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School: UCLA
Course: Probability Theory
Probability Theory, Math 170b, Winter 2013, Toni Antunovi - Homework 3 c c From the textbook solve the problems 29, 30, 31, 32 and 33 from the Chapter 4. Solve the problems 1, 2, 4, 5 and 6 from the Chapter 4 additional exercises at http:/www.athenasc.com
School: UCLA
Sule Ozler Economics 12 Fall 2011 READ THE EXPLANATIONS IN BOLD LETTERS CAREFULLY! MID-TERM EXAM - GOOD LUCK! WRITE YOUR NAME AND ID# You will not be allowed to ask questions during the exam. This exam has three sections. Each section has multiple questio
School: UCLA
Course: Probability Theory
Probability Theory, Math 170b, Winter 2013, Toni Antunovi c c Homework 1 From the textbook solve the problems 1, 2, 3, 5, 6, 8, 11 and 14 at the end of the Chapter 4. And also the problems below: Problem 1. Give examples of (not independent) random variab
School: UCLA
Course: Mathemtcl Modeling
Math 142-2, Homework 1 Solutions April 7, 2014 Problem 34.4 Suppose the growth rate of a certain species is not constant, but depends in a known way on the temperature of its environment. If the temperature is known as a function of time, derive an expres
School: UCLA
Spring 2011- Professor Sule Ozler Name Economics 121 ID# FINAL EXAM WRITE YOUR NAME AND ID# No questions will be answered during the exam There will not be any bathroom breaks during the exam You are permitted to have only your writing equipment on your d
School: UCLA
Course: Probability Theory
Probability Theory, Math 170b, Winter 2013, Toni Antunovi - Homework 4 c c solutions From the textbook solve the problems 42, 43 b) and 44 b), c) from the Chapter 4. Solve the problems 26, 27 and 29 from the Chapter 4 and problem 1 from the Chapter 7 addi
School: UCLA
Chapter Review Exercises SOLUTION 811 The function whose value we want to approximate is f (x, y, z) = x 2 + y2 + z We use the linear approximation at the point (7, 5, 70), hence h = 7.1 - 7 = 0.1, k = 4.9 - 5 = -0.1, and l = 70.1 - 70 = 0.1. We g
School: UCLA
MULTIVARIABLE CALCULUS (MATH 32A) FALL 2004 LECTURE NOTES 10/1/04 Office Hours: MS 7344, 11:00 12:40 Mon., Wed., Fri. E-mail: lchayes@math.ucla.edu Homework: 20% (each problem graded on a 2-pt. scale). PRINT OUT HOMEWORK FROM COURSE WEBPAGE Midterm
School: UCLA
Course: Sftwr-scntfc Cmpttn
What Shanl We Mam? Population, as Malthus said, naturally tends to grow "geometrically," or, as we would now say, exponentially. In a finite world this means that the per capita share of the world's goods must steadily decrease. Is ours a finite world? A
School: UCLA
Course: Sftwr-scntfc Cmpttn
6/23/15 UCLA Institute of the Environment and Sustainability Summer 2015 M. Nartey Myralyn Nartey= Interdisciplinarian! Environmental Biology Climate and Society African Studies Community Health Sciences (Comparative E
School: UCLA
Course: Sftwr-scntfc Cmpttn
UCLA Institute of the Environment and Sustainability Summer 2015 M. Nartey What does it mean to be resilient? Reduced probability of system failure Reduced consequences due to failure Reduced time to system re
School: UCLA
Course: SYSTMS-DIFFNTL EQTN
Math 33B Time table: Lecture time Lecture 2 Lecture 3 Lecture location Midterm 1 Midterm 2 Final time 1111:50am MWF 4000A MS October 31st , Friday. November 24th , Wednesday lecture time, lecture room lecture time, lecture room December 13, 11:30am2:30pm
School: UCLA
Course: MULTIVARIABLE CALCULUS
Mathematica for Rogawski's Calculus 2nd Edition 2010 Based on Mathematica Version 7 Abdul Hassen, Gary Itzkowitz, Hieu D. Nguyen, Jay Schiffman W. H. Freeman and Company New York 2 Mathematica for Rogawski's Calculus 2nd Editiion.nb Copyright 2010 Mathem
School: UCLA
172C June 2013 AV, Asset Shares, Year by Year Financial Universal Life Account Values 1. For Universal Life UL policies in particular, the Account Value AV functions like a policy holder "bank account that is periodically updated Even though premium
School: UCLA
School: UCLA
Course: Sftwr-scntfc Cmpttn
UCLA Institute of the Environment and Sustainability Summer 2015 M. Nartey Myralyn Nartey= Interdisciplinarian! Environmental Biology Climate and Society African Studies Community Health Sciences (Comparative Education)
School: UCLA
Course: Sftwr-scntfc Cmpttn
UCLA Institute of the Environment and Sustainability Summer 2015 M. Nartey Key terms Environment Sustainable development Natural Resources Ecosystem Services Key concepts Ecological footprint Interdiscipli
School: UCLA
Course: Introduction Of Complex Analysis
. ~o~.~H~ ~ ~ Q(i\'1+ r \ .)' v ~rJ1 OJ\ o~ c.~- &t H~b @-~6\ H(~\ ( \-\ Lt.-& H-o, ~\ ~c- \ i ' .f t.o 1f" 61. 1 ~"' .,_ d i:o ~'(t) ~ ~. e-'! - (u:l- 4. f.! 0 \ ' ~ ~ ~. ~ r:f l:-: .b. ~ +. H(-tj ~ ~ ~ ~ tt;tn.~ . ~; t '(:t\.:o \II C't- ~.,)'" _, Hl't\
School: UCLA
Course: Introduction Of Complex Analysis
-h cfw_; \ cfw_)'. sI . ~ I ~(.l. */ cfw_)1., ) ;: 7 : 1 ~ I( y )1. I d r;\ s :. f (,c, b)4 ( ~ (Y. '()- f ()l.,o) p( 'I ) - pcfw_)ll '() ')1. :. ~ (x, "f) . c l1.e. Palt\C-Mcfw__~/ tl f~" ~. ~\!JA\C(. . fr ~.q_._, 1]!.5' -. ~c r~J. Ml~ ~: J-t u ~ o. r
School: UCLA
Course: ANALYSIS
Math 131a Lecture 2 Spring 2009 Midterm 1 Name: Instructions: There are 4 problems. Make sure you are not missing any pages. Unless stated otherwise (or unless it trivializes the problem), you may use without proof anything proven in the sections of the b
School: UCLA
Mathematics 131A Fall 2007 FINAL Your Name: Signature: INSTRUCTIONS: This is a closed-book test. Do all work on the sheets provided. If you need more space for your solution, use the back of the sheets and leave a pointer for the grader. Good luc
School: UCLA
Course: Probability Theory
Midterm 3, Math 170b - Lec 1, Winter 2013 Instructor: Toni Antunovi c c Printed name: Signed name: Student ID number: Instructions: Read problems very carefully. Please raise your hand if you have questions at any time. The correct nal answer alone is n
School: UCLA
Course: Probability Theory
Midterm 2, Math 170b - Lec 1, Winter 2013 Instructor: Toni Antunovi c c Printed name: Signed name: Student ID number: Instructions: Read problems very carefully. Please raise your hand if you have questions at any time. The correct nal answer alone is n
School: UCLA
Course: Probability Theory
Midterm 1 practice, Math 170b - Lec 1, Winter 2013 Instructor: Toni Antunovi c c Name and student ID: Question Points 1 10 2 10 3 10 4 10 5 10 Total: 50 Score 1. (a) (2 points) If is a sample space, what is P(). Solution: P() = 1 (b) (2 points) If P(A) =
School: UCLA
Math 32A: Practice Problems for Exam 2 Problem 1. A particle travels along a path r(t) with acceleration vector t4 , 4 t . Find r(t) if at t = 0, the particle is located at the origin and has initial velocity v0 = 2, 3 . Solution: We have r (t) = 1 1 t4 ,
School: UCLA
Course: Probability Theory
Probability Theory, Math 170b, Winter 2013, Toni Antunovi - Homework 3 c c From the textbook solve the problems 29, 30, 31, 32 and 33 from the Chapter 4. Solve the problems 1, 2, 4, 5 and 6 from the Chapter 4 additional exercises at http:/www.athenasc.com
School: UCLA
Course: Probability Theory
Probability Theory, Math 170b, Winter 2013, Toni Antunovi c c Homework 1 From the textbook solve the problems 1, 2, 3, 5, 6, 8, 11 and 14 at the end of the Chapter 4. And also the problems below: Problem 1. Give examples of (not independent) random variab
School: UCLA
Course: Mathemtcl Modeling
Math 142-2, Homework 1 Solutions April 7, 2014 Problem 34.4 Suppose the growth rate of a certain species is not constant, but depends in a known way on the temperature of its environment. If the temperature is known as a function of time, derive an expres
School: UCLA
Course: Probability Theory
Probability Theory, Math 170b, Winter 2013, Toni Antunovi - Homework 4 c c solutions From the textbook solve the problems 42, 43 b) and 44 b), c) from the Chapter 4. Solve the problems 26, 27 and 29 from the Chapter 4 and problem 1 from the Chapter 7 addi
School: UCLA
Course: Introduction Of Complex Analysis
EE101 Engineering Electromagnetics Winter 2013 2/14/13 Homework #5 Due: Thursday Feb 21 10:00 AM Hand in to the TA at beginning of class. No late homework is accepted (see grading policy posted on eeweb). If you cannot make it to class, you must slip the
School: UCLA
Math32a HW1 Due on Monday, Jan 14 All the numbered problems are from the textbook Multivariable Calculus by Rogawski (2nd edition). Note: if your textbook is Early Transcendentals by Rogawski (2nd edition), then all your chapters are shifted by 1 down. Th
School: UCLA
Math 172 B Class Project 1 Object: to download a specific mortality table from the Society of Actuaries website and utilize mortality rates to calculate life expectancies Steps: 1. Log onto www.soa.org 2. At the bottom of the opening page, select popular
School: UCLA
Course: Linear Algebra
Math 33A, Sec. 2 Linear Algebra and Applications Spring 2012 Instructor: Professor Alan J. Laub Dept. of Mathematics / Electrical Engineering Math Sciences 7945 phone: 310-825-4245 e-mail: laub@ucla.edu Lecture: MWF 2:00 p.m. 2:50 p.m.; Rolfe 1200 Office
School: UCLA
Course: Math 31A
Dierential and Integral Calculus, Math 31a, Fall 2013, Lec 1 - Course Info Instructor: Tonci Antunovic, 6156 Math Sciences Building, tantunovic@math.ucla.edu, www.math.ucla.edu/tantunovic Instructor Oce Hours: Monday 11-12:30 and Thursday 5-6:30 in 6156 M
School: UCLA
Course: Sftwr-scntfc Cmpttn
Environment12: SustainabilityandtheEnvironment Summer2015 UCLAInstituteoftheEnvironment AndSustainability Page 1 of 5 SustainabilityandtheEnvironment Summer2015 Synopsis: There are a number of social, demographic, and ecological changes concurrently affec
School: UCLA
Course: SYSTMS-DIFFNTL EQTN
Math 131 A/1: Analysis, UCLA, Spring 2015 Instructor name Dr. Roman Taranets, Department of Mathematics, UCLA E-mail: taranets @ math.ucla.edu Website: www.math.ucla.edu/~taranets Office Phone: (310) 825-4746 Office: MS 7360 Office Hours: MWF 9:00 am
School: UCLA
Course: SYSTMS-DIFFNTL EQTN
MATH 135-2 Instructor: Sungjin Kim, MS 6617A, E-mail: i707107@math.ucla.edu Meeting Time and Location: MWF 3:00 - 3:50 PM, Room: MS 5200 O ce Hours: MW 11:45 PM - 12:30 PM, Room: MS 6617A Textbook: G. Simmons, Dierential Equations (2nd) with Applications
School: UCLA
Course: Differential Equations
Math 33B, Differential Equations Lecture 1, Spring 2015 Instructor: Will Conley Email: wconley@ucla.edu Office: MS 6322 Office Hours: Tentatively Wednesdays 3:004:00 Thursdays 12:001:30 and 3:004:00, and by appointment Website: http:/www.math.ucla.edu/~wc
School: UCLA
Course: Probability Theory
Probability Theory, Math 170b, Winter 2013, Toni Antunovi - Homework 3 c c From the textbook solve the problems 29, 30, 31, 32 and 33 from the Chapter 4. Solve the problems 1, 2, 4, 5 and 6 from the Chapter 4 additional exercises at http:/www.athenasc.com
School: UCLA
Sule Ozler Economics 12 Fall 2011 READ THE EXPLANATIONS IN BOLD LETTERS CAREFULLY! MID-TERM EXAM - GOOD LUCK! WRITE YOUR NAME AND ID# You will not be allowed to ask questions during the exam. This exam has three sections. Each section has multiple questio
School: UCLA
Course: Probability Theory
Probability Theory, Math 170b, Winter 2013, Toni Antunovi c c Homework 1 From the textbook solve the problems 1, 2, 3, 5, 6, 8, 11 and 14 at the end of the Chapter 4. And also the problems below: Problem 1. Give examples of (not independent) random variab
School: UCLA
Course: Mathemtcl Modeling
Math 142-2, Homework 1 Solutions April 7, 2014 Problem 34.4 Suppose the growth rate of a certain species is not constant, but depends in a known way on the temperature of its environment. If the temperature is known as a function of time, derive an expres
School: UCLA
Spring 2011- Professor Sule Ozler Name Economics 121 ID# FINAL EXAM WRITE YOUR NAME AND ID# No questions will be answered during the exam There will not be any bathroom breaks during the exam You are permitted to have only your writing equipment on your d
School: UCLA
Course: Probability Theory
Probability Theory, Math 170b, Winter 2013, Toni Antunovi - Homework 4 c c solutions From the textbook solve the problems 42, 43 b) and 44 b), c) from the Chapter 4. Solve the problems 26, 27 and 29 from the Chapter 4 and problem 1 from the Chapter 7 addi
School: UCLA
Course: ANALYSIS
Math 131a Lecture 2 Spring 2009 Midterm 1 Name: Instructions: There are 4 problems. Make sure you are not missing any pages. Unless stated otherwise (or unless it trivializes the problem), you may use without proof anything proven in the sections of the b
School: UCLA
Mathematics 131A Fall 2007 FINAL Your Name: Signature: INSTRUCTIONS: This is a closed-book test. Do all work on the sheets provided. If you need more space for your solution, use the back of the sheets and leave a pointer for the grader. Good luc
School: UCLA
Course: Probability Theory
Midterm 3, Math 170b - Lec 1, Winter 2013 Instructor: Toni Antunovi c c Printed name: Signed name: Student ID number: Instructions: Read problems very carefully. Please raise your hand if you have questions at any time. The correct nal answer alone is n
School: UCLA
Course: Probability Theory
Midterm 2, Math 170b - Lec 1, Winter 2013 Instructor: Toni Antunovi c c Printed name: Signed name: Student ID number: Instructions: Read problems very carefully. Please raise your hand if you have questions at any time. The correct nal answer alone is n
School: UCLA
Course: Probability Theory
Midterm 1 practice, Math 170b - Lec 1, Winter 2013 Instructor: Toni Antunovi c c Name and student ID: Question Points 1 10 2 10 3 10 4 10 5 10 Total: 50 Score 1. (a) (2 points) If is a sample space, what is P(). Solution: P() = 1 (b) (2 points) If P(A) =
School: UCLA
Math 32A: Practice Problems for Exam 2 Problem 1. A particle travels along a path r(t) with acceleration vector t4 , 4 t . Find r(t) if at t = 0, the particle is located at the origin and has initial velocity v0 = 2, 3 . Solution: We have r (t) = 1 1 t4 ,
School: UCLA
Course: MATH20F
Hector Ordorica Section: B51 / B02 TA: Matthew Cecil Exercise 3.1 Answer: D can be: 1. D = 0 0 00 2. D = 1 0 01 3. D = 0 1 10 Exercise 3.2 (a) Input: A = [1, 2, 0; 2, 1, 2; 0, 2, 1] B = [3, 0, 3; 1, 5, 1; 1, 1, 3] x = [1; 2; 3] C = (5*A^2*B - 3*A')^2 Outp
School: UCLA
Introduction to Probability 2nd Edition Problem Solutions (last updated: 11/25/11) c Dimitri P. Bertsekas and John N. Tsitsiklis Massachusetts Institute of Technology WWW site for book information and orders http:/www.athenasc.com Athena Scientific, Belmo
School: UCLA
Math 3C Midterm 1 Solutions Name: BruinID: Section: Read the following information before starting the exam. This test has six questions and seven pages. It is your responsibility to ensure that you have all of the pages! Show all work, clearly and in o
School: UCLA
Course: MATH20F
Hector Ordorica Section: Tues 2-3 TA: Greene Exercise 2.1 (a) (b) (c) Input: C = [5 2 1; 6 0 1; 4 -6 2] d = [-1; 2; 3] x = C\d Output: x=0.9091 -1.0455 -3.4545 (d) Input: C*x-d Output: C*x-d ans = 1.0e-015 * -0.4441 -0.4441 0.8882 Exercise 2.2 Input: C =
School: UCLA
Course: Actuarial Math
MATH 115A - Practice Final Exam Paul Skoufranis November 19, 2011 Instructions: This is a practice exam for the nal examination of MATH 115A that would be similar to the nal examination I would give if I were teaching the course. This test may or may not
School: UCLA
School: UCLA
Course: Introduction Of Complex Analysis
EE101 Engineering Electromagnetics Winter 2013 2/14/13 Homework #5 Due: Thursday Feb 21 10:00 AM Hand in to the TA at beginning of class. No late homework is accepted (see grading policy posted on eeweb). If you cannot make it to class, you must slip the
School: UCLA
Math32a HW1 Due on Monday, Jan 14 All the numbered problems are from the textbook Multivariable Calculus by Rogawski (2nd edition). Note: if your textbook is Early Transcendentals by Rogawski (2nd edition), then all your chapters are shifted by 1 down. Th
School: UCLA
Course: Probability Theory
Probability Theory, Math 170b, Winter 2013, Toni Antunovi - Homework 3 c c From the textbook solve the problems 29, 30, 31, 32 and 33 from the Chapter 4. Solve the problems 1, 2, 4, 5 and 6 from the Chapter 4 additional exercises at http:/www.athenasc.com
School: UCLA
School: UCLA
Course: Probability Theory
Probability Theory, Math 170b, Winter 2013, Toni Antunovi - Homework 6 c c Solve the problems 18 a) c) d) e) and 19 from the Chapter 7 additional exercises at http:/www.athenasc.com/prob-supp.html And also the problems below: Problem 1. Denote points P0 ,
School: UCLA
Course: Mathemtcl Modeling
Math 142 Homework 1 Solution 34.5 (a) If the growth rate is 0, the discrete model becomes Nm+1 Nm = tfm , And the solution is Nm = N0 + t(f0 + f1 + fm1 ), which is analogous to integration, since let t = mt, then t t(f0 + f1 + fm1 ) f (s)ds 0 for t small
School: UCLA
Course: Mathemtcl Modeling
Math 142 Homework 2 Solution 34.16 We will rstly proceed similarly as 34.14(c). We assume the theoretical growth has the form N0 eR0 t , and we hope to gure out which N0 and R0 t the data best. 34.14(b) suggests that N0 should be equal to the initial popu
School: UCLA
MATH 32A FINAL EXAMINATION March 21th, 2012 Please show your work. You will receive little or no credit for a correct answer to a problem which is not accompanied by sucient explanations. If you have a question about any particular problem, please raise y
School: UCLA
Math 131A/2 Winter 2002 Handout #1a Instructor: E. Eros, MS 6931. Lecture Meeting Time: MWF 2:00PM-2:50PM Location: MS 5117 Recitation TA: to be announced, MS 5117 T 2:00P-2:50P Oce hours (tentative): TF 4-5. Text Fundamental Ideas of Analysis by Michael
School: UCLA
Course: Probability Theory
Probability Theory, Math 170b, Winter 2013, Toni Antunovi - Homework 5 c c From the textbook solve the problems 4 and 5 from the Chapter 5. Solve the problems 2, 4, 6, 7, 9 and 10 from the Chapter 7 additional exercises at http:/www.athenasc.com/prob-supp
School: UCLA
Chapter Review Exercises SOLUTION 811 The function whose value we want to approximate is f (x, y, z) = x 2 + y2 + z We use the linear approximation at the point (7, 5, 70), hence h = 7.1 - 7 = 0.1, k = 4.9 - 5 = -0.1, and l = 70.1 - 70 = 0.1. We g
School: UCLA
MULTIVARIABLE CALCULUS (MATH 32A) FALL 2004 LECTURE NOTES 10/1/04 Office Hours: MS 7344, 11:00 12:40 Mon., Wed., Fri. E-mail: lchayes@math.ucla.edu Homework: 20% (each problem graded on a 2-pt. scale). PRINT OUT HOMEWORK FROM COURSE WEBPAGE Midterm
School: UCLA
Course: Sftwr-scntfc Cmpttn
What Shanl We Mam? Population, as Malthus said, naturally tends to grow "geometrically," or, as we would now say, exponentially. In a finite world this means that the per capita share of the world's goods must steadily decrease. Is ours a finite world? A
School: UCLA
Course: Sftwr-scntfc Cmpttn
6/23/15 UCLA Institute of the Environment and Sustainability Summer 2015 M. Nartey Myralyn Nartey= Interdisciplinarian! Environmental Biology Climate and Society African Studies Community Health Sciences (Comparative E
School: UCLA
Course: Sftwr-scntfc Cmpttn
UCLA Institute of the Environment and Sustainability Summer 2015 M. Nartey What does it mean to be resilient? Reduced probability of system failure Reduced consequences due to failure Reduced time to system re
School: UCLA
Course: Sftwr-scntfc Cmpttn
Progress in Human Geography http:/phg.sagepub.com Race, class and environmental justice Susan L. Cutter Prog Hum Geogr 1995; 19; 111 DOI: 10.1177/030913259501900111 The online version of this article can be found at: http:/phg.sagepub.com Published by: ht
School: UCLA
A Brief Note on Proofs in Pure Mathematics Shagnik Das What is pure mathematics? Pure mathematics is a discipline that enjoys a rich history, dating back to Ancient Greece. The goal is to rigorously establish mathematical truths; to show with absolute cer
School: UCLA
Continuity of Functions Shagnik Das Introduction A general function from R to R can be very convoluted indeed, which means that we will not be able to make many meaningful statements about general functions. To develop a useful theory, we must instead res
School: UCLA
Mathematics 131a - A Short But Fairly Complete Study Guide For The Midterm Instructor: D.E.Weisbart Here are some points you should look over: (1) Know the logical connectives and quantiers, the truth tables for the quantiers, and how to negate statements
School: UCLA
Mathematics 131a - A Short But Fairly Complete Study Guide For The Final Instructor: D.E.Weisbart Here are some points you should look over: (1) Know the logical connectives and quantiers, the truth tables for the quantiers, and how to negate statements.
School: UCLA
Theorem: For a simple graph, any two of these three statements, taken together, imply the third: The graph is connected. The graph is acyclic. The number of vertices in the graph is exactly one more than the number of edges. Proof: By induction. In a grap
School: UCLA
Course: Math 164
Math 164: Optimization Nonlinear optimization with equality constraints Instructor: Wotao Yin Department of Mathematics, UCLA Spring 2015 material taken from the textbook Chong-Zak, 4th Ed. online discussions on piazza.com we discuss how to recognize a s
School: UCLA
Course: Math 164
Math 164: Optimization The Simplex method Instructor: Wotao Yin Department of Mathematics, UCLA Spring 2015 material taken from the textbook Chong-Zak, 4th Ed. online discussions on piazza.com Overview: idea and approach If a standard-form LP has a solut
School: UCLA
Course: Math 164
Math 164: Optimization Krylov subspace, nonlinear CG, and preconditioning Instructor: Wotao Yin Department of Mathematics, UCLA Spring 2015 material taken from the textbook Chong-Zak, 4th Ed., and the CG paper by Shewchuk online discussions on piazza.com
School: UCLA
Course: Math 164
Math 164: Optimization Algorithms for constrained optimization Instructor: Wotao Yin Department of Mathematics, UCLA Spring 2015 material taken from the textbook Chong-Zak, 4th Ed. online discussions on piazza.com Coverage We will learn some algorithms fo
School: UCLA
Course: Math 164
Introduction to Optimization Major subfields Instructor: Wotao Yin Department of Mathematics, UCLA Spring 2015 online discussions on piazza.com Overview Continuous vs Discrete Continuous optimization: convex vs non-convex unconstrained vs constrained l
School: UCLA
Course: Math 164
Math 164: Optimization Nonlinear optimization with inequality constraints Instructor: Wotao Yin Department of Mathematics, UCLA Spring 2015 material taken from the textbook Chong-Zak, 4th Ed. online discussions on piazza.com we discuss how to recognize a
School: UCLA
Course: Math 164
Math 164: Optimization Basics of Optimization Instructor: Wotao Yin Department of Mathematics, UCLA Spring 2015 based on Chong-Zak, 4th Ed. online discussions on piazza.com Goals of this lecture For a general form minimize f (x) subject to x we study the
School: UCLA
Course: Math 164
Math 164: Optimization Netwons Method Instructor: Wotao Yin Department of Mathematics, UCLA Spring 2015 some material taken from Chong-Zak, 4th Ed. online discussions on piazza.com Main features of Newtons method Uses both first derivatives (gradients) a
School: UCLA
Course: Math 164
Math 164: Optimization Linear programming Instructor: Wotao Yin Department of Mathematics, UCLA Spring 2015 material taken from the textbook Chong-Zak, 4th Ed. online discussions on piazza.com History The word programming used traditionally by planners t
School: UCLA
Course: Math 164
Math 164: Optimization Barzilai-Borwein Method Instructor: Wotao Yin Department of Mathematics, UCLA Spring 2015 online discussions on piazza.com Main features of the Barzilai-Borwein (BB) method The BB method was published in a 8-page paper1 in 1988 It
School: UCLA
Course: Math 164
Math 164: Optimization Conjugate direction methods Instructor: Wotao Yin Department of Mathematics, UCLA Spring 2015 material taken from the textbook Chong-Zak, 4th Ed., and the CG paper by Shewchuk online discussions on piazza.com Main features of conjug
School: UCLA
Course: Math 164
Math 164: Optimization Gradient Methods Instructor: Wotao Yin Department of Mathematics, UCLA Spring 2015 some material taken from Chong-Zak, 4th Ed. online discussions on piazza.com Main features of gradient methods They are the most popular methods (in
School: UCLA
Course: Math 164
Math 164: Optimization One-Dimensional Search Methods Instructor: Wotao Yin Department of Mathematics, UCLA Spring 2015 based on Chong-Zak, 4th Ed. online discussions on piazza.com Goal of this lecture Develop methods for solving the one-dimensional probl
School: UCLA
Course: Math 164
Math 164: Optimization Optimization application examples Instructor: Wotao Yin Department of Mathematics, UCLA Spring 2015 online discussions on piazza.com Job assignment problem1 An insurance oce handles three types of work: Information, Policy, Claims.
School: UCLA
Course: Math 164
Math 164: Optimization Support vector machine Instructor: Wotao Yin Department of Mathematics, UCLA Spring 2015 online discussions on piazza.com Support vector machine (SVM) Background: to classify a set of data points into two sets. Examples: emails: le
School: UCLA
Course: Math 164
Math 164: Introduction to Optimization Instructor: Wotao Yin Department of Mathematics, UCLA Spring 2015 online discussions on piazza.com Resource-constrained revenue optimization m resources; resource i has bi units available n products; product j uses
School: UCLA
Course: Math 164
Math 164: Introduction to Optimization Instructor: Wotao Yin Department of Mathematics, UCLA Spring 2015 online discussions on piazza.com What is mathematical optimization? Optimization models the goal of solving a problem in the optimal way. Examples:
School: UCLA
Temperature (deg. F) 0 5 10 15 20 25 30 35 40 45 50 Wind Speed (mph) 3 6 -6.8801964 -11.878997 -1.2244259 -5.9243474 4.43134455 0.03030239 10.087115 5.98495223 15.7428855 11.9396021 21.398656 17.8942519 27.0544265 23.8489017 32.710197 29.8035516 38.365967
School: UCLA
Course: SYSTMS-DIFFNTL EQTN
Math 33B Time table: Lecture time Lecture 2 Lecture 3 Lecture location Midterm 1 Midterm 2 Final time 1111:50am MWF 4000A MS October 31st , Friday. November 24th , Wednesday lecture time, lecture room lecture time, lecture room December 13, 11:30am2:30pm
School: UCLA
Course: MULTIVARIABLE CALCULUS
Mathematica for Rogawski's Calculus 2nd Edition 2010 Based on Mathematica Version 7 Abdul Hassen, Gary Itzkowitz, Hieu D. Nguyen, Jay Schiffman W. H. Freeman and Company New York 2 Mathematica for Rogawski's Calculus 2nd Editiion.nb Copyright 2010 Mathem
School: UCLA
172C June 2013 AV, Asset Shares, Year by Year Financial Universal Life Account Values 1. For Universal Life UL policies in particular, the Account Value AV functions like a policy holder "bank account that is periodically updated Even though premium
School: UCLA
School: UCLA
Course: Sftwr-scntfc Cmpttn
UCLA Institute of the Environment and Sustainability Summer 2015 M. Nartey Myralyn Nartey= Interdisciplinarian! Environmental Biology Climate and Society African Studies Community Health Sciences (Comparative Education)
School: UCLA
Course: Sftwr-scntfc Cmpttn
UCLA Institute of the Environment and Sustainability Summer 2015 M. Nartey Key terms Environment Sustainable development Natural Resources Ecosystem Services Key concepts Ecological footprint Interdiscipli
School: UCLA
Course: Introduction Of Complex Analysis
. ~o~.~H~ ~ ~ Q(i\'1+ r \ .)' v ~rJ1 OJ\ o~ c.~- &t H~b @-~6\ H(~\ ( \-\ Lt.-& H-o, ~\ ~c- \ i ' .f t.o 1f" 61. 1 ~"' .,_ d i:o ~'(t) ~ ~. e-'! - (u:l- 4. f.! 0 \ ' ~ ~ ~. ~ r:f l:-: .b. ~ +. H(-tj ~ ~ ~ ~ tt;tn.~ . ~; t '(:t\.:o \II C't- ~.,)'" _, Hl't\
School: UCLA
Course: Introduction Of Complex Analysis
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School: UCLA
Course: Introduction Of Complex Analysis
- t, c Lcfw_ - R /~ /k.-. cfw_ > t) ,:,2- ~. ~ itMJ( -R 0 aJf i = l z = -1 ~ CZr;.fv,4 t re~ R/ 1 ~) _:_ J=b )-+i~ ~t wJ)_ < - - 1 - RL-\ ~ ~ ;, ~ a. lTK ~ul ~ct ~ k ~ )~Jj eM;~, ~:Jt tr~v)~t>~.f 1tr1>'fl-o ~ 4' 7tJ \ Jj :=. ~t;> _!. -. dlr~ f'(~) f,l ~ -
School: UCLA
Course: Introduction Of Complex Analysis
tr.hwrr . M~ I3d. )~JOI'-1 ~ t~)~ ~ j._ $' . It t(")'J) \l'~')1) ~ )l~)"'" cfw_lClt), 1 tt)\ -t f L-~J \'.et.<~ ~.J.e.' (t;: c. L ~ 'I . \ ~ Q. ;: Jl>v>-' ~ ) ~ '( 0 ~ CA~., "-~o.,.,1 \. ~ I X (-t) ; - ~1vvt - I 1 - "R(~iis,~vi) 'R (-:Sill+ f .\ em\ ., ~
School: UCLA
Course: Introduction Of Complex Analysis
:. -1i \t~o - 1-: ~ ~Vl-1-I.J.-l J d-t"'-\- h. d.~ 11\-\-cfw_a_:.-\ 7cfw_) OY "~ ~
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Course: SYSTMS-DIFFNTL EQTN
to bifurcation theory Introduction John David Crawford Institute for Fusion Studies, The University of Texas at Austin, Austin, Texas 78712 and Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, Pennsylvania 15280 The theory of bif
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Course: Math 31 B Lecture 4
Module 4: Neural and Hormonal Systems Overview: What We Have in Mind Building blocks of mind: Neurons and how they communicate (neurotransmi9ers) Systems that build the mind: Func=ons of Parts of the Nervous system Suppor
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Course: Math 31 B Lecture 4
Module 18: Vision, and Perceptual Organiza9on and Interpreta9on Vision: Energy, Sensa,on, and Percep,on The Visible Spectrum We encounter waves of electromagne2c radia2on. Our eyes respond to some of these waves. Our brain turns these
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Course: Math 31 B Lecture 4
Module 1: The Story of Psychology From specula9on to science: The Birth of Modern Psychology Aristotle (4th century BCE) asked ques9ons to understand the rela9onship between body and psyche. His way of answering those ques9ons
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Course: Math 31 B Lecture 4
Module 13: Developmental Issues, Prenatal Development, and the Newborn nature and nurture How do genes and experience guide development over our lifespan? change and stability Issues in Developmental Psychology con?nuity vs. stages In wh
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Course: Math 31 B Lecture 4
Module 7: Brain States and Consciousness What is Consciousness, Exactly? alertness; being awake vs. being unconscious self-awareness; the ability to think about self having free will; being able to make a conscious decision a pe
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Course: Math 31 B Lecture 4
Solutions to practice midterm 2 Sudesh Kalyanswamy and Jiayin Guo (1) From the problem we know k = 0.25 while N = 5000, and we are going to solve dy y = ky(1 ), dt N with initial value y(0) = 1. Since y(0) = 1, this means ky(1 separate the variables to g
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Course: Math 31 B Lecture 4
not the same, so no . k=1 k=2 for different values of k, you get anther solution. y=k(t+1) +6 is a "family" of solutions y(0)=8 - - - - - PG corrections, I "lost " a negative when we took the antiderivative .
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Course: Math 31 B Lecture 4
Math 3B Lecture 1 Winter 2015 due Friday March 6 Late homework is NOT accepted . at 8 am . HW # 7 Name _ Section A B C D (circle one ) UCLA ID # _ Print this page and staple it to your homework papers . 6.1 # 23-27,28-31,35 6.2 # 30,36,40 6.3 # 2,8,14,26
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Course: Math 31 B Lecture 4
Math 3B Lecture 1 Winter 2015 due Monday Feb 23 Late homework is NOT accepted . at 8 am . HW # 5 Name _ Section A B C D (circle one ) UCLA ID # _ Print this page and staple it to your homework papers . 5.6 #11,12,16,17,20,39 ( during eighth year means bet
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Course: Math 31 B Lecture 4
Math 3B Lecture 1 Winter 2015 due Friday Feb 27 Late homework is NOT accepted . at 8 am . HW # 6 Name _ Section A B C D (circle one ) UCLA ID # _ Print this page and staple it to your homework papers . 6.1 #1,3,7,10,14,16,19-22,39 6.2 # 1,5,8,12,17,21,24
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Course: Math 31 B Lecture 4
Math 3B Lecture 1 Winter 2015 due Wednesday February 17 at 8 am . Late homework is NOT accepted . HW # 4 Name _ Section A B C D (circle one ) UCLA ID # _ Print this page and staple it to your homework papers . Practice 5.4 # 1,5,11,15,17,27 5.5 # 1,3,7,11
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Course: Math 31 B Lecture 4
A few solutions to Prof. Greenes practice midterm 1 Sudesh Kalyanswamy (8) (a) The graph of y = 36 x2 is shown below in gure 1. Figure 1: The graph of f (x) = 36 x2 It is the top half of a circle of radius 6. The portion between x = 0 and x = 6 is the rig
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Course: Math 31 B Lecture 4
Math 3B Lecture 1 Winter 2015 NOT accepted . due Friday Feb 6 at 8 am . Late homework is HW # 3 Name _ Section A B C D (circle one ) UCLA ID # _ 4.3 # 24,26,38,44 4.5 # 35,41 5.1 #9,17,22,26,29 5.2 # 2,8,14,19,20,22,24,30 Chapter 4 review # 2,3,7,15,20 Tu
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Course: Math 31 B Lecture 4
Math 3B Lecture 1 Winter 2015 NOT accepted . due Friday January 23 at 8 am . Late homework is HW # 2 Name _ Section A B C D (circle one ) UCLA ID # _ 4.3 # 16,17,28,35,42 1.7 # 13,19,21,24,31,32 4.5 # 1-4,9,10,13,14,15,17,21,22 Turn in all of these proble
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Course: Math 31 B Lecture 4
Math 3B Lecture 1 Winter 2015 NOT accepted . due Friday January 16 at 8 am . Late homework is Name _ Section A B C D (circle one ) UCLA ID # _ "Practice problems"are for you to practice your skills. These are NOT collected . "Turn in problems" must be wri
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Course: ANALYSIS
Math 131a Lecture 2 Spring 2009 Midterm 1 Name: Instructions: There are 4 problems. Make sure you are not missing any pages. Unless stated otherwise (or unless it trivializes the problem), you may use without proof anything proven in the sections of the b
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Mathematics 131A Fall 2007 FINAL Your Name: Signature: INSTRUCTIONS: This is a closed-book test. Do all work on the sheets provided. If you need more space for your solution, use the back of the sheets and leave a pointer for the grader. Good luc
School: UCLA
Course: Probability Theory
Midterm 3, Math 170b - Lec 1, Winter 2013 Instructor: Toni Antunovi c c Printed name: Signed name: Student ID number: Instructions: Read problems very carefully. Please raise your hand if you have questions at any time. The correct nal answer alone is n
School: UCLA
Course: Probability Theory
Midterm 2, Math 170b - Lec 1, Winter 2013 Instructor: Toni Antunovi c c Printed name: Signed name: Student ID number: Instructions: Read problems very carefully. Please raise your hand if you have questions at any time. The correct nal answer alone is n
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Course: Probability Theory
Midterm 1 practice, Math 170b - Lec 1, Winter 2013 Instructor: Toni Antunovi c c Name and student ID: Question Points 1 10 2 10 3 10 4 10 5 10 Total: 50 Score 1. (a) (2 points) If is a sample space, what is P(). Solution: P() = 1 (b) (2 points) If P(A) =
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Math 32A: Practice Problems for Exam 2 Problem 1. A particle travels along a path r(t) with acceleration vector t4 , 4 t . Find r(t) if at t = 0, the particle is located at the origin and has initial velocity v0 = 2, 3 . Solution: We have r (t) = 1 1 t4 ,
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Math 3C Midterm 1 Solutions Name: BruinID: Section: Read the following information before starting the exam. This test has six questions and seven pages. It is your responsibility to ensure that you have all of the pages! Show all work, clearly and in o
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Course: Actuarial Math
MATH 115A - Practice Final Exam Paul Skoufranis November 19, 2011 Instructions: This is a practice exam for the nal examination of MATH 115A that would be similar to the nal examination I would give if I were teaching the course. This test may or may not
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MATH 32A FINAL EXAMINATION March 21th, 2012 Please show your work. You will receive little or no credit for a correct answer to a problem which is not accompanied by sucient explanations. If you have a question about any particular problem, please raise y
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Math 3C Midterm 2 Name: BruinID: Section: Read the following information before starting the exam. This test has six questions and fourteen pages. It is your responsibility to ensure that you have all of the pages! Show all work, clearly and in order, i
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Course: Probability Theory
Mathematics Department, UCLA P. Caputo Fall 08, midterm 1 Oct 22, 2008 Midterm 1: Math 170A Probability, Sec. 2 Last name First and Middle Names Signature UCLA ID number (if you are an extension student, say so) Please note: 1. Provide the information ask
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MATH 33A/2 PRACTICE MIDTERM 2 Problem 1. (True/False) . 1 1 1 Problem 2. Let v1 = 1 , v2 = 0 , v3 = 1 . 0 1 1 3 (a) Show that B = 1 , v2 , v3 ) is a basis for R . (v 1 (b) Let u = 1 . Find the coordinates of u with respect to the basis B. 2 (c) Find the c
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MATH 32A FIRST MIDTERM EXAMINATION October, 18th, 20101 Please show your work. You will receive little or no credit for a correct answer to a problem which is not accompanied by sufficient explanations. If you have a question about any particular problem,
School: UCLA
Course: Discrete Math
Practice First Test Mathematics 61 Disclaimer: Listed here is a selection of the many possible sorts of problems. actual test is apt to make a different selection. 1. Let S5 be the set of all binary strings of length 5 (such as 11001). The Let E be
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Math 31A Midterm 1 Solutions 1. [3 points] The derivative of (a) 1 (b) (c) x2 x)2 x3 Brent Nelson is: 1 x2 1 2 x3 (3x 1 1 + x2 + 8x2 x + 3) (d) None of the above. Solution. We rst apply the quotient rule d dx x2 x)2 x3 = d d x3 dx [(x2 x)2 ] (x2 x)2 dx (
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Management 1A Summer 2004 Danny S. Litt EXAM 2 SOLUTION Name: _ Student ID No. _ I agree to have my grade posted by Student ID Number. _ (Signature) PROBLEM 1 2 3 4 5 6 7 8 TOTAL POINTS 20 25 20 20 30 30 30 25 200 SCORE MANAGEMENT 1A Problem 1
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Course: 31b
Math 31B, Lecture 4, Fall 2011 Exam #2, 9 November 2011 Name: ID Number: Section and TA: You have 50 minutes for the exam. No calculators, phones, notes, or books allowed. You must show your work for credit. Hint: n n=0 cr Question: 1 2 3 4 5 6 7 Tota
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Course: Linear Algebra
Name: Student ID: Prof. Alan J. Laub June 9, 2011 Math 33A/1 FINAL EXAMINATION Spring 2011 Instructions: (a) The exam is closed-book (except for one two-sided page of notes) and will last two and a half hours. No calculators, cell phones, or other electro
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Management 1A Summer 2004 Danny S. Litt EXAM 1 SOLUTION Name: _ Student ID No. _ I agree to have my grade posted by Student ID Number. _ (Signature) PROBLEM 1 2 3 4 5 6 7 8 9 10 TOTAL POINTS 20 20 20 20 20 20 20 20 20 20 200 SCORE MANAGEMENT 1A
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Name: Student ID: Section: 2 Prof. Alan J. Laub Math 33A FINAL EXAMINATION Spring 2008 Instructions: June 9, 2008 (a) The exam is closed-book (except for one page of notes) and will last two and a half (2.5) hours. (b) Notation will conform as closely as
School: UCLA
Course: Math 32A
MATH 32A: SECOND PRACTICE MIDTERM EXAMINATION Summer 2008 1 1. (20 points) The C be a curve dened by the position function r(t) =< sin 2t, t, cos 2t >. (a) Calculate the equation of the Normal plane at the point (0, , 1). Its easier than you think.
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Management 1A Fall 2007 Danny S. Litt EXAM 1 Solutions I agree to have my grade posted by Student ID Number _ _ (Signature) (Student ID Number) Name: _ PROBLEM 1 2 3 4 5 6 7 8 TOTAL POINTS 30 30 20 30 20 20 20 30 200 SCORE MANAGEMENT 1A NAME: _
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Course: Math 31A
Midterm 1, Math 31A, Fall 2013 Instructor: Toni Antunovi c c Printed name and student ID: Signed name: Section number, time and TA name: Instructions: Read problems very carefully. Please raise your hand if you have questions at any time. The correct na
School: UCLA
Course: Math 31A
Practice Midterm 1, Math 31A, Fall 2013 Instructor: Toni Antunovi c c Printed name and student ID: Signed name: Section number, time and TA name: Instructions: Read problems very carefully. Please raise your hand if you have questions at any time. The c
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Course: Math 31A
Practice Midterm 2, Math 31A, Fall 2013 Instructor: Toni Antunovi c c Printed name and student ID: Signed name: Section number, time and TA name: Instructions: Read problems very carefully. Please raise your hand if you have questions at any time. The c
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Course: Math 31A
Final practice, Math 31A - Lec 1, Fall 2013 Instructor: Toni Antunovi c c Name and student ID: Question Points 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 10 10 10 Total: 100 Score 1. (a) (2 points) Find all points x at which the function f (x) = x1/3 is no
School: UCLA
School: UCLA
Course: Probability Theory
Probability Theory, Math 170b, Winter 2013, Toni Antunovi - Homework 3 c c From the textbook solve the problems 29, 30, 31, 32 and 33 from the Chapter 4. Solve the problems 1, 2, 4, 5 and 6 from the Chapter 4 additional exercises at http:/www.athenasc.com
School: UCLA
Course: Probability Theory
Probability Theory, Math 170b, Winter 2013, Toni Antunovi c c Homework 1 From the textbook solve the problems 1, 2, 3, 5, 6, 8, 11 and 14 at the end of the Chapter 4. And also the problems below: Problem 1. Give examples of (not independent) random variab
School: UCLA
Course: Mathemtcl Modeling
Math 142-2, Homework 1 Solutions April 7, 2014 Problem 34.4 Suppose the growth rate of a certain species is not constant, but depends in a known way on the temperature of its environment. If the temperature is known as a function of time, derive an expres
School: UCLA
Course: Probability Theory
Probability Theory, Math 170b, Winter 2013, Toni Antunovi - Homework 4 c c solutions From the textbook solve the problems 42, 43 b) and 44 b), c) from the Chapter 4. Solve the problems 26, 27 and 29 from the Chapter 4 and problem 1 from the Chapter 7 addi
School: UCLA
Course: Introduction Of Complex Analysis
EE101 Engineering Electromagnetics Winter 2013 2/14/13 Homework #5 Due: Thursday Feb 21 10:00 AM Hand in to the TA at beginning of class. No late homework is accepted (see grading policy posted on eeweb). If you cannot make it to class, you must slip the
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Math32a HW1 Due on Monday, Jan 14 All the numbered problems are from the textbook Multivariable Calculus by Rogawski (2nd edition). Note: if your textbook is Early Transcendentals by Rogawski (2nd edition), then all your chapters are shifted by 1 down. Th
School: UCLA
Course: Probability Theory
Probability Theory, Math 170b, Winter 2013, Toni Antunovi - Homework 3 c c From the textbook solve the problems 29, 30, 31, 32 and 33 from the Chapter 4. Solve the problems 1, 2, 4, 5 and 6 from the Chapter 4 additional exercises at http:/www.athenasc.com
School: UCLA
Course: Probability Theory
Probability Theory, Math 170b, Winter 2013, Toni Antunovi - Homework 6 c c Solve the problems 18 a) c) d) e) and 19 from the Chapter 7 additional exercises at http:/www.athenasc.com/prob-supp.html And also the problems below: Problem 1. Denote points P0 ,
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Course: Mathemtcl Modeling
Math 142 Homework 1 Solution 34.5 (a) If the growth rate is 0, the discrete model becomes Nm+1 Nm = tfm , And the solution is Nm = N0 + t(f0 + f1 + fm1 ), which is analogous to integration, since let t = mt, then t t(f0 + f1 + fm1 ) f (s)ds 0 for t small
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Course: Mathemtcl Modeling
Math 142 Homework 2 Solution 34.16 We will rstly proceed similarly as 34.14(c). We assume the theoretical growth has the form N0 eR0 t , and we hope to gure out which N0 and R0 t the data best. 34.14(b) suggests that N0 should be equal to the initial popu
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Course: Probability Theory
Probability Theory, Math 170b, Winter 2013, Toni Antunovi - Homework 5 c c From the textbook solve the problems 4 and 5 from the Chapter 5. Solve the problems 2, 4, 6, 7, 9 and 10 from the Chapter 7 additional exercises at http:/www.athenasc.com/prob-supp
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Course: Probability Theory
Probability Theory, Math 170b, Winter 2013, Toni Antunovi - Homework 2 c c From the textbook solve the problems 17, 18, 19, 22, 23 and 24 from the Chapter 4. Solve the problems 21, 22, 24, 30 from the Chapter 4 additional exercises at http:/www.athenasc.c
School: UCLA
Course: Introduction Of Complex Analysis
EE101 Engineering Electromagnetics Winter 2013 1/24/12 Homework #3 Due: Thursday Jan 31 10:00 AM Hand in to the TA at beginning of class. No late homework is accepted (see grading policy posted on eeweb). If you cannot make it to class, you must slip the
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Course: 245abc
Math 245A Homework 2 Brett Hemenway October 19, 2005 Real Analysis Gerald B. Folland Chapter 1. 13. Every -nite measure is seminite. Let be a -nite measure on X, M. Since is -nite, there exist a sequence of sets cfw_En in M, with (En ) < and En = X . n=1
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Course: Mathemtcl Modeling
Math 142 Homework 0 Solution 32.11 The easier way: (slightly unrigorous, but its ne for this class) Suppose the bank compounds the interest n times a year, and let t := 1/n. With the additional deposit (or withdrawl) D (t), the balance at time t + t is gi
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Homework 4 Solutions Math 3C Due: Friday Nov 2, 9 a.m. #1(a) Let X be a random variable that equals the number of defective items in the sample. When the sampling is done with replacement, X is binomially distributed with parameters n = 5 and p = 5/20 = 1
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Course: 245abc
Math 245A Homework 1 Brett Hemenway October 19, 2005 3. a. Let M be an innite -algebra on a set X Let Bx be the intersection of all E M with x E . Suppose y Bx , then By Bx because Bx is a set in M which contains y . But Bx By because if y E for any E M,
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Course: Mathemtcl Modeling
Math 142 Homework 1 Solution 32.3 (a) Use N (nt) to denote the population at time nt. Then for any n > 0, we have N (nt) = (1 + (b d)t)N (n 1)t) + 1000. (b) Given that N (0) = N0 , we hope to solve the equation above and get the general form for N (nt). T
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Course: Probability Theory
Probability Theory, Math 170b, Winter 2013, Toni Antunovi - Homework 4 c c From the textbook solve the problems 42, 43 b) and 44 b), c) from the Chapter 4. Solve the problems 26, 27 and 29 from the Chapter 4 and problem 1 from the Chapter 7 additional exe
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Course: Actuarial Math
MATH 115A - Assignment One - Solutions of Select Non-Graded Problems Paul Skoufranis October 3, 2011 1.2 Question 13) Let V denote the set of ordered pairs of real numbers. If (a1 , a2 ) and (b1 , b2 ) are elements of V and c R, dene (a1 , a2 ) + (b1 , b
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Course: Mathemtcl Modeling
Math 142-2, Homework 2 Solutions April 7, 2014 Problem 35.3 Consider a species in which both no individuals live to three years old and only one-year olds reproduce. (a) Show that b0 = 0, b2 = 0, d2 = 1 satisfy both conditions. All two year olds die befor
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Course: Real Analysis
Homework 8 Solutions Math 131A-3 1. Problems from Ross. (29.5) Suppose |f (x) f (y )| (x y )2 for all x, y in R. Then for any given a R, we )f )f have | f (xxa (a) | |x a|, so by the squeeze theorem, as x a, | f (xxa (a) | 0. Therefore f (a) = 0. Since a
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Course: Algebra
UCLA Mathematics 110A: selected solutions from homework #1 David Wihr Taylor July 2, 2010 Introduction When reading these solutions always keep in mind the common techniques being used. The point of homework, and subsequently these solutions, is to give y
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Course: Real Analysis
Math 131A Analysis Summer Session A Homework 3 Solutions n 1. Let (an ) be a bounded sequence and let (bn ) be a sequence such that lim bn = 0. Prove that lim an bn = 0. n Proof. Since (an ) is bounded, there exists M > 0 such that |an | < M for a
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1 1. Prove Proposition 3.1. Proposition 3.1 Let be a signed measure on . If is an increasing sequence in , then lim . If is a decreasing sequence in and is finite, then lim . Answer) Let be a signed measure on . a. If is an increasing sequence in
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Course: Linear Algebra
Ch. 1 Dept. of ECE, Faculty of Engineering University of Tehran Linear Algebra Homework # I Chapter 1: Vector Spaces Exercises to be handed: marked by *. Due date: Sunday Esfand 9th 1383. 1. Let V = cfw_ (a1 , a2) : a1 , a2 R . Define addition of elements
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Course: MULTIVARIABLE CALCULUS
This page intentionally left blank Students Solutions Manual to accompany Jon Rogawskis Single Variable CALCULUS SECOND EDITION BRIAN BRADIE Christopher Newport University ROGER LIPSETT W. H. FREEMAN AND COMPANY NEW YORK 2012 by W. H. Freeman and Company
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Course: Math 31A
4 APPLICATIONS OF THE DERIVATIVE 4.1 Linear Approximation and Applications Preliminary Questions 1. True or False? The Linear Approximation says that the vertical change in the graph is approximately equal to the vertical change in the tangent line. solut
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Math 172 B Class Project 1 Object: to download a specific mortality table from the Society of Actuaries website and utilize mortality rates to calculate life expectancies Steps: 1. Log onto www.soa.org 2. At the bottom of the opening page, select popular
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Course: CALCULUS FOR LIFE SCIENCE STUDENTS
i Mod-h BRZZ fevgewg Fined exam : 3/ 511/10 3 wade? V g«:>m a? Fm? 8 Lag _.o/20\3;. «,McuwiL-ztuct mgnmicmmxmmnlm WWW : E i ; E g i i; 'Yilxnc Mum wk 56:. at "moat 21,5 x . buggy THom I W: MiAVerm Km? *6 K) thiem. ' . mm mm he. 1-71.-?Ec§k\em.(3'3. at:
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Course: CALCULUS FOR LIFE SCIENCE STUDENTS
YEAHEW 2 . = /:2.g~/ was Wm 3% f2 f MT 11 case-rs: 3A mans-t3 3n 2 Co Emmi/y 3. '3) Limrs (Kg :09 Q 4 Sandwmh eowm / "Triahmxts 3 S lnlrcrmeéle '- \que Theorem Bis-ecklom. Mei'hcsé make- SUgQL-f-J? 4r. 1. Degmttien .09. QXQKiVCWlVQ: : ' ' Ycu W "0 42
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Course: Calculus
31B Notes Sudesh Kalyanswamy 1 Exponential Functions (7.1) The following theorem pretty much summarizes section 7.1. Theorem 1.1. Rules for exponentials: (1) Exponential Rules (Algebra): (a) ax ay = ax+y (b) ax ay = x y axy (c) (a ) = axy (2) Derivatives
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School: UCLA
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The Poisson distribution is a discrete distribution. It is often used as a model for the number of events (such as the number of telephone calls at a business, number of customers in waiting lines, number of defects in a given surface area, airplane arriv
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3C Notes Sudesh Kalyanswamy 1 Counting (12.1) 1.1 Guiding Questions for the Chapter Here are some questions for you to ask yourself as you read. If you can answer them all by the end, youre in pretty good shape as far as understanding the material. After
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3C Practice Problems for Midterm 1 Sudesh Kalyanswamy (1) Bills school schedule has four timeslots. Bill has 30 choices of classes for the rst timeslot, 20 for the second, 23 for the third, and 17 for the last. How many possible schedules are there? (2) C
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School: UCLA
Math 171 Spring 2013 Homework 8 (due Friday, June 7th at lecture time) (1) Read Sections 5.3 and 5.4. (2) Solve problems 2, 3, 4 and 6 from the end of chapter 5.
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School: UCLA
Course: INTEGRATION AND INFINITE SERIES
A dierential equation is an equation that relates the derivative of a function to that function (and possibly other functions). For example a dierential equation involving the function y (t) could look like dy (t) = ky (t) (1) dt for some constant k . Whe
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Course: Calculus
Answer Key B. Johnson Calculus AB Memory Quiz Important Relationships, Theorems, Rules, and Formulas Important Relationships 1. State the formal definition of derivative, include diagram. If y = f ( x ) then f ( x ) = lim h0 f ( x + h) f ( x) h 2. Where a
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Course: Linear Algebra
Math 33A, Sec. 2 Linear Algebra and Applications Spring 2012 Instructor: Professor Alan J. Laub Dept. of Mathematics / Electrical Engineering Math Sciences 7945 phone: 310-825-4245 e-mail: laub@ucla.edu Lecture: MWF 2:00 p.m. 2:50 p.m.; Rolfe 1200 Office
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Course: Math 31A
Dierential and Integral Calculus, Math 31a, Fall 2013, Lec 1 - Course Info Instructor: Tonci Antunovic, 6156 Math Sciences Building, tantunovic@math.ucla.edu, www.math.ucla.edu/tantunovic Instructor Oce Hours: Monday 11-12:30 and Thursday 5-6:30 in 6156 M
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Course: Sftwr-scntfc Cmpttn
Environment12: SustainabilityandtheEnvironment Summer2015 UCLAInstituteoftheEnvironment AndSustainability Page 1 of 5 SustainabilityandtheEnvironment Summer2015 Synopsis: There are a number of social, demographic, and ecological changes concurrently affec
School: UCLA
Course: SYSTMS-DIFFNTL EQTN
Math 131 A/1: Analysis, UCLA, Spring 2015 Instructor name Dr. Roman Taranets, Department of Mathematics, UCLA E-mail: taranets @ math.ucla.edu Website: www.math.ucla.edu/~taranets Office Phone: (310) 825-4746 Office: MS 7360 Office Hours: MWF 9:00 am
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Course: SYSTMS-DIFFNTL EQTN
MATH 135-2 Instructor: Sungjin Kim, MS 6617A, E-mail: i707107@math.ucla.edu Meeting Time and Location: MWF 3:00 - 3:50 PM, Room: MS 5200 O ce Hours: MW 11:45 PM - 12:30 PM, Room: MS 6617A Textbook: G. Simmons, Dierential Equations (2nd) with Applications
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Course: Differential Equations
Math 33B, Differential Equations Lecture 1, Spring 2015 Instructor: Will Conley Email: wconley@ucla.edu Office: MS 6322 Office Hours: Tentatively Wednesdays 3:004:00 Thursdays 12:001:30 and 3:004:00, and by appointment Website: http:/www.math.ucla.edu/~wc
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Math 174E (Summer 2014) Mathematics of Finance Instructor: David W. Taylor Time/Location: MWR 11:00am-12:50pm in MS 5137. Text: Options, Futures, and Other Derivatives (8th edition) by John C. Hull. Instructor: David W. Taylor Oce Hours: TBA Email Address
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Math 174E, Lecture 1 (Winter 2014) Mathematics of Finance Instructor: David W. Taylor Time/Location: MWF 3:00pm-3:50pm in MS 5127. Text: Options, Futures, and Other Derivatives (8th edition) by John C. Hull. Instructor: David W. Taylor Oce Hours: Monday a
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Math 3B Integral calculus and differential equations Prof. M. Roper Young Hall CS 76, MWF 2-2.50pm. All inquiries about this class should be posted to the class Piazza page. The only emails that I will respond to are questions about how to login to Piazza
School: UCLA
Course: Calculus
Sample Syllabus Philosophical Analysis of Contemporary Moral Issues Winter 2015, MW 8-9:50 Instructor: Robert Hughes (hughes@humnet.ucla.edu) Instructor office hours: Dodd 378, M 11-12 and W 10-11 or by appointment TAs: Kelsey Merrill (kmerrill@humnet.ucl
School: UCLA
Course: Math 170
Probability Theory, Math 170A, Winter 2015 - Course Info Instructor: Tonci Antunovic, 6156 Math Sciences Building, tantunovic@math.ucla.edu, www.math.ucla.edu/~tantunovic Instructor Oce Hours: Monday 10-12am and Wednesday 9-10am in 6156 Math Sciences Buil
School: UCLA
Course: Math 1
Math1 Fall2012 Lecture1(8am),Lecture2(10am)andLecture3(1pm) Instructor Paige Greene E-Mail: paige@math.ucla.edu Office Number: MS 6617 B Office Hours: Mon and Wed (copy from me in class) Course Goals: The purpose of Math 1 is to give you a strong preparat
School: UCLA
Math33BDifferentialEquations Winter2015,lecture3 Time:MWF3:003:50p.m. Room:MS4000A Discussionsections: 3A T3:00P3:50PGEOLOGY6704 3B R3:00P3:50PGEOLOGY4645 Instructor:YuZhang Office:MS6617F Officehours:Monday2:003:00p.m. Wednesday4:006:00p.m. Email:yuzhang
School: UCLA
Math 3B Integral calculus and differential equations Prof. M. Roper Young Hall CS 76, MWF 2-2.50pm. All inquiries about this class should be posted to the class Piazza page. The only emails that I will respond to are questions about how to login to Piazza
School: UCLA
Course: Actuarial Math
Math 172A: Syllabus, Structure, Details and Policies (Fall 2014) Introduction to Financial Mathematics This course is designed to provide an understanding of basic concepts of actuarial/financial mathematics. The course touches most (but not all) syllabus
School: UCLA
Course: Abstract Algebra
Math 32B (Calculus of several variables), Section 2 Fall 2013 MWF 2:00-2:50 pm, 1178 Franz Hall UCLA Instructor: William Simmons, MS 6617A, simmons@math.ucla.edu. Oce Hours (held in MS 6617A): M: 8:00-8:55 am T: 10:30-11:30 am W: 3:30-4:30 pm Teaching Ass
School: UCLA
Math 115A, Linear Algebra, Lecture 6, Fall 2014 Exterior Course Website: http:/www.math.ucla.edu/heilman/115af14.html Prerequisite: MATH 33A, Linear algebra and applications. Course Content: Linear independence, bases, orthogonality, the Gram-Schmidt proc
School: UCLA
Math 32A, Calculus of Several Variables Lecture 1, Winter 2014 Instructor: Will Conley Email: wconley@ucla.edu Office: MS 6322 Office Hours: Tentatively Wednesdays 11:3012:30, Thursdays 1:302:30, and any other time by appointment. Website: http:/www.math.
School: UCLA
Math 33A, Section 2 Linear Algebra and Applications Winter 2014 Instructor: Professor Alan J. Laub Dept. of Mathematics / Electrical Engineering Math Sciences 7945 phone: 310-825-4245 e-mail: laub@ucla.edu Lecture: MWF 1:00 p.m. 1:50 p.m. Math Sciences 40
School: UCLA
Math1 Fall2012 Lecture1(8am),Lecture2(10am)andLecture3(1pm) Instructor Paige Greene E-Mail: paige@math.ucla.edu Office Number: MS 6617 B Office Hours: Mon and Wed (copy from me in class) Course Goals: The purpose of Math 1 is to give you a strong preparat
School: UCLA
Differential Equations MATH 33B-1 Instructor Details: Lecture 1: Instructor: Office: Office hours: MWF 12PM-12:50PM in MOORE 100 Prof. Zachary Maddock MS 6236 (MS := Mathematical Sciences) Mondays 1:30PM-2:30PM Wednesdays 9:50AM-10:50AM, 1:30PM-2:30PM Tea
School: UCLA
Course: Linear Algebra
Course information and Policy, v. of Oct.1st, 2013 Marc-Hubert NICOLE Linear Algebra with Applications Math 33A-1, Lecture 1, Fall 2013. U.C.L.A. -Instructor Marc-Hubert NICOLE Lectures: MWF 8:00-8:50 AM in Rolfe 1200. Ofce: MS 5230 (see Ofce Hours belo
School: UCLA
Course: Linear Algebra
Winter 2011, UCLA Department of Economics Economics 106G: Introduction to Game Theory Instructor: Ichiro Obara Bunche 9381 E-mail: iobara@econ.ucla.edu Web: http:/www.econ.ucla.edu/iobara Office Hour: Tuesday 1:00pm - 2:30pm or by appointment. Time and Lo
School: UCLA
Course: Linear Algebra
Math 115A Fall 2012 Professor: David Gieseker Oce: Math Sciences 5636. Phone: 206-6321, email: dag at math ucla edu Oce Hours: M, W, F 9:30-10, 11-11:30. M,W 2-2:30 and by appointment. Text: Linear Algebra, Friedberg, Insel, Spence. Material to be covered
School: UCLA
S YLLABUS : P HYSICS 1B, L ECTURE 1, W INTER 2012 I NSTRUCTOR : Prof. Troy Carter 4-909 PAB (310) 825-4770 tcarter@physics.ucla.edu O FFICE H OURS : M 10-11AM, T 1PM-2PM, F 1-2PM, other times by appointment C OURSE A DMINISTRATOR : Elaine Dolalas (handles
School: UCLA
Course: Introduction Of Complex Analysis
Math 132: Complex Analysis for Applications INSTRUCTOR: Kefeng Liu TIME and LOCATION: MWF 9:00-9:50AM, MS5117. OFFICE HOURS: TT 9:00-10:00AM in MS 6330 (Or by appointment) SYLLABUS: Official Syllabus Text: Complex Analysis by Tom Gamelin TA: Wenjian Liu,
School: UCLA
Math 3C, Probability for Life Sciences Students, Lecture 2, Spring 2012 Instructor: Thomas Sinclair E-mail: thomas.sinclair@math.ucla.edu Oce: Math Sciences (MS) 7304 Oce Hours: M 3:004:30, W 11:0012:30 TAs: 2a,b Theodore Gast Oce: MS 2961 Oce Hours: TBA
School: UCLA
Mathematics 31B: Integration and Innite Series. Fall 2012 Instructor: David Weisbart Email: dweisbar@math.ucla.edu Textbook: J. Rogawski, Single Variable Calculus, 2nd Ed., W.H. Freeman & Co. 31B Teaching Statement: Along with 31A, this course is the foun
School: UCLA
Course: Linear Algebra
Physiological Science 5 Issues in Human Physiology: Diet and Exercise Winter 2012 Instructor: Joseph Esdin, Ph.D. Office Hours: Mon 12:30-1:20 pm & Wed 10:30-11:20 am Office: 3326 Life Sciences Building Phone: (310) 825-4118 Email: yezzeddi@ucla.edu TA: D
School: UCLA
Course: Linear Algebra
Mathematics 33B: Dierential Equations. Winter 2012 Instructor: David Weisbart Email: dweisbar@math.ucla.edu Textbook: Polking, Boggess, Arnold, Dierential Equations, 2nd Ed., Pearson. 33B Teaching Statement: Since the time of Newton, the language of diere