 improvig direcio Ch obj f is 0, lambda = /5 ^ /5 0 0 /5 B N N B,4 are basis variables * OTHER DIRECTION IS NOT IMROVING BUT OBTAINS LAMBDA =/ WITH X,X BASIS VARIABLE

. Compue opimal soluio o : ma z z such ha -z z <= zz<=6 z<=4 z,z>=0 Sol: z=4, z=

.Compue opimal soluio o: ma 45 such ha <= <= 4<= 45<= 5<= as. == 5=0.5

4. Fid he miimal cos spaig ree for he graph below where values o edges represe he coss. 4 4 5 As. 8 see bold black edges, Noe: oher ses of edges are also possible

5. Fid he maimum flow from s o of he graph below. Each forward s o arc Has capaciy of uless labeled oherwise. s,, As. Ma Flow =5

6.Fid maimum flow for give flows ad capaciies labelled o he graph below flow, capaciy. As. Ma flow = 9,4 S 5,5,6 0,, T 4,,5,5,,7 6,4,6

7. Give he iiial machig o he graph, provide he H ree Ad he e machig, usig he maimum machig algorihm. use curvy lies o ideify he e machig o he graph. X 4 5 6 y5 6 y y y y4 y5 y6 ah o flip is 4-y6--y-6-y5 5 y y y4 y y6 4

8. Usig shores pah algorihm used durig lecures, ideify all Shores pahs from ode u o all oher odes i he graph. Specifically Defie S={u}, S={u, }, S={u,, } hrough he shores pah algorihm a b Sages. 5 7 f 4 u g c 4 6 e d S={u}={u,a}={u,a,e}={u,a,e,f,d,b}={u,a,e,f,d,b,c}={u,a,e,f,d,b,c,g}

9. You are give a se of M asks, =,,,M ad N processors, =,, N. each ask mus be assiged o oe processor ad each rocessor ca be assiged a mos wo asks. Assume Represes he proposiio ha ask is assiged o processor use biary variables, a Formulae he followig cosrai: b Formulae he followig cosrai:, i i j, i,, j j, c Give a formulaio of he objecive fucio which is o miimize The umber of processors beig used. You may defie ew cosrais Or variables as required

,,,,,,, j i j i j i,,,,,,,,,,, j i i j i d b a c b a d c b a d c b a d c b a j i j i a b

c We have oly biary variables: we iroduce a ew biary variable: p which isif processor has a leas oe ask assiged o i, else i is zero., ow we ca creae he objecive fucio Mi opioally, we could add,, N p N p p p however his is o ecessary sice our objecive fucio is creaig his for us.,

0. Give a good I formulaio for he followig problem: The problem is he maimum cardialiy ode packig problem O he graph below cosraied wih he followig iequaliy: i = 0 bi i i= 7 4, b = {,5,, } 5 7 6 4

i 0 = bi i i= 7 5 5y 5y C C C C 4 8 y y = {,}, is a face. = {,}, is a face = {,4}, is a face = {,,4}, we do o kow if i is a face or o. Faces : 4, b = {,5,, } 9 y y 8 7 9 0 y y 4, 8 4, le y 4 6 4 7, 8 4 = 0 0 Is a kapsack iequaliy So we ca geerae faces or Sroger iequaliies o improve The formulaio 5 6 4 7 We ca use graph o erac ode packig Faces/iequaliies i 4 6 0, 5 7, ec, clique faces lifig lemma for odd cycles 4 5,

. Describe how you would solve he followig problem i geeral: Fid he miimum eecuio ime for processor o eecue a se of ask defied usig a direced acyclic graph, where arcs represe daa rasfers bewee asks. Assume oe ask a a ime ca be eecued o each processor, asks ca be eecued i parallel oe differe processors, ad here is o commuicaio delay. all asks have he same duraio=>maimum machig o complemeary ask graph

. Reformulae he followig opimizaio problem, so ha oe ca impleme i wih a L solver which oly suppors mi or ma objecive fucios o he mima objecive below Mi A b Ma a i j j i, j

Mi Ma A b MiZ j a i, j A b j i j a i, j cosider removig he ma Z, i j i par, reformulaed problem is

. For shores pah we have, { f s C } Miimum s, s s 4. Eplai he differece bewee simulaed aealig ad abu search: abu search acceps o-improvig moves, whereas i simulaed aealig o-improvig moves are acceped oly wih probabiliy calculaios. There may be cyclig i simulaed aealig bu o i abu search.