Lecture 1: Basics of Math and Economics AGEC 352 Spring 2011 – January 12 R. Keeney.

Slides:



Advertisements
Similar presentations
The simplex algorithm The simplex algorithm is the classical method for solving linear programs. Its running time is not polynomial in the worst case.
Advertisements

IEOR 4004: Introduction to Operations Research Deterministic Models January 22, 2014.
Linear Programming. Introduction: Linear Programming deals with the optimization (max. or min.) of a function of variables, known as ‘objective function’,
Assignment (6) Simplex Method for solving LP problems with two variables.
The Simplex Method The geometric method of solving linear programming problems presented before. The graphical method is useful only for problems involving.
Managerial Decision Modeling with Spreadsheets
Linear Programming Introduction George B Dantzig developed LP in It is a problem solving approach designed to help managers/decision makers in planning.
Lecture 6: Algorithm Approach to LP Soln AGEC 352 Fall 2012 – Sep 12 R. Keeney.
Lecture 7: Linear Programming in Excel AGEC 352 Spring 2011 – February 9, 2011 R. Keeney.
Lecture 9: Optimization with a Min objective AGEC 352 Spring 2011 – February 16 R. Keeney.
Lecture 8: LP in Excel (Review Assign. 1) AGEC 352 Spring 2011 – February 14 R. Keeney.
Lecture 2: Economics and Optimization AGEC 352 Spring 2011 – January 19 R. Keeney.
AGEC 340 – International Economic Development Course slides for week 6 (Feb. 16 & 18) The Microeconomics of Development: Are low-income people “poor but.
Lecture 5: Objective Equations AGEC 352 Spring 2011 – January 31 R. Keeney.
Lecture 4: Feasible Space and Analysis AGEC 352 Spring 2011 – January 26 R. Keeney.
HAWKES LEARNING SYSTEMS math courseware specialists Copyright © 2011 Hawkes Learning Systems. All rights reserved. Hawkes Learning Systems: Introductory.
Basic LP Problem McCarl and Spreen Chapter 2 LP problem is linear form of Mathematical Program This formulation may also be expressed in matrix notation.
Lecture 35 Constrained Optimization
1 2TN – Linear Programming  Linear Programming. 2 Linear Programming Discussion  Requirements of a Linear Programming Problem  Formulate:  Determine:Graphical.
Today’s quiz on 8.2 A Graphing Worksheet 1 will be given at the end of class. You will have 12 minutes to complete this quiz, which will consist of one.
Please open your laptops, log in to the MyMathLab course web site, and open Daily Quiz 16. IMPORTANT NOTE: If you have time left out of your five minutes.
FORMULATION AND GRAPHIC METHOD
Algebra II March 2 nd Students should complete warm-up problems. Given a graph of a system of equations students will be able to determine how many solutions.
Linear Programming Models: Graphical and Computer Methods
Introduction to Linear Programming
Thinking Mathematically Algebra: Graphs, Functions and Linear Systems 7.3 Systems of Linear Equations In Two Variables.
LINEAR PROGRAMMING SIMPLEX METHOD.
Welcome! Econ A494 Math Econ & Advanced Micro Theory Spring 2013 Prof. Jim Murphy.
Chapter 19 Linear Programming McGraw-Hill/Irwin
Lecture 6: Algorithm Approach to LP Soln AGEC 352 Fall 2012 – Sep 12 R. Keeney.
Lecture 2: Economics and Optimization AGEC 352 Fall 2012 – August 27 R. Keeney.
Linear Programming: Basic Concepts
McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. 6S Linear Programming.
Lecture #9. Review Homework Set #7 Continue Production Economic Theory: product-product case.
Table of Contents Solving Linear Systems of Equations - Dependent Systems The goal in solving a linear system of equations is to find the values of the.
Introduction A GENERAL MODEL OF SYSTEM OPTIMIZATION.
Management Science – MNG221 Linear Programming: Graphical Solution.
Decision Making Under Risk and Uncertainty: An Overview Lecture II.
ENM 503 Lesson 1 – Methods and Models The why’s, how’s, and what’s of mathematical modeling A model is a representation in mathematical terms of some real.
CDAE Class 12 Oct. 5 Last class: Quiz 3 3. Linear programming and applications Today: Result of Quiz 3 3. Linear programming and applications Next.
CSE 3802 / ECE 3431 Numerical Methods in Scientific Computation
Response surfaces. We have a dependent variable y, independent variables x 1, x 2,...,x p The general form of the model y = f(x 1, x 2,...,x p ) +  Surface.
Linear Programming Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill.
Solving a System of Equations by SUBSTITUTION. GOAL: I want to find what x equals, and what y equals. Using substitution, I can say that x = __ and y.
Substitution Method: 1. Solve the following system of equations by substitution. Step 1 is already completed. Step 2:Substitute x+3 into 2 nd equation.
Solving Linear Systems by Substitution O Chapter 7 Section 2.
10/2 The simplex algorithm. In an augmented matrix, if a column has a 1 and all other entries 0, it is said to be ‘in solution’. The 1 is called a ‘pivot’
Lecture 3: Introductory Spreadsheet Modeling AGEC 352 Spring 2012 – January 23 R. Keeney.
1 Linear Programming (LP) 線性規劃 - George Dantzig, 1947.
CDAE Class 13 Oct. 10 Last class: Result of Quiz 3 3. Linear programming and applications Class exercise 5 Today: 3. Linear programming and applications.
Homework Assignment and Labs Monday  Last few minutes of class I will introduce lab  Only the lab (part I) will be posted  You will need to work through.
Chapter 1 Introduction n Introduction: Problem Solving and Decision Making n Quantitative Analysis and Decision Making n Quantitative Analysis n Model.
Monday WARM-UP: TrueFalseStatementCorrected Statement F 1. Constraints are conditions written as a system of equations Constraints are conditions written.
Simplex Method for solving LP problems with two variables.
Lecture 8: Optimization with a Min objective AGEC 352 Spring 2012 – February 8 R. Keeney.
3rd 9 weeks.
Ch : Solving Systems of Equations Algebraically.
1 Optimization Techniques Constrained Optimization by Linear Programming updated NTU SY-521-N SMU EMIS 5300/7300 Systems Analysis Methods Dr.
MCCARL AND SPREEN TEXT CH. 2 T Y/MCCARL-BRUCE/BOOKS.HTM Lecture 2: Basic LP Formulation.
Systems of Linear Equations. Solve a System of Equations by Graphing Objectives: Solve a System of Equations by Graphing Standards: Learn and apply geometric.
Introduction and Preliminaries D Nagesh Kumar, IISc Water Resources Planning and Management: M4L1 Dynamic Programming and Applications.
OBJ: Solve Linear systems graphically & algebraically Do Now: Solve GRAPHICALLY 1) y = 2x – 4 y = x - 1 Do Now: Solve ALGEBRAICALLY *Substitution OR Linear.
Final Exam Information These slides and more detailed information will be posted on the webpage later…
Decision Support Systems
Agricultural Economic Students and Faculty!
Introduction to linear programming (LP): Minimization
Systems of equations Internal assessment 3.15.
The Simplex Method The geometric method of solving linear programming problems presented before. The graphical method is useful only for problems involving.
MATH 1310 Session 2.
Graphical solution A Graphical Solution Procedure (LPs with 2 decision variables can be solved/viewed this way.) 1. Plot each constraint as an equation.
Presentation transcript:

Lecture 1: Basics of Math and Economics AGEC 352 Spring 2011 – January 12 R. Keeney

Basic Algebra Number of equations Number of unknowns What relationship between these two is required to solve for the unknowns?

Applied Algebra 20 acres of land 40 hours of labor Planting requirements ◦ Corn = 1 acre of land; 2 hours of labor ◦ Soybean = 1 acre of land; 2 hours of labor What’s an algebraic description of my situation assuming I will use all resources and plant some combination of these two crops?

Applied Algebra Let C = planted corn Let B = planted soybeans C + B = 20 2C + 2B = 40 Can we ‘solve’ this?

‘Solving’ C + B = 20 2C + 2B = 40 C = 20 – B (rewrite 1 st equation) ◦ Substitute into 2 nd equation 2*(20-B) + 2B = – 2B + 2B = B = 2B + 40???

‘Solving’ C + B = 20 2C + 2B = 40 ◦ We can divide the second equation by 2 without changing the relationship ½*(2C + 2B = 40) => C + B = 20 The 2 nd equation provides no ‘different’ information about my planting problem ◦ Tradeoffs between the two crops ◦ Limits I face in my planting

‘Solution’? B = 2B + 40 ◦ Any value works for B ◦ Once you plug in a choice for B, then you just need to set the value of C to make sure the equation B + C = 20 holds E.g. : set B = 50 => C = -30 But, we might not want a value of planted acres that is < 0 so we could change our problem

‘Solution’? Choose values of B and C with ◦ 1) B + C = 20 ◦ B >=0, C>=0  These are called non-negativity conditions Then B will be some choice on the interval [0,20] ◦ C = 20 – B What then is your solution to this problem?

Graphical ‘Solution’ Any combination that appears on the line connecting (0,20) and (20,0) is a legitimate ‘solution’.

Need more information, some economics What would we use to make a choice among the infinite combinations that satisfy the resource (land, labor) equations?

Economic information Corn Net Returns/acre ◦ $100 Soybean Net Returns/acre ◦ $50 First, how do we represent this information mathematically?

Back to algebra The equation should describe total net returns, so let’s call that R. ◦ Every acre of corn is $100 so that gives  100*C ◦ Every acre of soybeans is $50  50*B R = 100C + 50B

Collecting our mathematical information we have… C + B =20 R = 100C + 50B That’s two equations for 3 variables We’re no better off algebraically with the new information

But, we know the solution if… We are willing to assume that the operator with limited land and labor wants to maximize net returns Step 1: Compare the net returns between the 2 crops (C > B) Step 2: Choose to produce as much of that crop as is feasible (C = 20) Step 3: If resources remain, use those for the other crop (B = 0)

Optimization The optimization assumption takes care of R for us Says, 1 st find B and C that will make R bigger than any other value it can have Then, calculate R at the end C = 20, B =0 R = 2,000

Simple with two choices When we have a large number of choices this gets more complicated Spreadsheet modeling ◦ Formulate the model on paper ◦ Input it correctly into a spreadsheet ◦ Solve  Graphical methods  Algorithm methods ◦ Understand, interpret, and communicate the final solution

In general Most of the work in this course is in developing the mathematical representation of the problem ◦ Identifying objectives that decision makers use as optimization criteria ◦ Identifying choices available to the decision maker that adjust their objective ◦ Identifying the limits decision makers control in adjusting their objective

Math and Computing We will get better at writing the equations and building spreadsheets through repetition In lecture we will often focus on how our decision problems relate to basic economic principles as you might have seen in AGEC 203 or something similar

Next Week Monday is a holiday Tuesday there will not be a lab session, I will post the first assignment that day Wednesday ◦ Discuss assignment and lab procedures which begin in week 3 ◦ Review some of the stuff from the 2 nd page of the level exam  Calculus, elasticity, more optimization