DETERMINISTIC EQUIVALENTS FOR OPTIMIZING
AND SATISFICING UNDER CHANCE
CONSTRAINTS!
A. Chames
Northwestern University, Bvanston, III
and W. W. Cooper
Carnegie Institute of Technology, Pittsburgh, Pa
(E
Z00_REND1011_11_SE_MOD4 PP3.QXD
2/21/11
12:49 PM
Page M4-1
MODULE
4
Game Theory
LEARNING OBJECTIVES
After completing this supplement, students will be able to:
1. Understand the principles of zero-sum
ANSWERS to Midterm 2 questions
1. (20 points.) Prove that the intersection of two convex set is also convex. (Hint: use definition of convex
set and formal definition of intersection of two sets: ( )
Chapter 2
Linear programming
1
Introduction
Many management decisions involve trying to make the
most effective use of an organizations resources.
Resources typically include machinery, labor, money
ANSWERS TO MIDTERM EXAM 1 QUESTIONS
1. (30 pts.)
x1 = the number of soldiers produced per week
x2 = the number of trains produced per week
si+ = amount by which the ith goal level is exceeded.
si- = a
College of Management, NCTU
Operation Research I
Fall, 2008
Chap 4 The Simplex Method
The Essence of the Simplex Method
Recall the Wyndor problem
Max Z = 3x1 + 5x2
4
S.T. x1
2x2 12
3x1 + 2x2 18
x1, x
CHAPTER
LINEAR
PROGRAMMING
AND APPLICATIONS
7.1
7.2
7.3
7.4
7.5
7.6
7.7
7.8
7.9
7.to
Basic Concepts Linear Programming
in
Degenerate
LP's-Graphical Solution
Natural Occurrence Linear Constraints
of
Th
104 CHAPTER 2 Systems of Linear Equations and Matrices
where xi , x2 x3 x4 represent the amount, in millions of dollars, that must be produced to satisfy internal and external
demands of the four sect
A Tutorial on Convex Optimization
Haitham Hindi
Palo Alto Research Center (PARC), Palo Alto, California
email: [email protected]
Abstract In recent years, convex optimization has become a computational
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C H A P T E R
1
Decision Analysis
CONTENTS
1.1
A Decision Tree Model and its Analysis
Bill Sampras Summer Job Decision
1.2
Summary of the General Method of Deci
Rob J Hyndman
Forecasting using
8. Stationarity and Differencing
OTexts.com/fpp/8/1
Forecasting using R
1
Outline
1 Stationarity
2 Ordinary differencing
3 Seasonal differencing
4 Unit root tests
5 Bac
Introductory Time Series with R
Paul S.P. Cowpertwait and Andrew V. Metcalfe
2009
1.
1.1.
Time Series data
Purpose
1. Time series are analysed to understand the past and to predict the future.
2. Kyot
Forecasting Methods
Erkan Tre
Forecasting
Forecas(ng is the es(ma(on of the value of a
variable (or set of variables) at some future point in
(me.
Applica(ons for forecas(ng
Rob J Hyndman
Forecasting using
3. Autocorrelation and seasonality
OTexts.com/fpp/2/
OTexts.com/fpp/6/1
Forecasting using R
1
Outline
1 Time series graphics
2 Seasonal or cyclic?
3 Autocorrelation
For
Applied Time Series Analysis
FS 2012 Week 01
Marcel Dettling
Institute for Data Analysis and Process Design
Zurich University of Applied Sciences
[email protected]
http:/stat.ethz.ch/~dettling
E
Introductory Time Series with R
Paul S.P. Cowpertwait and Andrew V. Metcalfe
2009
4.1.
Basic Stochastic Models
4.1.1.
Purpose
1. The rst is based on an assumption that there is a
xed seasonal pattern
Introductory Time Series with R
Paul S.P. Cowpertwait and Andrew V. Metcalfe
2009
2.1.
Correlation
2.1.1.
Purpose
1. In many cases, consecutive variables will be correlated. If we identify such correl
Applied Time Series Analysis
FS 2012 Week 02
Marcel Dettling
Institute for Data Analysis and Process Design
Zurich University of Applied Sciences
[email protected]
http:/stat.ethz.ch/~dettling
E
Example
A company is planning the manufacture of a product for
March, April, May and June of next year. The demand
quantities are 520, 720, 520 and 620 units, respectively.
The company has 10 employee
NAME
1.
MATH 304
Examination 2
Page 1
[18 points]
(a) Find the following determinant. However, use only properties of determinants,
without calculating directly (that is without expanding along a colu
What is Operation Research?
What is Operation Research?
What is Operation Research?
During World War II, British military leaders asked
scientists and mathematicians to analyze several
military proble
Linear programming modeling and examples
Example 1
Each gallon of milk, pound of cheese, and pound of
apple produces a known number of milligrams of
protein and vitamins A, B, and C, as given in follo
OPIM 915
Final Examination Spring 2015
Read at least twice before looking at the exam questions.
Sign the NO CHEATING FORM and indicate the time at which you started the exam. An
exam without this for
A Solution Manual and Notes for:
The Elements of Statistical Learning
by Jerome Friedman, Trevor Hastie,
and Robert Tibshirani
John L. Weatherwax
David Epstein
21 June 2013
Introduction
The Elements o
DSO 530: Simple Linear Regression
Abbass Al Sharif
Predicting House Value from Percent of Low Income Household
We are going to use a dataset called Boston which is part of the MASS package. It recorde
Regression and Classication with R
Yanchang Zhao
http:/www.RDataMining.com
R and Data Mining Workshop @ AusDM 2014
27 November 2014
Presented at Australian Customs and UJAT
1 / 44
Outline
Introduction