Integer Programming Models
|
|
- Candice Willis
- 6 years ago
- Views:
Transcription
1 Integer Programming Models Fabio Furini December 10, 2014 Integer Programming Models 1
2 Outline 1 Combinatorial Auctions 2 The Lockbox Problem 3 Constructing an Index Fund Integer Programming Models 2
3 Integer Programming Models Constructing financial models Integer Programming Models 3
4 Combinatorial Auctions An auction is a process of buying and selling goods or services by offering them up for bid, taking bids, and then selling the item to the highest bidder. In economic theory, an auction may refer to any mechanism or set of trading rules for exchange. the value that a bidder has for a set of items may not be the sum of the values that he has for individual items Examples are equity trading, electricity markets, pollution right auctions and auctions for airport landing slots. To take this into account, combinatorial auctions allow the bidders to submit bids on combinations of items. Integer Programming Models 4
5 Combinatorial Auctions Problem data: let M = {1, 2,..., m} be the set of items that the auctioneer has to sell a bid is a pair B j = (S j, p j ) where S j M is a nonempty set of items and p j is the price offer for this set Suppose that the auctioneer has received n bids B1, B2,..., B n How should the auctioneer determine the winners in order to maximize his revenue? Integer Programming Models 5
6 Combinatorial Auctions The auctioneer maximizes his revenue by solving the integer program: x j = { 1 if bid Bj wins 0 otherwise max n p j x j (1) j=1 j:i S j x j 1 i = 1,..., m (2) x j {0, 1} j = 1,..., n (3) The constraints impose that each item i is sold at most once. Integer Programming Models 6
7 Combinatorial Auctions For example, if there are four items for sale and the following bids have been received: B 1 = ({1}, 6) B 2 = ({2}, 3) B 3 = ({3, 4}, 12) B 4 = ({1, 3}, 12) B 5 = ({2, 4}, 8) B 6 = ({1, 3, 4}, 16) The winners can be determined by the following integer program: max 6x 1 + 3x x x 4 + 8x x 6 (4) x 1 + x 4 + x 6 1 (5) x 2 + x 5 1 (6) x 3 + x 4 + x 6 1 (7) x 3 + x 5 + x 6 1 (8) x j {0, 1} j = 1,..., 6 (9) Integer Programming Models 7
8 The Lockbox Problem Consider a national firm that receives checks from all over the United States. Due to the vagaries of the U.S. Postal Service, as well as the banking system, there is a variable delay from when the check is postmarked and when the check clears For instance, a check mailed in Pittsburgh sent to a Pittsburgh address might clear in just 2 days. A similar check sent to Los Angeles might take 4 days to clear. In order to speed up this clearing, firms open offices (called lockboxes) in different cities to handle the checks. Which lockboxes should we open? Integer Programming Models 8
9 The Lockbox Problem We receive payments from 4 regions (West, Mid-west, East, and South) The average daily value from each region is as follows: 300,000 from the West 120,000 from the Midwest 360,000 from the East 180,000 from the South We are considering opening lock- boxes in (and/or): L.A. Cincinnati Boston Houston Operating a lockbox costs 90,000 per year. Integer Programming Models 9
10 The Lockbox Problem The average days from mailing to clearing is given in the table: From To L.A. Cincinnati Boston Houston West Midwest East South Table: Average days from mailing to clearing First we must calculate the losses due to lost interest for each possible assignment. For example, if the West sends to Boston, then on average there will be 1,800,000 (= 6 300, 000) in process on any given day. Assuming an investment rate of 10%, this corresponds to a yearly loss of 180,000. Integer Programming Models 10
11 The Lockbox Problem We can calculate the losses for the other possibilities in a similar fashion to get the table: From To L.A. Cincinnati Boston Houston West Midwest East South Table: losses Integer Programming Models 11
12 The Lockbox Problem n = number of lockboxes (j = 1,..., n) m = number of regions (i = 1,..., m) α ij = annual cost of assigning region i to the lockbox j β j = annual cost of lockbox j { 1 if lockbox j is opened y j = 0 otherwise { 1 if region i sends to lockbox j x ij = 0 otherwise Integer Programming Models 12
13 The Lockbox Problem n m n min α ij x ij + β j y j j=1 i=1 j=1 n x ij = 1 j=1 m x j my j i=1 x ij {0, 1} y j {0, 1} i = 1,..., m j = 1,..., n i = 1,..., m, j = 1,..., n j = 1,..., n Integer Programming Models 13
14 The Lockbox Problem min 60x x x x x x x x x x x x x x x x y y y y 4 x 11 + x 12 + x 13 + x 14 = 1 x 21 + x 22 + x 23 + x 24 = 1 x 31 + x 32 + x 33 + x 34 = 1 x 41 + x 42 + x 43 + x 44 = 1 x 11 + x 21 + x 31 + x 41 4y 1 0 x 12 + x 22 + x 32 + x 42 4y 2 0 x 13 + x 23 + x 33 + x 43 4y 3 0 x 14 + x 24 + x 34 + x 44 4y 4 0 x ij {0, 1} i = 1,..., 4, j = 1,..., 4 y j {0, 1} j = 1,..., 4 Integer Programming Models 14
15 The Lockbox Problem The above is a correct 01 programming formulation of the lockbox problem. There are other formulations, however. For instance, consider the sixteen constraints of the form: x ij y j i = 1,..., m, j = 1,..., n (10) Different formulations can have very different properties with respect to their associated linear program. One very active research area is to take common problems and find good reformulations. Integer Programming Models 15
16 Constructing an Index Fund Different possibilities of portfolio management: active portfolio management tries to achieve superior performance by using forecasting techniques passive portfolio management avoids any forecasting techniques and rather relies on diversification to achieve a desired performance There are 2 types of passive management strategies: buy and hold: assets are selected on the basis of some fundamental criteria and there is no active selling or buying of these stocks afterwards indexing: the goal is to choose a portfolio that mirrors the movements of a broad market population or a market index. Such a portfolio is called an index fund. Integer Programming Models 16
17 Index fund Definition Given a target population of n stocks, one selects q stocks and their weights in the index fund, to represent the target population as closely as possible. In the last twenty years, an increasing number of investors, both large and small, have established index funds. The rising popularity of index funds can be justified both theoretically and empirically Integer Programming Models 17
18 Constructing an Index Fund Market Efficiency: If the market is efficient, no superior risk-adjusted returns can be achieved by stock picking strategies since the prices reflect all the information available in the marketplace. An index fund captures the efficiency of the market via diversification. Empirical Performance: Considerable empirical literature provides strong evidence that, on average, money managers have consistently underperformed the major indexes (luck is an explanation for good performance). Transaction Cost: Actively managed funds incur transaction costs, which reduce the overall performance of these funds. In addition, active management implies significant research costs. Integer Programming Models 18
19 Index fund Additional features Additional features: Here we take the point of view of a fund manager who wants to construct an index fund. Strategies for forming index funds involve choosing a broad market index as a proxy for an entire market, e.g. the Standard and Poor list of 500 stocks (S&P 500) A pure indexing approach consists in purchasing all the issues in the index, with the same exact weights as in the index. In most instances, this approach is impractical (many small positions) and expensive (rebalancing costs may be incurred frequently) Integer Programming Models 19
20 Index fund Additional features We propose a large-scale deterministic model for aggregating a broad market index of stocks into a smaller more manageable index fund. This approach will produce a portfolio that closely replicates the underlying market population. We present a model that: clusters the assets into groups of similar assets selects one representative asset from each group to be included in the index fund portfolio The model is based on the following data: ρ ij = similarity between stock i and stock j (11) Integer Programming Models 20
21 Index fund Typical features ρ ii = 1 ρ ij 1 for i j ρ ij is larger for more similar stocks An example is the correlation between the returns of stocks i and j Decision variables: { 1 if stock j is in the index fund y j = 0 otherwise { 1 if j is the most similar stock to stock i in the index fund x ij = 0 otherwise Integer Programming Models 21
22 Index fund (IF ) n n max ρ ij x ij (12) i=1 j=1 n y j = q (13) j=1 n x ij = 1 i = 1,..., n (14) j=1 x ij y j i = 1,..., n, j = 1,..., n (15) x ij {0, 1} i = 1,..., n, j = 1,..., n (16) y j {0, 1} j = 1,..., n (17) Integer Programming Models 22
23 Index fund Once the model has been solved and a set of q stocks has been selected for the index fund, a weight w j is calculated for each j in the fund: w j = n V i x ij (18) i=1 where V i is the market value of stock i. So w j is the total market value of the stocks represented by stock j in the fund The fraction of the index fund to be invested in stock j is proportional to the stocks weight w j w j q f =1 w f Note that, instead of the objective function used, one could have used an objective function that takes the weights w j directly into account n n i=1 j=1 V iρ ij x ij Integer Programming Models 23
24 Index fund Solution strategies Branch-and-bound is a natural candidate for solving the model, however that the formulation can be very large. Indeed, for the S&P 500, there are 250,000 variables x ij and 250,000 constraints x ij y j The linear programming relaxation needed to get upper bounds in the branch-and-bound algorithm is difficult to solve Instead of the linear programming relaxation, one can use the Lagrangian relaxation Integer Programming Models 24
25 Index fund Lagrangian Relaxation max L(u) = n n n n ρ ij x ij + u i (1 x ij ) i=1 j=1 i=1 j=1 n y j = q j=1 x ij y j x ij {0, 1} y j {0, 1} i = 1,..., n, j = 1,..., n i = 1,..., n, j = 1,..., n j = 1,..., n where u = (u1,..., u n ) is the vector of the Lagrangian Multipliers Integer Programming Models 25
26 Index fund Property 1: L(u) Z, where Z is the maximum for the model Proof: Let x be the optimal solution of the problem IF, then x is also a feasible solution of L(u) for each u 0. the quantity (Ax b) is 0, then: c T x + u(ax b) L(u) proving that L(u) Z. c T x L(u) The objective function L(u) may be equivalently stated as: max L(u) = n n n (ρ ij u i )x ij + u i (19) i=1 j=1 i=1 Integer Programming Models 26
27 Index fund (ρ ij u i ) + = { (ρij u i ) if ρ ij - u i 0 0 otherwise C j = n (ρ ij u i ) + (20) i=1 n n max L(u) = C j y j + u i (21) j=1 i=1 n y j = q (22) j=1 y j {0, 1} j = 1,..., n (23) Integer Programming Models 27
28 Index fund Property 3: In an optimal solution of the Lagrangian relax, y j is equal to 1 for the q largest values of C j, and the remaining y j are equal to 0. Interestingly, the set of q stocks corresponding to the q largest values of C j can also be used as a heuristic solution for the model Specifically, construct an index fund containing these q stocks and assign each stock i = 1,..., n to the most similar stock in this fund This solution is feasible to the model, although not necessarily optimal. So for any vector u, we can compute quickly both a lower bound and an upper bound on the optimum value of the model How does one minimize L(u)? Integer Programming Models 28
29 Index fund Since L(u) is non-differentiable and convex, one can use the subgradient method At each iteration, a revised set of Lagrange multipliers u and an accompanying lower bound and upper bound to the model are computed The algorithm terminates when these two bounds match or when a maximum number of iterations is reached It can be proved that min L(u) is equal to the value of the linear programming relaxation of the model In general, this is not true and therefore it is not possible to match the upper and lower bounds If one wants to solve the integer program to optimality, one can use a branch-and-bound algorithm, using the upper bound min L(u) for pruning the nodes. Integer Programming Models 29
30 Integer Programming Models 30
Financial Optimization ISE 347/447. Lecture 15. Dr. Ted Ralphs
Financial Optimization ISE 347/447 Lecture 15 Dr. Ted Ralphs ISE 347/447 Lecture 15 1 Reading for This Lecture C&T Chapter 12 ISE 347/447 Lecture 15 2 Stock Market Indices A stock market index is a statistic
More informationLecture 10: The knapsack problem
Optimization Methods in Finance (EPFL, Fall 2010) Lecture 10: The knapsack problem 24.11.2010 Lecturer: Prof. Friedrich Eisenbrand Scribe: Anu Harjula The knapsack problem The Knapsack problem is a problem
More informationChapter 9 Integer Programming Part 1. Prof. Dr. Arslan M. ÖRNEK
Chapter 9 Integer Programming Part 1 Prof. Dr. Arslan M. ÖRNEK Integer Programming An integer programming problem (IP) is an LP in which some or all of the variables are required to be non-negative integers.
More informationInteger Programming. Review Paper (Fall 2001) Muthiah Prabhakar Ponnambalam (University of Texas Austin)
Integer Programming Review Paper (Fall 2001) Muthiah Prabhakar Ponnambalam (University of Texas Austin) Portfolio Construction Through Mixed Integer Programming at Grantham, Mayo, Van Otterloo and Company
More informationBounding Optimal Expected Revenues for Assortment Optimization under Mixtures of Multinomial Logits
Bounding Optimal Expected Revenues for Assortment Optimization under Mixtures of Multinomial Logits Jacob Feldman School of Operations Research and Information Engineering, Cornell University, Ithaca,
More informationAdvanced Operations Research Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology, Madras
Advanced Operations Research Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology, Madras Lecture 21 Successive Shortest Path Problem In this lecture, we continue our discussion
More informationDM559/DM545 Linear and integer programming
Department of Mathematics and Computer Science University of Southern Denmark, Odense May 22, 2018 Marco Chiarandini DM559/DM55 Linear and integer programming Sheet, Spring 2018 [pdf format] Contains Solutions!
More informationEssays on Some Combinatorial Optimization Problems with Interval Data
Essays on Some Combinatorial Optimization Problems with Interval Data a thesis submitted to the department of industrial engineering and the institute of engineering and sciences of bilkent university
More informationOptimization Methods in Management Science
Problem Set Rules: Optimization Methods in Management Science MIT 15.053, Spring 2013 Problem Set 6, Due: Thursday April 11th, 2013 1. Each student should hand in an individual problem set. 2. Discussing
More informationCS 573: Algorithmic Game Theory Lecture date: 22 February Combinatorial Auctions 1. 2 The Vickrey-Clarke-Groves (VCG) Mechanism 3
CS 573: Algorithmic Game Theory Lecture date: 22 February 2008 Instructor: Chandra Chekuri Scribe: Daniel Rebolledo Contents 1 Combinatorial Auctions 1 2 The Vickrey-Clarke-Groves (VCG) Mechanism 3 3 Examples
More informationTutorial 4 - Pigouvian Taxes and Pollution Permits II. Corrections
Johannes Emmerling Natural resources and environmental economics, TSE Tutorial 4 - Pigouvian Taxes and Pollution Permits II Corrections Q 1: Write the environmental agency problem as a constrained minimization
More information1 Shapley-Shubik Model
1 Shapley-Shubik Model There is a set of buyers B and a set of sellers S each selling one unit of a good (could be divisible or not). Let v ij 0 be the monetary value that buyer j B assigns to seller i
More informationCSCI 1951-G Optimization Methods in Finance Part 00: Course Logistics Introduction to Finance Optimization Problems
CSCI 1951-G Optimization Methods in Finance Part 00: Course Logistics Introduction to Finance Optimization Problems January 26, 2018 1 / 24 Basic information All information is available in the syllabus
More informationLecture 5: Iterative Combinatorial Auctions
COMS 6998-3: Algorithmic Game Theory October 6, 2008 Lecture 5: Iterative Combinatorial Auctions Lecturer: Sébastien Lahaie Scribe: Sébastien Lahaie In this lecture we examine a procedure that generalizes
More informationCS364B: Frontiers in Mechanism Design Lecture #18: Multi-Parameter Revenue-Maximization
CS364B: Frontiers in Mechanism Design Lecture #18: Multi-Parameter Revenue-Maximization Tim Roughgarden March 5, 2014 1 Review of Single-Parameter Revenue Maximization With this lecture we commence the
More information6.896 Topics in Algorithmic Game Theory February 10, Lecture 3
6.896 Topics in Algorithmic Game Theory February 0, 200 Lecture 3 Lecturer: Constantinos Daskalakis Scribe: Pablo Azar, Anthony Kim In the previous lecture we saw that there always exists a Nash equilibrium
More informationGraphs Details Math Examples Using data Tax example. Decision. Intermediate Micro. Lecture 5. Chapter 5 of Varian
Decision Intermediate Micro Lecture 5 Chapter 5 of Varian Decision-making Now have tools to model decision-making Set of options At-least-as-good sets Mathematical tools to calculate exact answer Problem
More informationIssues. Senate (Total = 100) Senate Group 1 Y Y N N Y 32 Senate Group 2 Y Y D N D 16 Senate Group 3 N N Y Y Y 30 Senate Group 4 D Y N D Y 22
1. Every year, the United States Congress must approve a budget for the country. In order to be approved, the budget must get a majority of the votes in the Senate, a majority of votes in the House, and
More informationMS&E 246: Lecture 2 The basics. Ramesh Johari January 16, 2007
MS&E 246: Lecture 2 The basics Ramesh Johari January 16, 2007 Course overview (Mainly) noncooperative game theory. Noncooperative: Focus on individual players incentives (note these might lead to cooperation!)
More informationA Robust Option Pricing Problem
IMA 2003 Workshop, March 12-19, 2003 A Robust Option Pricing Problem Laurent El Ghaoui Department of EECS, UC Berkeley 3 Robust optimization standard form: min x sup u U f 0 (x, u) : u U, f i (x, u) 0,
More informationYao s Minimax Principle
Complexity of algorithms The complexity of an algorithm is usually measured with respect to the size of the input, where size may for example refer to the length of a binary word describing the input,
More informationMS-E2114 Investment Science Lecture 5: Mean-variance portfolio theory
MS-E2114 Investment Science Lecture 5: Mean-variance portfolio theory A. Salo, T. Seeve Systems Analysis Laboratory Department of System Analysis and Mathematics Aalto University, School of Science Overview
More informationAlgorithmic Game Theory (a primer) Depth Qualifying Exam for Ashish Rastogi (Ph.D. candidate)
Algorithmic Game Theory (a primer) Depth Qualifying Exam for Ashish Rastogi (Ph.D. candidate) 1 Game Theory Theory of strategic behavior among rational players. Typical game has several players. Each player
More informationScenario reduction and scenario tree construction for power management problems
Scenario reduction and scenario tree construction for power management problems N. Gröwe-Kuska, H. Heitsch and W. Römisch Humboldt-University Berlin Institute of Mathematics Page 1 of 20 IEEE Bologna POWER
More informationMechanism Design and Auctions
Mechanism Design and Auctions Game Theory Algorithmic Game Theory 1 TOC Mechanism Design Basics Myerson s Lemma Revenue-Maximizing Auctions Near-Optimal Auctions Multi-Parameter Mechanism Design and the
More informationChapter 3. Dynamic discrete games and auctions: an introduction
Chapter 3. Dynamic discrete games and auctions: an introduction Joan Llull Structural Micro. IDEA PhD Program I. Dynamic Discrete Games with Imperfect Information A. Motivating example: firm entry and
More informationFinal Examination December 14, Economics 5010 AF3.0 : Applied Microeconomics. time=2.5 hours
YORK UNIVERSITY Faculty of Graduate Studies Final Examination December 14, 2010 Economics 5010 AF3.0 : Applied Microeconomics S. Bucovetsky time=2.5 hours Do any 6 of the following 10 questions. All count
More informationThe Irrevocable Multi-Armed Bandit Problem
The Irrevocable Multi-Armed Bandit Problem Ritesh Madan Qualcomm-Flarion Technologies May 27, 2009 Joint work with Vivek Farias (MIT) 2 Multi-Armed Bandit Problem n arms, where each arm i is a Markov Decision
More informationChapter 7: Portfolio Theory
Chapter 7: Portfolio Theory 1. Introduction 2. Portfolio Basics 3. The Feasible Set 4. Portfolio Selection Rules 5. The Efficient Frontier 6. Indifference Curves 7. The Two-Asset Portfolio 8. Unrestriceted
More informationMartingale Pricing Theory in Discrete-Time and Discrete-Space Models
IEOR E4707: Foundations of Financial Engineering c 206 by Martin Haugh Martingale Pricing Theory in Discrete-Time and Discrete-Space Models These notes develop the theory of martingale pricing in a discrete-time,
More informationAn Enhanced Combinatorial Clock Auction *
An Enhanced Combinatorial ClockAuction * Lawrence M. Ausubel, University of Maryland Oleg V. Baranov, University of Colorado 26 February 2013 *All rights reserved. The findings and conclusions are solely
More informationRobust Dual Dynamic Programming
1 / 18 Robust Dual Dynamic Programming Angelos Georghiou, Angelos Tsoukalas, Wolfram Wiesemann American University of Beirut Olayan School of Business 31 May 217 2 / 18 Inspired by SDDP Stochastic optimization
More informationBabu Banarasi Das National Institute of Technology and Management
Babu Banarasi Das National Institute of Technology and Management Department of Computer Applications Question Bank Masters of Computer Applications (MCA) NEW Syllabus (Affiliated to U. P. Technical University,
More informationMath 167: Mathematical Game Theory Instructor: Alpár R. Mészáros
Math 167: Mathematical Game Theory Instructor: Alpár R. Mészáros Midterm #1, February 3, 2017 Name (use a pen): Student ID (use a pen): Signature (use a pen): Rules: Duration of the exam: 50 minutes. By
More informationCS364A: Algorithmic Game Theory Lecture #3: Myerson s Lemma
CS364A: Algorithmic Game Theory Lecture #3: Myerson s Lemma Tim Roughgarden September 3, 23 The Story So Far Last time, we introduced the Vickrey auction and proved that it enjoys three desirable and different
More informationAn Approximation Algorithm for Capacity Allocation over a Single Flight Leg with Fare-Locking
An Approximation Algorithm for Capacity Allocation over a Single Flight Leg with Fare-Locking Mika Sumida School of Operations Research and Information Engineering, Cornell University, Ithaca, New York
More informationLecture 2: Fundamentals of meanvariance
Lecture 2: Fundamentals of meanvariance analysis Prof. Massimo Guidolin Portfolio Management Second Term 2018 Outline and objectives Mean-variance and efficient frontiers: logical meaning o Guidolin-Pedio,
More informationLog-Robust Portfolio Management
Log-Robust Portfolio Management Dr. Aurélie Thiele Lehigh University Joint work with Elcin Cetinkaya and Ban Kawas Research partially supported by the National Science Foundation Grant CMMI-0757983 Dr.
More informationThe Assignment Problem
The Assignment Problem E.A Dinic, M.A Kronrod Moscow State University Soviet Math.Dokl. 1969 January 30, 2012 1 Introduction Motivation Problem Definition 2 Motivation Problem Definition Outline 1 Introduction
More informationJournal of Computational and Applied Mathematics. The mean-absolute deviation portfolio selection problem with interval-valued returns
Journal of Computational and Applied Mathematics 235 (2011) 4149 4157 Contents lists available at ScienceDirect Journal of Computational and Applied Mathematics journal homepage: www.elsevier.com/locate/cam
More informationQ1. [?? pts] Search Traces
CS 188 Spring 2010 Introduction to Artificial Intelligence Midterm Exam Solutions Q1. [?? pts] Search Traces Each of the trees (G1 through G5) was generated by searching the graph (below, left) with a
More informationPrice of Anarchy Smoothness Price of Stability. Price of Anarchy. Algorithmic Game Theory
Smoothness Price of Stability Algorithmic Game Theory Smoothness Price of Stability Recall Recall for Nash equilibria: Strategic game Γ, social cost cost(s) for every state s of Γ Consider Σ PNE as the
More informationHandout 8: Introduction to Stochastic Dynamic Programming. 2 Examples of Stochastic Dynamic Programming Problems
SEEM 3470: Dynamic Optimization and Applications 2013 14 Second Term Handout 8: Introduction to Stochastic Dynamic Programming Instructor: Shiqian Ma March 10, 2014 Suggested Reading: Chapter 1 of Bertsekas,
More informationECON 459 Game Theory. Lecture Notes Auctions. Luca Anderlini Spring 2017
ECON 459 Game Theory Lecture Notes Auctions Luca Anderlini Spring 2017 These notes have been used and commented on before. If you can still spot any errors or have any suggestions for improvement, please
More informationAssortment Planning under the Multinomial Logit Model with Totally Unimodular Constraint Structures
Assortment Planning under the Multinomial Logit Model with Totally Unimodular Constraint Structures James Davis School of Operations Research and Information Engineering, Cornell University, Ithaca, New
More informationInteger Solution to a Graph-based Linear Programming Problem
Integer Solution to a Graph-based Linear Programming Problem E. Bozorgzadeh S. Ghiasi A. Takahashi M. Sarrafzadeh Computer Science Department University of California, Los Angeles (UCLA) Los Angeles, CA
More informationAuctions. Michal Jakob Agent Technology Center, Dept. of Computer Science and Engineering, FEE, Czech Technical University
Auctions Michal Jakob Agent Technology Center, Dept. of Computer Science and Engineering, FEE, Czech Technical University AE4M36MAS Autumn 2015 - Lecture 12 Where are We? Agent architectures (inc. BDI
More informationIEOR E4004: Introduction to OR: Deterministic Models
IEOR E4004: Introduction to OR: Deterministic Models 1 Dynamic Programming Following is a summary of the problems we discussed in class. (We do not include the discussion on the container problem or the
More informationMAT 4250: Lecture 1 Eric Chung
1 MAT 4250: Lecture 1 Eric Chung 2Chapter 1: Impartial Combinatorial Games 3 Combinatorial games Combinatorial games are two-person games with perfect information and no chance moves, and with a win-or-lose
More informationChapter 8. Markowitz Portfolio Theory. 8.1 Expected Returns and Covariance
Chapter 8 Markowitz Portfolio Theory 8.1 Expected Returns and Covariance The main question in portfolio theory is the following: Given an initial capital V (0), and opportunities (buy or sell) in N securities
More informationMultistage Stochastic Demand-side Management for Price-Making Major Consumers of Electricity in a Co-optimized Energy and Reserve Market
Multistage Stochastic Demand-side Management for Price-Making Major Consumers of Electricity in a Co-optimized Energy and Reserve Market Mahbubeh Habibian Anthony Downward Golbon Zakeri Abstract In this
More informationOnline Appendix: Extensions
B Online Appendix: Extensions In this online appendix we demonstrate that many important variations of the exact cost-basis LUL framework remain tractable. In particular, dual problem instances corresponding
More informationDecomposition Methods
Decomposition Methods separable problems, complicating variables primal decomposition dual decomposition complicating constraints general decomposition structures Prof. S. Boyd, EE364b, Stanford University
More informationTechniques for Calculating the Efficient Frontier
Techniques for Calculating the Efficient Frontier Weerachart Kilenthong RIPED, UTCC c Kilenthong 2017 Tee (Riped) Introduction 1 / 43 Two Fund Theorem The Two-Fund Theorem states that we can reach any
More informationStrategy -1- Strategy
Strategy -- Strategy A Duopoly, Cournot equilibrium 2 B Mixed strategies: Rock, Scissors, Paper, Nash equilibrium 5 C Games with private information 8 D Additional exercises 24 25 pages Strategy -2- A
More informationORF 307: Lecture 12. Linear Programming: Chapter 11: Game Theory
ORF 307: Lecture 12 Linear Programming: Chapter 11: Game Theory Robert J. Vanderbei April 3, 2018 Slides last edited on April 3, 2018 http://www.princeton.edu/ rvdb Game Theory John Nash = A Beautiful
More informationA Branch-and-Price method for the Multiple-depot Vehicle and Crew Scheduling Problem
A Branch-and-Price method for the Multiple-depot Vehicle and Crew Scheduling Problem SCIP Workshop 2018, Aachen Markó Horváth Tamás Kis Institute for Computer Science and Control Hungarian Academy of Sciences
More informationCS360 Homework 14 Solution
CS360 Homework 14 Solution Markov Decision Processes 1) Invent a simple Markov decision process (MDP) with the following properties: a) it has a goal state, b) its immediate action costs are all positive,
More informationSingle Price Mechanisms for Revenue Maximization in Unlimited Supply Combinatorial Auctions
Single Price Mechanisms for Revenue Maximization in Unlimited Supply Combinatorial Auctions Maria-Florina Balcan Avrim Blum Yishay Mansour February 2007 CMU-CS-07-111 School of Computer Science Carnegie
More informationOptimization 101. Dan dibartolomeo Webinar (from Boston) October 22, 2013
Optimization 101 Dan dibartolomeo Webinar (from Boston) October 22, 2013 Outline of Today s Presentation The Mean-Variance Objective Function Optimization Methods, Strengths and Weaknesses Estimation Error
More informationMacroeconomics and finance
Macroeconomics and finance 1 1. Temporary equilibrium and the price level [Lectures 11 and 12] 2. Overlapping generations and learning [Lectures 13 and 14] 2.1 The overlapping generations model 2.2 Expectations
More informationThe Duo-Item Bisection Auction
Comput Econ DOI 10.1007/s10614-013-9380-0 Albin Erlanson Accepted: 2 May 2013 Springer Science+Business Media New York 2013 Abstract This paper proposes an iterative sealed-bid auction for selling multiple
More informationProgressive Hedging for Multi-stage Stochastic Optimization Problems
Progressive Hedging for Multi-stage Stochastic Optimization Problems David L. Woodruff Jean-Paul Watson Graduate School of Management University of California, Davis Davis, CA 95616, USA dlwoodruff@ucdavis.edu
More informationAuctions. Michal Jakob Agent Technology Center, Dept. of Computer Science and Engineering, FEE, Czech Technical University
Auctions Michal Jakob Agent Technology Center, Dept. of Computer Science and Engineering, FEE, Czech Technical University AE4M36MAS Autumn 2014 - Lecture 12 Where are We? Agent architectures (inc. BDI
More informationRevenue Management Under the Markov Chain Choice Model
Revenue Management Under the Markov Chain Choice Model Jacob B. Feldman School of Operations Research and Information Engineering, Cornell University, Ithaca, New York 14853, USA jbf232@cornell.edu Huseyin
More informationStrategies and Nash Equilibrium. A Whirlwind Tour of Game Theory
Strategies and Nash Equilibrium A Whirlwind Tour of Game Theory (Mostly from Fudenberg & Tirole) Players choose actions, receive rewards based on their own actions and those of the other players. Example,
More informationThe Lagrangian method is one way to solve constrained maximization problems.
LECTURE 4: CONSTRAINED OPTIMIZATION QUESTIONS AND PROBLEMS True/False Questions The Lagrangian method is one way to solve constrained maximization problems. The substitution method is a way to avoid using
More informationContinuing Education Course #287 Engineering Methods in Microsoft Excel Part 2: Applied Optimization
1 of 6 Continuing Education Course #287 Engineering Methods in Microsoft Excel Part 2: Applied Optimization 1. Which of the following is NOT an element of an optimization formulation? a. Objective function
More informationOverview. ICE: Iterative Combinatorial Exchanges. Combinatorial Auctions. Motivating Domains. Exchange Example 1. Benjamin Lubin
Overview ICE: Iterative Combinatorial Exchanges Benjamin Lubin In Collaboration with David Parkes and Adam Juda Early work Giro Cavallo, Jeff Shneidman, Hassan Sultan, CS286r Spring 2004 Introduction ICE
More informationPORTFOLIO OPTIMIZATION AND EXPECTED SHORTFALL MINIMIZATION FROM HISTORICAL DATA
PORTFOLIO OPTIMIZATION AND EXPECTED SHORTFALL MINIMIZATION FROM HISTORICAL DATA We begin by describing the problem at hand which motivates our results. Suppose that we have n financial instruments at hand,
More informationCOMP331/557. Chapter 6: Optimisation in Finance: Cash-Flow. (Cornuejols & Tütüncü, Chapter 3)
COMP331/557 Chapter 6: Optimisation in Finance: Cash-Flow (Cornuejols & Tütüncü, Chapter 3) 159 Cash-Flow Management Problem A company has the following net cash flow requirements (in 1000 s of ): Month
More informationDeterministic Dynamic Programming
Deterministic Dynamic Programming Dynamic programming is a technique that can be used to solve many optimization problems. In most applications, dynamic programming obtains solutions by working backward
More informationNet lift and return maximization. Victor D. Zurkowski Analytics Consultant Metrics and Analytics CIBC National Collection
Net lift and return maximization Victor D. Zurkowski Analytics Consultant Metrics and Analytics CIBC National Collection Page 2 Page 3 Could I have been wrong all along? Page 4 There has been recent mentions
More informationOutline for today. Stat155 Game Theory Lecture 19: Price of anarchy. Cooperative games. Price of anarchy. Price of anarchy
Outline for today Stat155 Game Theory Lecture 19:.. Peter Bartlett Recall: Linear and affine latencies Classes of latencies Pigou networks Transferable versus nontransferable utility November 1, 2016 1
More informationCOSC 311: ALGORITHMS HW4: NETWORK FLOW
COSC 311: ALGORITHMS HW4: NETWORK FLOW Solutions 1 Warmup 1) Finding max flows and min cuts. Here is a graph (the numbers in boxes represent the amount of flow along an edge, and the unadorned numbers
More informationNew Features of Population Synthesis: PopSyn III of CT-RAMP
New Features of Population Synthesis: PopSyn III of CT-RAMP Peter Vovsha, Jim Hicks, Binny Paul, PB Vladimir Livshits, Kyunghwi Jeon, Petya Maneva, MAG 1 1. MOTIVATION & STATEMENT OF INNOVATIONS 2 Previous
More informationPricing Transmission
1 / 47 Pricing Transmission Quantitative Energy Economics Anthony Papavasiliou 2 / 47 Pricing Transmission 1 Locational Marginal Pricing 2 Congestion Rent and Congestion Cost 3 Competitive Market Model
More informationThe Duration Derby: A Comparison of Duration Based Strategies in Asset Liability Management
The Duration Derby: A Comparison of Duration Based Strategies in Asset Liability Management H. Zheng Department of Mathematics, Imperial College London SW7 2BZ, UK h.zheng@ic.ac.uk L. C. Thomas School
More informationCS364A: Algorithmic Game Theory Lecture #14: Robust Price-of-Anarchy Bounds in Smooth Games
CS364A: Algorithmic Game Theory Lecture #14: Robust Price-of-Anarchy Bounds in Smooth Games Tim Roughgarden November 6, 013 1 Canonical POA Proofs In Lecture 1 we proved that the price of anarchy (POA)
More informationEnergy Systems under Uncertainty: Modeling and Computations
Energy Systems under Uncertainty: Modeling and Computations W. Römisch Humboldt-University Berlin Department of Mathematics www.math.hu-berlin.de/~romisch Systems Analysis 2015, November 11 13, IIASA (Laxenburg,
More informationPortfolio selection with multiple risk measures
Portfolio selection with multiple risk measures Garud Iyengar Columbia University Industrial Engineering and Operations Research Joint work with Carlos Abad Outline Portfolio selection and risk measures
More informationOPTIMIZATION METHODS IN FINANCE
OPTIMIZATION METHODS IN FINANCE GERARD CORNUEJOLS Carnegie Mellon University REHA TUTUNCU Goldman Sachs Asset Management CAMBRIDGE UNIVERSITY PRESS Foreword page xi Introduction 1 1.1 Optimization problems
More informationScenario Generation and Sampling Methods
Scenario Generation and Sampling Methods Güzin Bayraksan Tito Homem-de-Mello SVAN 2016 IMPA May 9th, 2016 Bayraksan (OSU) & Homem-de-Mello (UAI) Scenario Generation and Sampling SVAN IMPA May 9 1 / 30
More informationMarshall and Hicks Understanding the Ordinary and Compensated Demand
Marshall and Hicks Understanding the Ordinary and Compensated Demand K.J. Wainwright March 3, 213 UTILITY MAXIMIZATION AND THE DEMAND FUNCTIONS Consider a consumer with the utility function =, who faces
More informationNotes on Auctions. Theorem 1 In a second price sealed bid auction bidding your valuation is always a weakly dominant strategy.
Notes on Auctions Second Price Sealed Bid Auctions These are the easiest auctions to analyze. Theorem In a second price sealed bid auction bidding your valuation is always a weakly dominant strategy. Proof
More information1 Overview. 2 The Gradient Descent Algorithm. AM 221: Advanced Optimization Spring 2016
AM 22: Advanced Optimization Spring 206 Prof. Yaron Singer Lecture 9 February 24th Overview In the previous lecture we reviewed results from multivariate calculus in preparation for our journey into convex
More informationOptimization Methods in Finance
Optimization Methods in Finance Gerard Cornuejols Reha Tütüncü Carnegie Mellon University, Pittsburgh, PA 15213 USA January 2006 2 Foreword Optimization models play an increasingly important role in financial
More informationPAULI MURTO, ANDREY ZHUKOV
GAME THEORY SOLUTION SET 1 WINTER 018 PAULI MURTO, ANDREY ZHUKOV Introduction For suggested solution to problem 4, last year s suggested solutions by Tsz-Ning Wong were used who I think used suggested
More informationCS224W: Social and Information Network Analysis Jure Leskovec, Stanford University
CS224W: Social and Information Network Analysis Jure Leskovec, Stanford University http://cs224w.stanford.edu 10/27/16 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu
More informationTHE TRAVELING SALESMAN PROBLEM FOR MOVING POINTS ON A LINE
THE TRAVELING SALESMAN PROBLEM FOR MOVING POINTS ON A LINE GÜNTER ROTE Abstract. A salesperson wants to visit each of n objects that move on a line at given constant speeds in the shortest possible time,
More informationMultistage risk-averse asset allocation with transaction costs
Multistage risk-averse asset allocation with transaction costs 1 Introduction Václav Kozmík 1 Abstract. This paper deals with asset allocation problems formulated as multistage stochastic programming models.
More informationGames of Incomplete Information ( 資訊不全賽局 ) Games of Incomplete Information
1 Games of Incomplete Information ( 資訊不全賽局 ) Wang 2012/12/13 (Lecture 9, Micro Theory I) Simultaneous Move Games An Example One or more players know preferences only probabilistically (cf. Harsanyi, 1976-77)
More informationThe Clock-Proxy Auction: A Practical Combinatorial Auction Design
The Clock-Proxy Auction: A Practical Combinatorial Auction Design Lawrence M. Ausubel, Peter Cramton, Paul Milgrom University of Maryland and Stanford University Introduction Many related (divisible) goods
More informationThreshold Accepting for Credit Risk Assessment and Validation
Threshold Accepting for Credit Risk Assessment and Validation M. Lyra 1 A. Onwunta P. Winker COMPSTAT 2010 August 24, 2010 1 Financial support from the EU Commission through COMISEF is gratefully acknowledged
More informationSo we turn now to many-to-one matching with money, which is generally seen as a model of firms hiring workers
Econ 805 Advanced Micro Theory I Dan Quint Fall 2009 Lecture 20 November 13 2008 So far, we ve considered matching markets in settings where there is no money you can t necessarily pay someone to marry
More informationAn Exact Solution Approach for Portfolio Optimization Problems under Stochastic and Integer Constraints
An Exact Solution Approach for Portfolio Optimization Problems under Stochastic and Integer Constraints P. Bonami, M.A. Lejeune Abstract In this paper, we study extensions of the classical Markowitz mean-variance
More informationChoice. A. Optimal choice 1. move along the budget line until preferred set doesn t cross the budget set. Figure 5.1.
Choice 34 Choice A. Optimal choice 1. move along the budget line until preferred set doesn t cross the budget set. Figure 5.1. Optimal choice x* 2 x* x 1 1 Figure 5.1 2. note that tangency occurs at optimal
More informationStochastic Optimization Methods in Scheduling. Rolf H. Möhring Technische Universität Berlin Combinatorial Optimization and Graph Algorithms
Stochastic Optimization Methods in Scheduling Rolf H. Möhring Technische Universität Berlin Combinatorial Optimization and Graph Algorithms More expensive and longer... Eurotunnel Unexpected loss of 400,000,000
More informationUtility Indifference Pricing and Dynamic Programming Algorithm
Chapter 8 Utility Indifference ricing and Dynamic rogramming Algorithm In the Black-Scholes framework, we can perfectly replicate an option s payoff. However, it may not be true beyond the Black-Scholes
More informationOptimal Integer Delay Budget Assignment on Directed Acyclic Graphs
Optimal Integer Delay Budget Assignment on Directed Acyclic Graphs E. Bozorgzadeh S. Ghiasi A. Takahashi M. Sarrafzadeh Computer Science Department University of California, Los Angeles (UCLA) Los Angeles,
More information