Math443/543 Mathematical Modeling and Optimization

Slides:



Advertisements
Similar presentations
Algorithm Design Methods (I) Fall 2003 CSE, POSTECH.
Advertisements

1 Material to Cover  relationship between different types of models  incorrect to round real to integer variables  logical relationship: site selection.
ECE Longest Path dual 1 ECE 665 Spring 2005 ECE 665 Spring 2005 Computer Algorithms with Applications to VLSI CAD Linear Programming Duality – Longest.
Linear Programming. Introduction: Linear Programming deals with the optimization (max. or min.) of a function of variables, known as ‘objective function’,
1Other Network ModelsLesson 6 LECTURE SIX Other Network Models.
DMOR Networks. Graphs: Koenigsberg bridges Leonard Euler problem (1736)
Management Science 461 Lecture 2b – Shortest Paths September 16, 2008.
9.2 The Traveling Salesman Problem. Let us return to the question of finding a cheapest possible cycle through all the given towns: We have n towns (points)
Optimization Problems 虞台文 大同大學資工所 智慧型多媒體研究室. Content Introduction Definitions Local and Global Optima Convex Sets and Functions Convex Programming Problems.
Nonlinear Programming
Discrete Optimization Shi-Chung Chang. Discrete Optimization Lecture #1 Today: Reading Assignments 1.Chapter 1 and the Appendix of [Pas82] 2.Chapter 1.
Lecture 1: Introduction to the Course of Optimization 主講人 : 虞台文.
Computability and Complexity 23-1 Computability and Complexity Andrei Bulatov Search and Optimization.
Math 308 Discrete Mathematics Discrete Mathematics deals with “Separated” or discrete sets of objects (rather than continuous sets) Processes with a sequence.
Operations Management
Mathematical Modeling and Optimization: Summary of “Big Ideas”
The Theory of NP-Completeness
Network Optimization Models: Maximum Flow Problems In this handout: The problem statement Solving by linear programming Augmenting path algorithm.
Models Physical: Scale, Analog Symbolic: Drawings Computer Programs Mathematical: Analytical (Deduction) Experimental (Induction)
1 More Counting Techniques Possibility trees Multiplication rule Permutations Combinations.
Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy under contract.
Linear Programming Applications
Network Optimization Problems: Models and Algorithms
Solution methods for Discrete Optimization Problems.
1 Discrete Optimization. 2 The relationship between counting techniques/graph theory and discrete optimization Adding a goal (objective function) to a.
Approximation Algorithms Motivation and Definitions TSP Vertex Cover Scheduling.
1 Integrality constraints Integrality constraints are often crucial when modeling optimizayion problems as linear programs. We have seen that if our linear.
NetworkModel-1 Network Optimization Models. NetworkModel-2 Network Terminology A network consists of a set of nodes and arcs. The arcs may have some flow.
Chapter 4 An Introduction to Optimization
Package Transportation Scheduling Albert Lee Robert Z. Lee.
Algorithms for Network Optimization Problems This handout: Minimum Spanning Tree Problem Approximation Algorithms Traveling Salesman Problem.
Network Models (2) Tran Van Hoai Faculty of Computer Science & Engineering HCMC University of Technology Tran Van Hoai.
Network Models Tran Van Hoai Faculty of Computer Science & Engineering HCMC University of Technology Tran Van Hoai.
IT 60101: Lecture #201 Foundation of Computing Systems Lecture 20 Classic Optimization Problems.
Mathematical Modeling and Optimization: Summary of “Big Ideas”
Great Theoretical Ideas in Computer Science.
MODELING AND ANALYSIS OF MANUFACTURING SYSTEMS Session 12 MACHINE SETUP AND OPERATION SEQUENCING E. Gutierrez-Miravete Spring 2001.
15.082J and 6.855J and ESD.78J Lagrangian Relaxation 2 Applications Algorithms Theory.
Notes 5IE 3121 Knapsack Model Intuitive idea: what is the most valuable collection of items that can be fit into a backpack?
Lecture 6 – Integer Programming Models Topics General model Logic constraint Defining decision variables Continuous vs. integral solution Applications:
For Wednesday No reading No homework There will be homework for Friday, as well the program being due – plan ahead.
Models in I.E. Lectures Introduction to Optimization Models: Shortest Paths.
and 6.855J Lagrangian Relaxation I never missed the opportunity to remove obstacles in the way of unity. —Mohandas Gandhi.
CSE 589 Part VI. Reading Skiena, Sections 5.5 and 6.8 CLR, chapter 37.
Traveling Salesman Problem (TSP)
Dvir Shabtay Moshe Kaspi The Department of IE&M Ben-Gurion University of the Negev, Israel.
___________________________________________________________________________ ___________________________________________________________________________.
Integer Programming (정수계획법)
Group members: Ibrahim jan Qesar Habib Najeebullah
8/14/04 J. Bard and J. W. Barnes Operations Research Models and Methods Copyright All rights reserved Lecture 6 – Integer Programming Models Topics.
___________________________________________________________________________ ___________________________________________________________________________.
Lecture 6 – Integer Programming Models Topics General model Logic constraint Defining decision variables Continuous vs. integral solution Applications:
DEPARTMENT/SEMESTER ME VII Sem COURSE NAME Operation Research Manav Rachna College of Engg.
Review for E&CE Find the minimal cost spanning tree for the graph below (where Values on edges represent the costs). 3 Ans. 18.
Management Science 461 Lecture 7 – Routing (TSP) October 28, 2008.
Mathematical modeling To describe or represent a real-world situation quantitatively, in mathematical language.
Exhaustive search Exhaustive search is simply a brute- force approach to combinatorial problems. It suggests generating each and every element of the problem.
deterministic operations research
Graph Theory and Optimization
ENGM 535 Optimization Networks
Heuristics Definition – a heuristic is an inexact algorithm that is based on intuitive and plausible arguments which are “likely” to lead to reasonable.
Transportation, Assignment and Network Models
Integer Programming (정수계획법)
1.206J/16.77J/ESD.215J Airline Schedule Planning
Introduction Basic formulations Applications
Lecture 19-Problem Solving 4 Incremental Method
Integer Programming (정수계획법)
Lecture 19 Linear Program
Approximation Algorithms
REVIEW FOR EXAM 1 Chapters 3, 4, 5 & 6.
Presentation transcript:

Math443/543 Mathematical Modeling and Optimization

A schematic view of modeling/optimization process assumptions, abstraction,data,simplifications Real-world problem Mathematical model makes sense? change the model, assumptions? optimization algorithm Solution to real-world problem Solution to model interpretation

What is a model? Model: A schematic description Mathematical models of a system, theory, or phenomenon that accounts for its known or inferred properties and maybe used for further study of its characteristics. Mathematical models are abstract models describe the mathematical relationships among elements in a system In this class, mathematical models dealing with discrete optimization

Mathematical models in Optimization The general form of an optimization model: min or max f(x1,…,xn) (objective function) subject to gi(x1,…,xn) ≥ 0 (functional constraints) x1,…,xn  S (set constraints) x1,…,xn are called decision variables In words, the goal is to find x1,…,xn that satisfy the constraints; achieve min (max) objective function value.

Types of Optimization Models Stochastic (probabilistic information on data) Deterministic (data are certain) Discrete, Integer (S = Zn) Continuous (S = Rn) Linear (f and g are linear) Nonlinear (f and g are nonlinear)

What is Discrete Optimization? is a field of applied mathematics, combining techniques from combinatorics and graph theory, linear programming, theory of algorithms, to solve optimization problems over discrete structures.

Examples of Discrete Optimization Models: Traveling Salesman Problem (TSP) There are n cities. The salesman  starts his tour from City 1,  visits each of the cities exactly once,  and returns to City 1. For each pair of cities i,j there is a cost cij associated with traveling from City i to City j . Goal: Find a minimum-cost tour.

Examples of Discrete Optimization Models: Job Scheduling There are 4 jobs that should be processed on the same machine. (Can’t be processed simultaneously). Job k has processing time pk . Here is an example of a possible schedule: Goal: Find a schedule which minimizes the average completion time of the jobs. Job 3 Job 1 Job 4 Job 2 2 6 14 9 time

Examples of Discrete Optimization Models: Shortest Path Problem In a network, we have distances on arcs ; source node s and sink node t . Goal: Find a shortest path from the source to the sink. 3 a d 4 1 1 1 4 7 2 s c t 2 2 1 2 5 b e

Problems that can be modeled and solved by discrete optimization techniques Scheduling Problems (production, airline, etc.) Network Design Problems Facility Location Problems Inventory management Transportation Problems

Problems that can be modeled and solved by discrete optimization techniques Minimum spanning tree problem Shortest path problem Maximum flow problem Min-cost flow problem Assignment Problem

Solution Methods for Discrete Optimization Problems Integer Programming Network Algorithms Dynamic Programming Approximation Algorithms