Operations Research. Overview of Areas of Faculty Research. Graduate Orientation Fall Department of Mathematical and Statistical Sciences
|
|
|
- Sarah Watts
- 5 years ago
- Views:
Transcription
1 Operations Research Overview of Areas of Faculty Research Department of Mathematical and Statistical Sciences Graduate Orientation Fall 2011
2 What is Operations Research? Agent-based modeling Airline scheduling Arrival process Assignment problem Birthdeath process Black-box optimization Blending problem Branch-and-bound Capital budgeting Cargo loading Combinatorial optimization Computational biology Critical path Data envelopment analysis Data mining Decision analysis Derivative-free optimization Deviation theory Diet problem Discrete event simulation Dynamic program Economic order problem Evolutionary game Facility location Fibonacci search Financial engineering First passage time Floor planning Forecasting Fuzziness Game theory Genetic algorithm Global optimization Golden-section search Graph theory Greedy heuristic Integer programming Inventory control Knapsack problem Linear programming Logistics Machine servicing Management science Managerial economics Manufacturing Markov chain Matrix game Matroid Maximum cut Mean return time Mean-variance model Metaheuristic Minimum cost Mixed-strategy game Monte-Carlo simulation Nash equilibrium Nearest neighbor Network flow Newsvendor problem Nonlinear program Operations management Optimization P = NP? Pareto frontier Perfect matching Policy modeling Population dynamics Pricing Prisoners dilemma Probability Production planning Project management Prospect theory Queueing Random walk Renewal process Resource allocation Revenue management Risk analysis Routing Scheduling Set covering Shadow price Shortest path Simulated annealing Simulation Spanning tree Staffing Statistical analysis Stochastic process Supply chain management System engineering Tabu search Transition probability Transportation Traveling salesman Two-person game Uncertainty Utility theory Wargaming Water flow management Work scheduling Zero-sum game
3 What can we do with Operations Research?
4 What do we do with Operations Research? O.R. and Biomedicine Computational Biology: data-intensive numerical methods Medicine/Healthcare: intensity modulated radiation therapy Bioinformatics: genomic alignment by hidden Markov models Super Simulation! uses random, probabilistic experiments to model uncertainty powerful to model stochastic and highly complex phenomena analyzes arrival, service, and departure of customers in queues Solving Puzzling Problems create and solve Sudoku puzzles using integer programming compute optimal matchings, tours, paths, and flows in graphs invent new theory and determine winning strategies of games
5 What can you do with Operations Research? (Cont.) Minimize the convex quadratic function f (x, y) = x 2 + y 2 The global minimum is (x, y) = (0, 0). Minimize the highly-nonconvex Rastrigin function Ras(x, y) = 20 + x 2 + y 2 10(cos 2πx + cos 2πy)
6 O.R. Faculty and Research Areas Stephen Billups Alexander Engau Weldon Lodwick Antolii Puhalskii Burton Simon Gary Kochenberger (Business School) Optimization, Global/Nonlinear programming, Numerical algorithms, Computational biology Optimization, Convex/Conic programming, Decision analysis, Management sciences Optimization, Fuzzy sets and systems, Interval analysis, Medical applications Probability theory, Stochastic Processes, Queueing theory, Large deviation theory Probability, Queueing, Simulation Models, Population dynamics, Evolutionary games Optimization, Discrete/Integer programming, Heuristic algorithms, Business applications
7 O.R. Course Rotation Fall 2011 Math 5310 Probability (TR 11:00-12:15 pm, Tolya) Math 5350 Mathematical Theory of Interest (TR 2:00-3:15 pm, Tolya) Math 5593 Linear Programming (MW 3:30-4:45 pm, Alex) Math 5779 Math Clinic: Simulation Optimization (TR 3:30-4:45 pm, Steve) Math 5792 Probabilistic Modeling (TR 12:30-1:45 pm, Burt) Spring 2012 Math 5490 Network Flows Math 5779 Math Clinic Math 7384 Mathematical Probability Math 7825 Topics in Optimization Fall 2012 Math 6380 Stochastic Processes Math 6595 Computational Methods in Nonlinear Programming Spring 2013 Math 5794 Optimization Modeling Math 7593/7594/7595 Advanced (Non)Linear/Integer Programming
8 Anything you like to know? Today is Pierre de Fermat s 410th birthday! Cuius rei demonstrationem mirabilem sane detexi. Hanc marginis exiguitas non caperet. Welcome to our Department and to UC Denver!
DETERMINISTIC OPERATIONS RESEARCH
DETERMINISTIC OPERATIONS RESEARCH Models and Methods in Optimization Linear DAVID J. RADER, JR. Rose-Hulman Institute of Technology Department of Mathematics Terre Haute, IN WILEY A JOHN WILEY & SONS,
Origins of Operations Research: World War II
ESD.83 Historical Roots Assignment METHODOLOGICAL LINKS BETWEEN OPERATIONS RESEARCH AND STOCHASTIC OPTIMIZATION Chaiwoo Lee Jennifer Morris 11/10/2010 Origins of Operations Research: World War II Need
CIS 192: Artificial Intelligence. Search and Constraint Satisfaction Alex Frias Nov. 30 th
CIS 192: Artificial Intelligence Search and Constraint Satisfaction Alex Frias Nov. 30 th What is AI? Designing computer programs to complete tasks that are thought to require intelligence 4 categories
Index. optimal package assignments, 143 optimal package assignments, symmetrybreaking,
A Abstract approach, 79 Amphibian coexistence aquarium, 7 executes, 16 model, 13 15 modeling languages, 11 12 MPSolver, 12 OR-Tools library, 12 preys, 7 run a model, 17 18 solution, 17 three-step approach,
Industrial And Manufacturing Systems (IMSE)
Industrial And Manufacturing Systems (IMSE) 1 Industrial And Manufacturing Systems (IMSE) IMSE 1000: Introduction to Industrial Introduction to industrial engineering profession, the Industrial and Manufacturing
Machine Learning for Software Engineering
Machine Learning for Software Engineering Introduction and Motivation Prof. Dr.-Ing. Norbert Siegmund Intelligent Software Systems 1 2 Organizational Stuff Lectures: Tuesday 11:00 12:30 in room SR015 Cover
Subject Index. Journal of Discrete Algorithms 5 (2007)
Journal of Discrete Algorithms 5 (2007) 751 755 www.elsevier.com/locate/jda Subject Index Ad hoc and wireless networks Ad hoc networks Admission control Algorithm ; ; A simple fast hybrid pattern-matching
IE 102 Spring Routing Through Networks - 1
IE 102 Spring 2017 Routing Through Networks - 1 The Bridges of Koenigsberg: Euler 1735 Graph Theory began in 1735 Leonard Eüler Visited Koenigsberg People wondered whether it is possible to take a walk,
INEN 420 Final Review
INEN 420 Final Review Office Hours: Mon, May 2 -- 2:00-3:00 p.m. Tues, May 3 -- 12:45-2:00 p.m. (Project Report/Critiques due on Thurs, May 5 by 5:00 p.m.) Tuesday, April 28, 2005 1 Final Exam: Wednesday,
Mathematics of Optimization Viewpoint of Applied Mathematics Logic of Optimization
Global Focus on Knowledge Lecture (The World of Mathematics #7) 2007 06 07 Mathematics of Optimization Viewpoint of Applied Mathematics Logic of Optimization Kazuo Murota Department. of Mathematical Engineering
Optimization with Multiple Objectives
Optimization with Multiple Objectives Eva K. Lee, Ph.D. eva.lee@isye.gatech.edu Industrial & Systems Engineering, Georgia Institute of Technology Computational Research & Informatics, Radiation Oncology,
INDUSTRIAL ENGINEERING & MANAGEMENT (IEM)
Industrial Engineering & Management (IEM) 1 INDUSTRIAL ENGINEERING & MANAGEMENT (IEM) IEM 2903 Introduction to Manufacturing and Service Systems Prerequisites: ENGR 1111; MATH 2144. Description: Introduction
OR 674 DYNAMIC PROGRAMMING Rajesh Ganesan, Associate Professor Systems Engineering and Operations Research George Mason University
OR 674 DYNAMIC PROGRAMMING Rajesh Ganesan, Associate Professor Systems Engineering and Operations Research George Mason University Ankit Shah Ph.D. Candidate Analytics and Operations Research (OR) Descriptive
Stochastic Algorithms
Stochastic Algorithms Some of the fastest known algorithms for certain tasks rely on chance Stochastic/Randomized Algorithms Two common variations Monte Carlo Las Vegas We have already encountered some
Index. ,a-possibility efficient solution, programming, 275
Index,a-possibility efficient solution, 196 0-1 programming, 275 aggregation, 31 of fuzzy number, 55 operator, 37, 45 agreement-discordance principle, 61 agricultural economics, 205 Arrow's impossibility
Integer Programming and Network Modeis
H.A. Eiselt C.-L. Sandblom Integer Programming and Network Modeis With Contributions by K. Spielberg, E. Richards, B.T. Smith, G. Laporte, B.T. Boffey With 165 Figures and 43 Tables &m Springer CONTENTS
56:272 Integer Programming & Network Flows Final Exam -- December 16, 1997
56:272 Integer Programming & Network Flows Final Exam -- December 16, 1997 Answer #1 and any five of the remaining six problems! possible score 1. Multiple Choice 25 2. Traveling Salesman Problem 15 3.
Industrial and Systems Engineering (ISE)
Lehigh University 2018-2019 1 Industrial and Systems Engineering (ISE) Courses ISE 100 Industrial Employment 0 Usually following the junior year, students in the industrial engineering curriculum are required
Discrete Covering. Location. Problems. Louis. Luangkesorn. Housekeeping. Dijkstra s Shortest Path. Discrete. Covering. Models.
Network Design Network Design Network Design Network Design Office Hours Wednesday IE 079/079 Logistics and Supply Chain Office is closed Wednesday for building renovation work. I will be on campus (or
OPTIMIZATION. joint course with. Ottimizzazione Discreta and Complementi di R.O. Edoardo Amaldi. DEIB Politecnico di Milano
OPTIMIZATION joint course with Ottimizzazione Discreta and Complementi di R.O. Edoardo Amaldi DEIB Politecnico di Milano edoardo.amaldi@polimi.it Website: http://home.deib.polimi.it/amaldi/opt-16-17.shtml
Optimization. Using Analytic Solver Platform REVIEW BASED ON MANAGEMENT SCIENCE
Optimization Using Analytic Solver Platform REVIEW BASED ON MANAGEMENT SCIENCE What We ll Cover Today Introduction Frontline Systems Session Ι beta training program goals Overview of Analytic Solver Platform
METAHEURISTICS. Introduction. Introduction. Nature of metaheuristics. Local improvement procedure. Example: objective function
Introduction METAHEURISTICS Some problems are so complicated that are not possible to solve for an optimal solution. In these problems, it is still important to find a good feasible solution close to the
Bowie State University
Bowie State University Department of Mathematics Master of Science in Applied and Computational Mathematics Certificate in Applied and Computational Mathematics Department of Mathematics Crawford Science
STATISTICS (STAT) Statistics (STAT) 1
Statistics (STAT) 1 STATISTICS (STAT) STAT 2013 Elementary Statistics (A) Prerequisites: MATH 1483 or MATH 1513, each with a grade of "C" or better; or an acceptable placement score (see placement.okstate.edu).
ACO and other (meta)heuristics for CO
ACO and other (meta)heuristics for CO 32 33 Outline Notes on combinatorial optimization and algorithmic complexity Construction and modification metaheuristics: two complementary ways of searching a solution
INF Biologically inspired computing Lecture 1: Marsland chapter 9.1, Optimization and Search Jim Tørresen
INF3490 - Biologically inspired computing Lecture 1: Marsland chapter 9.1, 9.4-9.6 2017 Optimization and Search Jim Tørresen Optimization and Search 2 Optimization and Search Methods (selection) 1. Exhaustive
Math 3012 Combinatorial Optimization Worksheet
Math 3012 Combinatorial Optimization Worksheet Combinatorial Optimization is the way in which combinatorial thought is applied to real world optimization problems. Optimization entails achieving the sufficient
Optimization of Complex Systems with OptQuest
Optimization of Complex Systems with OptQuest MANUEL LAGUNA Graduate School of Business Administration University of Colorado, Boulder, CO 80309-0419 Manuel.Laguna@Colorado.EDU Latest revision: April 8,
Chapter 1. Introduction
Chapter 1 Introduction A Monte Carlo method is a compuational method that uses random numbers to compute (estimate) some quantity of interest. Very often the quantity we want to compute is the mean of
A Parallel Architecture for the Generalized Traveling Salesman Problem
A Parallel Architecture for the Generalized Traveling Salesman Problem Max Scharrenbroich AMSC 663 Project Proposal Advisor: Dr. Bruce L. Golden R. H. Smith School of Business 1 Background and Introduction
A NEW HEURISTIC ALGORITHM FOR MULTIPLE TRAVELING SALESMAN PROBLEM
TWMS J. App. Eng. Math. V.7, N.1, 2017, pp. 101-109 A NEW HEURISTIC ALGORITHM FOR MULTIPLE TRAVELING SALESMAN PROBLEM F. NURIYEVA 1, G. KIZILATES 2, Abstract. The Multiple Traveling Salesman Problem (mtsp)
Introduction to Algorithms Third Edition
Thomas H. Cormen Charles E. Leiserson Ronald L. Rivest Clifford Stein Introduction to Algorithms Third Edition The MIT Press Cambridge, Massachusetts London, England Preface xiü I Foundations Introduction
IE521 Convex Optimization Introduction
IE521 Convex Optimization Introduction Instructor: Niao He Jan 18, 2017 1 About Me Assistant Professor, UIUC, 2016 Ph.D. in Operations Research, M.S. in Computational Sci. & Eng. Georgia Tech, 2010 2015
Industrial and Manufacturing Engineering (IME)
Industrial and Manufacturing Engineering (IME) 1 Industrial and Manufacturing Engineering (IME) IME 111. Introduction to Industrial and Manufacturing Engineering. 3 Credits. Overview of industrial engineering
Rich Vehicle Routing Problems Challenges and Prospects in Exploring the Power of Parallelism. Andreas Reinholz. 1 st COLLAB Workshop
Collaborative Research Center SFB559 Modeling of Large Logistic Networks Project M8 - Optimization Rich Vehicle Routing Problems Challenges and Prospects in Exploring the Power of Parallelism Andreas Reinholz
The Facility Location Problem: Modeling and Solution Methods
The Facility Location Problem: Modeling and Fubin Qian (PhD Candidate) Molde University College, Specialized University in Logistics, Norway Outline The Set Covering Problem (SCP) The Maximal Covering
Design and Analysis of Algorithms CS404/504. Razvan Bunescu School of EECS.
Welcome Design and Analysis of Algorithms Razvan Bunescu School of EECS bunescu@ohio.edu 1 Course Description Course Description: This course provides an introduction to the modern study of computer algorithms.
8/19/13. Computational problems. Introduction to Algorithm
I519, Introduction to Introduction to Algorithm Yuzhen Ye (yye@indiana.edu) School of Informatics and Computing, IUB Computational problems A computational problem specifies an input-output relationship
Integer and Combinatorial Optimization
Integer and Combinatorial Optimization GEORGE NEMHAUSER School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta, Georgia LAURENCE WOLSEY Center for Operations Research and
Pre-requisite Material for Course Heuristics and Approximation Algorithms
Pre-requisite Material for Course Heuristics and Approximation Algorithms This document contains an overview of the basic concepts that are needed in preparation to participate in the course. In addition,
Data Mining Chapter 8: Search and Optimization Methods Fall 2011 Ming Li Department of Computer Science and Technology Nanjing University
Data Mining Chapter 8: Search and Optimization Methods Fall 2011 Ming Li Department of Computer Science and Technology Nanjing University Search & Optimization Search and Optimization method deals with
IE598 Big Data Optimization Summary Nonconvex Optimization
IE598 Big Data Optimization Summary Nonconvex Optimization Instructor: Niao He April 16, 2018 1 This Course Big Data Optimization Explore modern optimization theories, algorithms, and big data applications
Statistics (STAT) Statistics (STAT) 1. Prerequisites: grade in C- or higher in STAT 1200 or STAT 1300 or STAT 1400
Statistics (STAT) 1 Statistics (STAT) STAT 1200: Introductory Statistical Reasoning Statistical concepts for critically evaluation quantitative information. Descriptive statistics, probability, estimation,
A Combinatorial Algorithm for The Cardinality Constrained Portfolio Optimization Problem
0 IEEE Congress on Evolutionary Computation (CEC) July -, 0, Beijing, China A Combinatorial Algorithm for The Cardinality Constrained Portfolio Optimization Problem Tianxiang Cui, Shi Cheng, and Ruibin
Fundamentals of Integer Programming
Fundamentals of Integer Programming Di Yuan Department of Information Technology, Uppsala University January 2018 Outline Definition of integer programming Formulating some classical problems with integer
EM COURSE OFFERINGS SPRING 2019 See Timetable of Classes for more options
EM COURSE OFFERINGS SPRING 2019 See Timetable of Classes for more options EM 500 Thesis CRN 27464 Prof. Simonton CRN 28645 Prof. Yu EM 501 Capstone CRN 22010 Prof. Tolk EM 502 Registration for Use of Facilities
IEOR E4008: Computational Discrete Optimization
Yuri Faenza IEOR Department Jan 23th, 2018 Logistics Instructor: Yuri Faenza Assistant Professor @ IEOR from 2016 Research area: Discrete Optimization Schedule: MW, 10:10-11:25 Room: 303 Mudd Office Hours:
Combinatorial Search; Monte Carlo Methods
Combinatorial Search; Monte Carlo Methods Parallel and Distributed Computing Department of Computer Science and Engineering (DEI) Instituto Superior Técnico May 02, 2016 CPD (DEI / IST) Parallel and Distributed
SPATIAL OPTIMIZATION METHODS
DELMELLE E. (2010). SPATIAL OPTIMIZATION METHODS. IN: B. WHARF (ED). ENCYCLOPEDIA OF HUMAN GEOGRAPHY: 2657-2659. SPATIAL OPTIMIZATION METHODS Spatial optimization is concerned with maximizing or minimizing
Class Overview. Introduction to Artificial Intelligence COMP 3501 / COMP Lecture 2. Problem Solving Agents. Problem Solving Agents: Assumptions
Class Overview COMP 3501 / COMP 4704-4 Lecture 2 Prof. JGH 318 Problem Solving Agents Problem Solving Agents: Assumptions Requires a goal Assume world is: Requires actions Observable What actions? Discrete
The Algorithm Design Manual
Steven S. Skiena The Algorithm Design Manual With 72 Figures Includes CD-ROM THE ELECTRONIC LIBRARY OF SCIENCE Contents Preface vii I TECHNIQUES 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 1.10 2 2.1 2.2 2.3
Metaheuristic Development Methodology. Fall 2009 Instructor: Dr. Masoud Yaghini
Metaheuristic Development Methodology Fall 2009 Instructor: Dr. Masoud Yaghini Phases and Steps Phases and Steps Phase 1: Understanding Problem Step 1: State the Problem Step 2: Review of Existing Solution
LECTURE 20: SWARM INTELLIGENCE 6 / ANT COLONY OPTIMIZATION 2
15-382 COLLECTIVE INTELLIGENCE - S18 LECTURE 20: SWARM INTELLIGENCE 6 / ANT COLONY OPTIMIZATION 2 INSTRUCTOR: GIANNI A. DI CARO ANT-ROUTING TABLE: COMBINING PHEROMONE AND HEURISTIC 2 STATE-TRANSITION:
Machine Learning Techniques for the Smart Grid Modeling of Solar Energy using AI
Machine Learning Techniques for the Smart Grid Modeling of Solar Energy using AI Professor Dr. Wilfried Elmenreich Dr. Tamer Khatib Networked and Embedded Systems Overview Scope of this tutorial Meta-heuristic
6. Tabu Search 6.1 Basic Concepts. Fall 2010 Instructor: Dr. Masoud Yaghini
6. Tabu Search 6.1 Basic Concepts Fall 2010 Instructor: Dr. Masoud Yaghini Outline Tabu Search: Part 1 Introduction Illustrative Problems Search Space Neighborhood Structure Tabus Aspiration Criteria Termination
Randomized rounding of semidefinite programs and primal-dual method for integer linear programming. Reza Moosavi Dr. Saeedeh Parsaeefard Dec.
Randomized rounding of semidefinite programs and primal-dual method for integer linear programming Dr. Saeedeh Parsaeefard 1 2 3 4 Semidefinite Programming () 1 Integer Programming integer programming
Monte Carlo Methods; Combinatorial Search
Monte Carlo Methods; Combinatorial Search Parallel and Distributed Computing Department of Computer Science and Engineering (DEI) Instituto Superior Técnico November 22, 2012 CPD (DEI / IST) Parallel and
Numerical approach estimate
Simulation Nature of simulation Numericalapproachfor investigating models of systems. Data are gathered to estimatethe true characteristics of the model. Garbage in garbage out! One of the techniques of
About the Author. Dependency Chart. Chapter 1: Logic and Sets 1. Chapter 2: Relations and Functions, Boolean Algebra, and Circuit Design
Preface About the Author Dependency Chart xiii xix xxi Chapter 1: Logic and Sets 1 1.1: Logical Operators: Statements and Truth Values, Negations, Conjunctions, and Disjunctions, Truth Tables, Conditional
Data Structures and Algorithms
Data Structures and Algorithms Session 26. April 29, 2009 Instructor: Bert Huang http://www.cs.columbia.edu/~bert/courses/3137 Announcements Homework 6 due before last class: May 4th Final Review May 4th
DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING Question Bank Subject Name: CS6402- Design & Analysis of Algorithm Year/Sem : II/IV UNIT-I INTRODUCTION
Chendu College of Engineering & Technology (Approved by AICTE, New Delhi and Affiliated to Anna University) Zamin Endathur, Madurantakam, Kancheepuram District 603311 +91-44-27540091/92 www.ccet.org.in
CT79 SOFT COMPUTING ALCCS-FEB 2014
Q.1 a. Define Union, Intersection and complement operations of Fuzzy sets. For fuzzy sets A and B Figure Fuzzy sets A & B The union of two fuzzy sets A and B is a fuzzy set C, written as C=AUB or C=A OR
CS 440 Theory of Algorithms /
CS 440 Theory of Algorithms / CS 468 Algorithms in Bioinformaticsi Brute Force. Design and Analysis of Algorithms - Chapter 3 3-0 Brute Force A straightforward approach usually based on problem statement
Metaheuristic Optimization with Evolver, Genocop and OptQuest
Metaheuristic Optimization with Evolver, Genocop and OptQuest MANUEL LAGUNA Graduate School of Business Administration University of Colorado, Boulder, CO 80309-0419 Manuel.Laguna@Colorado.EDU Last revision:
Research Interests Optimization:
Mitchell: Research interests 1 Research Interests Optimization: looking for the best solution from among a number of candidates. Prototypical optimization problem: min f(x) subject to g(x) 0 x X IR n Here,
Classification of Optimization Problems and the Place of Calculus of Variations in it
Lecture 1 Classification of Optimization Problems and the Place of Calculus of Variations in it ME256 Indian Institute of Science G. K. Ananthasuresh Professor, Mechanical Engineering, Indian Institute
Thomas H. Cormen Charles E. Leiserson Ronald L. Rivest. Introduction to Algorithms
Thomas H. Cormen Charles E. Leiserson Ronald L. Rivest Introduction to Algorithms Preface xiii 1 Introduction 1 1.1 Algorithms 1 1.2 Analyzing algorithms 6 1.3 Designing algorithms 1 1 1.4 Summary 1 6
MLR Institute of Technology
Course Name : Engineering Optimization Course Code : 56021 Class : III Year Branch : Aeronautical Engineering Year : 2014-15 Course Faculty : Mr Vamsi Krishna Chowduru, Assistant Professor Course Objective
OPTIMIZATION EVOLUTIONARY ALGORITHMS. Biologically-Inspired and. Computer Intelligence. Wiley. Population-Based Approaches to.
EVOLUTIONARY OPTIMIZATION ALGORITHMS Biologically-Inspired and Population-Based Approaches to Computer Intelligence Dan Simon Cleveland State University Wiley DETAILED TABLE OF CONTENTS Acknowledgments
Optimal tour along pubs in the UK
1 From Facebook Optimal tour along 24727 pubs in the UK Road distance (by google maps) see also http://www.math.uwaterloo.ca/tsp/pubs/index.html (part of TSP homepage http://www.math.uwaterloo.ca/tsp/
Queuing Systems. 1 Lecturer: Hawraa Sh. Modeling & Simulation- Lecture -4-21/10/2012
Queuing Systems Queuing theory establishes a powerful tool in modeling and performance analysis of many complex systems, such as computer networks, telecommunication systems, call centers, manufacturing
Lecture outline. Graph coloring Examples Applications Algorithms
Lecture outline Graph coloring Examples Applications Algorithms Graph coloring Adjacent nodes must have different colors. How many colors do we need? Graph coloring Neighbors must have different colors
B553 Lecture 12: Global Optimization
B553 Lecture 12: Global Optimization Kris Hauser February 20, 2012 Most of the techniques we have examined in prior lectures only deal with local optimization, so that we can only guarantee convergence
Algorithmic Game Theory - Introduction, Complexity, Nash
Algorithmic Game Theory - Introduction, Complexity, Nash Branislav Bošanský Czech Technical University in Prague branislav.bosansky@agents.fel.cvut.cz February 25, 2018 About This Course main topics of
COMP 355 Advanced Algorithms Approximation Algorithms: VC and TSP Chapter 11 (KT) Section (CLRS)
COMP 355 Advanced Algorithms Approximation Algorithms: VC and TSP Chapter 11 (KT) Section 35.1-35.2(CLRS) 1 Coping with NP-Completeness Brute-force search: This is usually only a viable option for small
Algorithm Design Methods. Some Methods Not Covered
Algorithm Design Methods Greedy method. Divide and conquer. Dynamic Programming. Backtracking. Branch and bound. Some Methods Not Covered Linear Programming. Integer Programming. Simulated Annealing. Neural
Design and Analysis of Algorithms
CSE 101, Winter 2018 Design and Analysis of Algorithms Lecture 17: Coping With Intractability Class URL: http://vlsicad.ucsd.edu/courses/cse101-w18/ Branch-and-Bound (B&B) Variant of backtrack with costs
The Design of Approximation Algorithms
The Design of Approximation Algorithms David P. Williamson Cornell University David B. Shmoys Cornell University m Щ0 CAMBRIDGE UNIVERSITY PRESS Contents Preface page ix I An Introduction to the Techniques
Algorithms. Algorithms ALGORITHM DESIGN
Algorithms ROBERT SEDGEWICK KEVIN WAYNE ALGORITHM DESIGN Algorithms F O U R T H E D I T I O N ROBERT SEDGEWICK KEVIN WAYNE http://algs4.cs.princeton.edu analysis of algorithms greedy divide-and-conquer
Developing Optimization Algorithms for Real-World Applications
Developing Optimization Algorithms for Real-World Applications Gautam Ponnappa PC Training Engineer Viju Ravichandran, PhD Education Technical Evangelist 2015 The MathWorks, Inc. 1 2 For a given system,
Hybrid approach for solving TSP by using DPX Cross-over operator
Available online at www.pelagiaresearchlibrary.com Advances in Applied Science Research, 2011, 2 (1): 28-32 ISSN: 0976-8610 CODEN (USA): AASRFC Hybrid approach for solving TSP by using DPX Cross-over operator
OPERATIONS RESEARCH. Dr. Mohd Vaseem Ismail. Assistant Professor. Faculty of Pharmacy Jamia Hamdard New Delhi
OPERATIONS RESEARCH OPERATIONS RESEARCH By Dr. Qazi Shoeb Ahmad Professor Department of Mathematics Integral University Lucknow Dr. Shakeel Javed Assistant Professor Department of Statistics & O.R. AMU,
Innovative Systems Design and Engineering ISSN (Paper) ISSN (Online) Vol.5, No.1, 2014
Abstract Tool Path Optimization of Drilling Sequence in CNC Machine Using Genetic Algorithm Prof. Dr. Nabeel Kadim Abid Al-Sahib 1, Hasan Fahad Abdulrazzaq 2* 1. Thi-Qar University, Al-Jadriya, Baghdad,
Introduction to Linear Programming. Algorithmic and Geometric Foundations of Optimization
Introduction to Linear Programming Algorithmic and Geometric Foundations of Optimization Optimization and Linear Programming Mathematical programming is a class of methods for solving problems which ask
Real Coded Genetic Algorithm Particle Filter for Improved Performance
Proceedings of 2012 4th International Conference on Machine Learning and Computing IPCSIT vol. 25 (2012) (2012) IACSIT Press, Singapore Real Coded Genetic Algorithm Particle Filter for Improved Performance
IINSTITUTE OF AERONAUTICAL ENGINEERING (Autonomous) Dundigal, Hyderabad
Hall Ticket No Question Paper Code: AHS012 IINSTITUTE OF AERONAUTICAL ENGINEERING (Autonomous) Dundigal, Hyderabad - 500 043 MODEL QUESTION PAPER B. TechV Semester End Examinations (Regular), December
Using AIMMS for Monte Carlo Simulation
Using AIMMS for Monte Carlo Simulation Product Training Webinar Deanne Zhang November 16, 2016 Agenda > Introduction > Steps to do Monte Carlo simulation in AIMMS > A demo of running multiple simulations
Preface MOTIVATION ORGANIZATION OF THE BOOK. Section 1: Basic Concepts of Graph Theory
xv Preface MOTIVATION Graph Theory as a well-known topic in discrete mathematics, has become increasingly under interest within recent decades. This is principally due to its applicability in a wide range
CMSC 451: Lecture 22 Approximation Algorithms: Vertex Cover and TSP Tuesday, Dec 5, 2017
CMSC 451: Lecture 22 Approximation Algorithms: Vertex Cover and TSP Tuesday, Dec 5, 2017 Reading: Section 9.2 of DPV. Section 11.3 of KT presents a different approximation algorithm for Vertex Cover. Coping
Comparison of Heuristics for the Colorful Traveling Salesman Problem
Comparison of Heuristics for the Colorful Traveling Salesman Problem John Silberholz R.H. Smith School of Business University of Maryland Joint Work With: Andrea Raiconi, Raffaele Cerulli, Monica Gentili,
Class Overview. Introduction to Artificial Intelligence COMP 3501 / COMP Lecture 2: Search. Problem Solving Agents
Class Overview COMP 3501 / COMP 4704-4 Lecture 2: Search Prof. 1 2 Problem Solving Agents Problem Solving Agents: Assumptions Requires a goal Assume world is: Requires actions Observable What actions?
Applied Optimization and Metaheuristic Innovations
Conference Program Applied Optimization and Metaheuristic Innovations International Conference in Honor of the 70th Birthday of Academician Ivan V. Sergienko July 19-21, 2006 Yalta, Ukraine Conference
Stochastic Coalitional Games with Constant Matrix of Transition Probabilities
Applied Mathematical Sciences, Vol. 8, 2014, no. 170, 8459-8465 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ams.2014.410891 Stochastic Coalitional Games with Constant Matrix of Transition Probabilities
INTERNATIONAL INSTITUTE OF MANAGEMENT, ENGINEERING & TECHNOLOGY, JAIPUR (IIMET)
INTERNATIONAL INSTITUTE OF MANAGEMENT, ENGINEERING & TECHNOLOGY, JAIPUR (IIMET) UNIT-1 Q.1 What is the significance of using notations in analysis of algorithm? Explain the various notations in brief?
6 ROUTING PROBLEMS VEHICLE ROUTING PROBLEMS. Vehicle Routing Problem, VRP:
6 ROUTING PROBLEMS VEHICLE ROUTING PROBLEMS Vehicle Routing Problem, VRP: Customers i=1,...,n with demands of a product must be served using a fleet of vehicles for the deliveries. The vehicles, with given
CS 380 ALGORITHM DESIGN AND ANALYSIS
CS 380 ALGORITHM DESIGN AND ANALYSIS Lecture 1: Course Introduction and Motivation Text Reference: Chapters 1, 2 Syllabus Book Schedule Grading: Assignments/Projects/Exams/Quizzes Policies Late Policy
The Cross-Entropy Method for Mathematical Programming
The Cross-Entropy Method for Mathematical Programming Dirk P. Kroese Reuven Y. Rubinstein Department of Mathematics, The University of Queensland, Australia Faculty of Industrial Engineering and Management,
Solving Optimization Problems with MATLAB Loren Shure
Solving Optimization Problems with MATLAB Loren Shure 6 The MathWorks, Inc. Topics Introduction Least-squares minimization Nonlinear optimization Mied-integer programming Global optimization Optimization
A New Approach for Finding the Global Optimal Point Using Subdividing Labeling Method (SLM)
A New Approach for Finding the Global Optimal Point Using Subdividing Labeling Method (SLM) MasoumehVali Department of Mathematics, Dolatabad Branch, Islamic Azad University, Isfahan, Iran E-mail: vali.masoumeh@gmail.com
56:272 Integer Programming & Network Flows Final Examination -- December 14, 1998
56:272 Integer Programming & Network Flows Final Examination -- December 14, 1998 Part A: Answer any four of the five problems. (15 points each) 1. Transportation problem 2. Integer LP Model Formulation