Auctions for Digital Goods Review
|
|
- Sara Stone
- 7 years ago
- Views:
Transcription
1 Auctions for Digital Goods Review Rattapon Limprasittiporn Computer Science Department University of California Davis Abstract The emergence of the Internet brings a large change in the economic trend; one such change is the negligible cost of duplicating digital goods An auction is one of the dynamic selling mechanisms that yields a good result dealing with the case when the consumer utility values are dramatically vary such that it is infeasible to do the market research to determine the best fixed price In such scenario, an auction which performs probably well under any market conditions is necessary We review the state-of-the-art auction model from five papers related to auctions for digital goods and categorized them in to sections Then we illustrate the model evolution and compare these models in order to better understand the auction technique proposed in today s market 1 Introduction One certain kind of the economic efficiency is to transfer resources to those who can make the most use of these resources Auctions attempt to achieve this by giving the bidders the opportunity to declare how much they are willing to pay Of course, from the seller s perspective, what matters is not the relative usefulness of the item for sale, but rather the revenue generated by the sale In general, it may not be possible to simultaneously maximize efficiency and revenue, but in many natural situations, the seller can extract reasonable revenue while maximizing efficiency However, a combination of recent economic and computational trends, such the negligible cost of duplicating digital goods and, most importantly, the emergence of the Internet as one of the most important arenas for resource sharing between parties with diverse and selfish interests, has created a number of new and interesting dynamic pricing problems including the profit optimization for the seller in an auctions We consider the design of profit-maximizing auctions for digital, or information, goods such as electronic books, software, and digital copies of music or movies 2 Preliminaries and Notion We consider single-round, sealed-bid auctions for a set of identical items available in unlimited supply A single-round sealed-bid auction is one where each bidder submits a bid, representing the maximum amount they are willing to pay for an item We denote by b the vector of all submitted bids The i th component of b is b i, the bid submitted by bidder i We denote by n the number of bidders Given the vector b, the auctioneer computes a result allocation If bidder i receives the item under the price p i < b i, we say that bidder i wins; otherwise, bidder i loses, or is rejected The price p i is what bidder i pays the auctioneer The profit of the auction is the sum of the revenue that the auctioneer obtains from the bidders Each bidder has a private utility value, representing the true maximum price they are willing to pay for an item We denote by u i the utility value of bidder i Each bidder bids so as to maximize their profit, u i x i p i Bidders bid with full knowledge of the auctioneer's strategy and we assume that bidders do not collude We say that an auction is truthful if, for each bidder i and any choice of bid values for all other bidders, the profit of bidder i is maximized by bidding their utility value, ie, by setting b i = u i Truthful auctions encourage rational bidders to bid at utility value 21 Optimal Single Price Omniscient Auctions A key question is how to evaluate the performance of auctions with respect to the goal of profit optimization An objective would be to design auctions that, on all bid vectors b, achieve a profit close to that of the optimal single-price omniscient auction F The optimal single price omniscient auction F is defined as follows: Let b be a bid vector, and let v i be the i th largest bid in b Auction F on input b determines the value k 2 such that (k)(v k ) is maximized All bidders with b i v k win at price v k ; all remaining bidders lose The profit of F on input b is thus
2 F(b) = max iv 2 i n The value of k must be greater than or equal to 2 If we allow k to be 1, then it is impossible to be competitive with F; consider the case when the highest bid h is much greater than all other bids combined, it is impossible to truthfully extract a constant fraction of h in profit from the high bidder 22 Competitiveness We now formalize the notion of a competitive auction Let A be a truthful auction We say that A is β- competitive against F if for all bid vectors b, the expected profit of A on b satisfies E[A(b)] i F (b) β To gain insight into competitive auctions, consider the k-item Vickrey auction V k This auction is a singleprice auction that sells k items to the k highest bidders at the price equal to the k+1 st highest bid When an unlimited supply of identical items is available, taking k = f(n) yields a truthful auction for any function f with 1 f(n) < n that depends on n only (and not on the bid values) Unfortunately, the resulting auction V f(n) is not competitive For example, consider an input b that has k bids at some very high value h and n k bids at 1 In this case V f(n) (b) = k, whereas F(b) (h)(k) One can attempt to fix this problem by choosing for each bid vector b the value of k that maximizes V k (b) However, the resulting auction is not truthful Consider three bidders with utilities 10, 30, and 40 If the bidders bid their utilities, then optimal choice of k is 1, in which case only the third bidder wins and pays 30 However, if the second bidder changes their bid to 11, then the optimal value of k is 2, the second and the third bidders win, and pay 10 each Therefore bidding utility is not a dominant strategy for the second bidder These examples show that competitive auctions must depend on the bid values in a way that keeps the auction truthful 23 Bid-Independent Auctions This concept is introduced by [1] and is used widely The basic idea is that when we compute the result of bidder i, we will not take into account of his bid value It is shown in [1] that any deterministic bidindependent auction is truthful and any truthful deterministic auction is bid-independent As a result, an auction is truthful if and only if it is equivalent to a bid-independent auction 23 Randomized Auctions We say the auction is randomized if the procedure by which the auctioneer computes the allocation and prices is randomized Note that if the auction is randomized, the profit of the auction, the output prices, and the allocation are random variables Otherwise, the auction is deterministic It is shown in [1] that no truthful deterministic auction is competitive; hence, the studies on the auction for digital goods concentrate on the randomized auction; otherwise, the constraints are relaxed 3 Competitive Auctions The work in [1] presented two auction models that achieve both the truthfulness and competitiveness: DSOT and SCS 31 Dual-Price Sampling Optimal Threshold The idea underlying the first competitive auction introduced in this paper is to use a random sample to get an estimate of the optimal fixed price and then use this price as a threshold for the bids that were not sampled; each bid below the threshold is rejected, and each bid at or above the threshold wins at the threshold price The bids are partitioned into two sets uniformly at random, compute the optimal fixed price for each set, and then use that threshold for the other set We call the resulting family of auctions the dual-price sampling optimal threshold auctions or DSOT r where r is a parameter used by the auction The paper introduced the notion of opt r (b) to be the optimal fixed price for the bid vector b that sells at least r items, ie, opt r (b) is the value v k such that k r and kv k = max iv r i n The algorithm partitions the set of bids uniformly at random into two sets This consists of putting each bid into one set or the other independently with probability 1/2 The full description is described below 1 Partition bids b uniformly at random into two sets, resulting in bid vectors b and b 2 Let p = opt r (b ) and p = opt r (b ), the optimal fixed price thresholds that sell at least r items for b and b, respectively i
3 3 Use p as a threshold for all bids in b (ie, all bids in b of value below p are rejected; all remaining bids win at price p ) 4 Use p as a threshold for all bids in b When r = 1, it refers to this auction simply as DSOT Intuitively, the parameter r enables the auction to avoid outcomes with a small number of winners By this algorithm, DSOT is a dual-priced auction If it is important to have a single-price auction, the paper suggests one can simply skip the 4 th step and reject all the bids in b, at the cost of half the expected profit Since DSOT computes the result of bidder i without taking into account of his bid value, it can be implemented as a bid-independent auction Thus, DSOT is truthful The paper also proved that there is a constant β such that DSOT is β-competitive This upper bound analysis yields result that DSOT is not better than 4- competitive 32 Sampling Cost-Sharing This paper also presents another competitive truthful auction that is simple, easy to analyze, and achieves a good competitive ratio The goal of this technique is, given bids b and cost C, to find a subset of the bidders to share the cost C More precisely the cost-sharing and mechanism is defined as follows Given bids b, the auctioneer finds the largest k such that the highest k bidders can equally share the cost C, and then charges each C/k CostShare C works as follows It maintains a current set X, initialized to the set of all bids At each iteration, bids of value below C/ X are deleted from X When X stops changing, CostShare C declares X to be the set of winning bids and C/ X to be the sale price for every winner The Sampling Cost-Sharing (SCS) auction is described below 1 Partition bids b uniformly at random into two sets, resulting in bid vectors b and b 2 Compute F = F(b ) and F = F(b ), the optimal fixed price profits for b and b, respectively 3 Compute the auction results by running CostShare F on b and CostShare F on b SCS can be implemented in a bid-independent fashion: For each bid, simulate the cost-sharing method assuming that bid is infinite to determine the output of the bid-independent function Thus SCS is truthful The profit of an auction is min(f, F ) One of the CostShare F on b and CostShare F on b will reject all bids The paper proved that SCS is 4-competitive, and this bound is tight 33 Bounded Supply Up to this point, the paper has studied the unlimited supply case, motivated by the digital goods market In addition, it also considers the case where the number of items available for sale is bounded This case is typical for physical goods markets It denotes the number of items available by k As ate before, the seller wishes to maximize profit and is not required to sell all the items The definition of truthful and competitive auctions, which we stated for the unlimited supply case, also applies to the bounded supply case To reduce the bounded supply case to the unlimited supply case, it just simply ignores (rejects) all but the highest k bidders This reduction trivially extends all results presented in the paper to the bounded supply case 4 Envy-Free Auctions for Digital Goods This paper introduces a new concept of envy-free auction After the auction is run, no bidder would be happier with the outcome of another bidder For unlimited supply auctions, this means that there is a single sale price and goods are allocated to all bidders willing to pay this price The main result in this paper is to show that no constant-competitive truthful auction is envy-free Two relaxations of these requirements are considered, allowing the auction to be untruthful with vanishingly small probability, and allowing the auction to give nonenvy-free outcomes with vanishingly small probability Under both of these relaxations we get competitive auctions This paper introduces the notion of β(m)- competitive where the auctioneer sells at least m items It proves that there is no truthful envy-free auction with β(m)-competitive where n log β ( m) O( m ) loglog n However, the paper also proves that there exists a truthful envy-free auction that is Θ(log n)-competitive
4 Nonetheless, both truthfulness and envy-free outcomes are important As a result, the paper presents two auctions that come close to being competitive, truthful, and envy-free at the same time The first auction is truthful, but is only envy-free with high probability; the second auction is envy-free, but only truthful with high probability 41 Almost Envy-Free In this section, the condition that the auction outcome is always envy-free is relaxed and instead it considers auctions that are truthful but only envy-free with high probability One such auction is the Consensus Revenue Estimate (CORE) auction from [5] We review the mass-market version of the CORE auction and discuss its properties The CORE auction combines two general ideas that have proven to be successful for designing competitive mechanisms The first is that of a profit extractor A profit extractor is a truthful parameterized mechanism that, given a target profit, extracts that profit from the bidders if it is possible For basic auctions the maximum extractable profit is the same as the profit of the optimal omniscient auction F The profit extraction mechanism for basic auctions is the following cost sharing mechanism To determine CostShare R of the given bids b, find the largest k such that the highest k bidders can equally share the cost R Charge each of these bidders R/k If no k exists, no bidders win This mechanism is truthful, envy-free, and yields profit R if R F(b) The second technique for the design of competitive mechanisms is that of using a bid-independent consensus estimate The truthfulness of CostShare R implies that it is implemented by some bid-independent function, cs R That is, the bid-independent auction defined by f(b i ) = cs R (b i ) is exactly CostShare R Consider the auction, A, parameterized by function r( ) that is defined by bid-independent function f(b i ) = cs r( ) ( b i ) i b Note that if r is consensus, ie, r(b i ) = R for all i, then A is identically CostShare R Recall that CostShare R gives profit R in the case that R F The consensus estimate technique is to construct an r( ) that gives an estimate of F and is constant on b i for all i For the case that F(b i ) is a constant fraction of F(b), ie, 1 F ( b) F ( b i ) < F ( b), ρ it is possible to pick an r( ) from a distribution of functions that are good estimates of F such that with high probability that r is a consensus Parameterized by constant c with c > ρ, we choose r( ) as follow For u be uniformly distributed on [0, 1], define r(b) is F(b) rounded down to nearest c i+u for integer i The auction is envy-free if r is consensus The paper proves that this probability is 1 log C ρ 42 Almost Truthful Another approach to deal with the non-existence of truthful competitive auctions that always have envyfree outcomes is to relax the requirement that the auction always be truthful Next we define a probabilistically truthful mechanism Let m be the number of winners in the optimal auction on a given set of bids We will be looking for a mechanism with good probabilistically truthful properties in terms of m Here are some definitions defined by the paper An auction is truthful with probability 1 ε if the probability that any bidder can benefit from an untruthful bid is at most ε An auction is truthful with high probability if ε 0 as m, where m is the number of winners in F The paper modifies CORE a little bit as follow 1 Pick u uniformly at random from [0, 1] 2 Let function r( ) be F( ) rounded to nearest c i+u for integer i 3 Run CostShare r(b) on b The paper also proves that this auction is truthful with probability m 1 logc = 1 Θ(1/ m) m 1 And the expected revenue from this auction is F ( b) 1 (1 ) ln c c 5 Online Auctions This paper in [3] presents the concept of online auction in which the bidders know their result immediately The motivation of this study is that most digital goods, for instance, books, music, or software, are sold continuously, without any point at which sales end Further more, since digital goods can be delivered
5 immediately, buyers would presumably expect a fairly rapid, if not immediate, response to a bid for digital goods Even in the case of a digital broadcast, where the delivery is not immediate, buyers want to know quickly whether or not their offers have been accepted In auctioning a pay-per-view broadcast, for instance, we might want to allow bids to come in mere minutes before the broadcast, or maybe even after the broadcast has begun It would be clearly unacceptable to delay all decisions about who receives the broadcast until such a point As a result, it is important to design auctions which work in as online model The auctions must be truthful which decide whether to satisfy a bid and at what price before the next bid arrives The goal of the model is to achieve the good revenue relative to the optimal single price omniscient auction F The model considers auctions for goods available in unlimited supply Each bidder can win at most one item of the good Let n be the number of bidders In the truthful auction, each bidder has his private utility value v i which equal to his bid By normalizing the bids (divide all bids by the smallest bid), we can assume that each v i is a real number in the interval [1, h] Notice that h is the ratio between the highest and lowest bid values The model of an online auction receives bids from the n bidders one by one in a sequence b = [b 1, b 2,, b n ] The auctioneer, when presented with the bid b i of bidder i, determines whether to sell the good to bidder i and if so, at what price This determination must be made before any further bids are presented The price p i charged to bidder i is never more than b i An online auction A can be viewed as a collection of n pricing functions s 1, s 2,, s n, where s i (b 1,, b i ) is a function of the bids received up to step i The interpretation of these functions is that s i sets a selling price for bidder i If s i b i, bidder i wins the good and pay s i ; otherwise, bidder i does not win the good and pay nothing The total revenue of an auction is the sum of the payments received An online auction is truthful is the pricing function for bidder i depends only on the previous bids, b 1,, b i 1, and in the other word, for any two choices for the i th bid, b i and b i, the expected price s i is the same This definition of bid-independent is more general than one given in the previous section Since no auction does well compare to F when there is a single high bidder, it is natural to restrict the attention to the bid sequence for which F αh, for some α 1 Another notion introduced in this study is the bid buckets The bid sequence of range [1, h] is partitioned into l = log h + 1 sub-intervals, I 0,, I l 1, where I k = [2 k, 2 k+1 ) Given a bid sequence b, we define the k th bucket of b to be the collection of bids that fall in the k th interval The weight of the k th bucket is the sum of the bids in the bucket The paper also proves that there is no deterministic auction that performs well in the online auction The proof is different than that in the offline auction presented in [1] 51 Simple Randomized Auction The first idea is an extremely simple truthful randomized online auction that achieves an optimal competitive ratio of O(log h) relative to the total revenue This auction model assumes that we have some way to know the upper bound of the value of h on the range of bids This auction calculates the value s i of bidder i by picking a random k {0,, log h }, and set s i = 2 k Since this auction does not take b i into account, it is truthful The paper presents a proof that this simple online auction is Θ(log h)-competitive relative to the total revenue and relative to the optimal fixed price revenue 52 Weighted Buckets Auction The paper presents an online auction W d which uses the bid buckets previously defined as well as a parameter d 1, which will be tune later The idea is that for the i th bidder, the bid buckets from b 1,, b i 1 are constructed In this context, b i is defined to be the bid values from b 1,, b i 1 Then one bucket is picked at random, with the probabilities weighted according to the weight of the buckets raise to the power d The chosen bucket is essentially the guess for the next bid, and the selling price is set to be the lower limit of that bucket In the other word, d k ( wk ( b i )) Pr[ si = 2 ] = l 1 d r = 0 ( wr ( b i )) Since W d is bid-independent, this weighted buckets auction is truthful The paper illustrates that, by picking d to be log log h and restricting the bid sequences such that F(b) 4h, the weighted buckets auction W d is
6 O(exp log log h) -competitive relative to the optimal fixed price revenue 53 Online Learning in Online Auction The paper in [4] presents some modification to the technique described in section 52 by applying the online learning technique to yield the better decision The key insight connecting the online auction problem to online learning is that setting a single fixed price can be thought of as following the advice of a single expert who predicts that fixed price for every bidder Performing well relative to the optimal fixed price is then equivalent to performing well relative to the best of these experts The model uses the variant of Littlestone and Warmuth s weighted majority (WM) algorithm [6] From the previous section, we have l buckets The selling price is chosen from the lower bound of one random bucket When applying the online learning technique to this context, the candidate prices is chosen from X = {x 1,, x l } Each of this can be viewed as a candidate fixed price corresponding to a set of experts Let r k (b) be the revenue obtained by setting the fixed price x k for the bid sequence b Given a parameter α (0, 1], define weights w ( i) α k r ( v 1,, v ) / h = (1 + ) k i Clearly, the weights can be easily maintained using a multiplicative update Then, for bidder i, the auction chooses s i X with probability wk ( i 1) Pr[ si = xk ] = l j = 1 w j ( i 1) The paper also proves that, restricting the bid 18h 4 sequence such that F ( b) (lnln ln( )) ε 2 h +, the ε auction Weight Majority (WM) is (1+ε)-competitive relative to the optimal fixed price revenue 6 Comparison This section compares and contrast several techniques reviewed in this paper We mention the constraint and situation in which it is feasible to apply the each auction model 61 Offline Model Comparisons The competitive auction is compared with the envyfree technique The CORE technique is suitable for mass-market sale of goods Since the envy-free property is very important to the practical auctions, the CORE technique is preferable to DSPT or SCS Regarding to the revenue, the CORE technique gain some advantages from using the bigger size of bid input In DSOT or SCS, the bids are partition into half, thus lose some profit in computing F 62 Online Model Comparisons For any moderately large auction, the performance guarantee of the weighted majority auction mechanism is dramatically better than that of the weighted buckets auction As a comparison, Bar-Yossef et al show that their weighted buckets auction is O(exp log log h) - competitive [3] However, in that case, the competitive ratio is achieved for the bid sequences with F(b) 4h It is proved in [4] that weighted majority auction fails on such small bid sequences, and indeed, the paper provide a fairly tight lower bound on the sequences for which WM succeeds We can also combine the weight buckets and weighted majority by simply assigning probability ½ to each to achieve performance which is within a factor of two of the better auction 63 Online vs Offline Auctions Comparison The offline auction seems to have some advantage from knowing all the bids before the auction run This implies that the online auction may perform very poor in the very beginning of the auction However, the online auctions have some advantage in some aspect First, it appropriates to some type of digital goods where there is no explicit end time; most digital goods are sold continuously Furthermore, it is impractical for the pay-per-view in which bidders can submit their bids even after the movies start Besides, the online auctions gain benefit from the envy-free problem avoidance Since the price can have different prices at different time, the online auction may be attractive to the auctioneers 7 Open Problem There are some open problems in each of the paper we review The envy-free problem in [1] is addressed by the work in [2] However, in [2], it may also desirable to have one more property in which it prevents the
7 bidders from collude This can happen in the practical and thus can be a problem The online auction may have some trouble as well Since at the beginning of the auction the auctioneer will not have enough information to compute the result, this may require some statistic mechanism to deal with this problem The online auction may have the envy-free problem as well if two bidders submit their bids at almost the same time This may be fixed by running the auction after some short period of time instead of one by one bid References [1] AV Goldberg, JD Hartline, A Karlin, M Saks, AWright Competitive Auctions and Digital Goods, Games and Economic Behavior, 2002 [2] A V Goldberg, JD Hartline Envy-Free Auctions for Digital Goods, 2003 [3] Ziv Bar-Yossef, Kirsten Hildrum, Felix Wu Incentive-Compatible Online Auctions for Digital Goods, in Proceedings of the 13 th Annual ACM-SIAM Symposium on Discrete Algorithm (SODA), pages , 2002 [4] Avrim Blum, Vijay Kumar, Atri Ruda, Felix Wu Online Learning in Online Auctions, 2003 [5] A V Goldberg, JD Hartline Competitiveness via Consensus In Proc 14 th Symp on Discrete Alg ACM/SIAM, 2003 [6] Nick Littlestone and Manfred K Warmuth The weighted majority algorithm Information and computation, 108: , 1994
Chapter 7. Sealed-bid Auctions
Chapter 7 Sealed-bid Auctions An auction is a procedure used for selling and buying items by offering them up for bid. Auctions are often used to sell objects that have a variable price (for example oil)
More information14.1 Rent-or-buy problem
CS787: Advanced Algorithms Lecture 14: Online algorithms We now shift focus to a different kind of algorithmic problem where we need to perform some optimization without knowing the input in advance. Algorithms
More informationECON 459 Game Theory. Lecture Notes Auctions. Luca Anderlini Spring 2015
ECON 459 Game Theory Lecture Notes Auctions Luca Anderlini Spring 2015 These notes have been used before. If you can still spot any errors or have any suggestions for improvement, please let me know. 1
More informationThe Online Set Cover Problem
The Online Set Cover Problem Noga Alon Baruch Awerbuch Yossi Azar Niv Buchbinder Joseph Seffi Naor ABSTRACT Let X = {, 2,..., n} be a ground set of n elements, and let S be a family of subsets of X, S
More informationMulti-unit auctions with budget-constrained bidders
Multi-unit auctions with budget-constrained bidders Christian Borgs Jennifer Chayes Nicole Immorlica Mohammad Mahdian Amin Saberi Abstract We study a multi-unit auction with multiple agents, each of whom
More informationBalloon Popping With Applications to Ascending Auctions
Balloon Popping With Applications to Ascending Auctions Nicole Immorlica Anna R. Karlin Mohammad Mahdian Kunal Talwar Abstract We study the power of ascending auctions in a scenario in which a seller is
More informationManagerial Economics
Managerial Economics Unit 8: Auctions Rudolf Winter-Ebmer Johannes Kepler University Linz Winter Term 2012 Managerial Economics: Unit 8 - Auctions 1 / 40 Objectives Explain how managers can apply game
More informationMechanisms for Fair Attribution
Mechanisms for Fair Attribution Eric Balkanski Yaron Singer Abstract We propose a new framework for optimization under fairness constraints. The problems we consider model procurement where the goal is
More informationLecture 1: Course overview, circuits, and formulas
Lecture 1: Course overview, circuits, and formulas Topics in Complexity Theory and Pseudorandomness (Spring 2013) Rutgers University Swastik Kopparty Scribes: John Kim, Ben Lund 1 Course Information Swastik
More informationDynamic TCP Acknowledgement: Penalizing Long Delays
Dynamic TCP Acknowledgement: Penalizing Long Delays Karousatou Christina Network Algorithms June 8, 2010 Karousatou Christina (Network Algorithms) Dynamic TCP Acknowledgement June 8, 2010 1 / 63 Layout
More informationAn Auction Mechanism for a Cloud Spot Market
An Auction Mechanism for a Cloud Spot Market Adel Nadjaran Toosi, Kurt Van Mechelen, and Rajkumar Buyya December 2, 2014 Abstract Dynamic forms of resource pricing have recently been introduced by cloud
More informationOffline sorting buffers on Line
Offline sorting buffers on Line Rohit Khandekar 1 and Vinayaka Pandit 2 1 University of Waterloo, ON, Canada. email: rkhandekar@gmail.com 2 IBM India Research Lab, New Delhi. email: pvinayak@in.ibm.com
More informationOnline Primal-Dual Algorithms for Maximizing Ad-Auctions Revenue
Online Primal-Dual Algorithms for Maximizing Ad-Auctions Revenue Niv Buchbinder 1, Kamal Jain 2, and Joseph (Seffi) Naor 1 1 Computer Science Department, Technion, Haifa, Israel. 2 Microsoft Research,
More informationarxiv:1112.0829v1 [math.pr] 5 Dec 2011
How Not to Win a Million Dollars: A Counterexample to a Conjecture of L. Breiman Thomas P. Hayes arxiv:1112.0829v1 [math.pr] 5 Dec 2011 Abstract Consider a gambling game in which we are allowed to repeatedly
More informationOnline Adwords Allocation
Online Adwords Allocation Shoshana Neuburger May 6, 2009 1 Overview Many search engines auction the advertising space alongside search results. When Google interviewed Amin Saberi in 2004, their advertisement
More information9th Max-Planck Advanced Course on the Foundations of Computer Science (ADFOCS) Primal-Dual Algorithms for Online Optimization: Lecture 1
9th Max-Planck Advanced Course on the Foundations of Computer Science (ADFOCS) Primal-Dual Algorithms for Online Optimization: Lecture 1 Seffi Naor Computer Science Dept. Technion Haifa, Israel Introduction
More informationDiscrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 10
CS 70 Discrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 10 Introduction to Discrete Probability Probability theory has its origins in gambling analyzing card games, dice,
More informationA dynamic auction for multi-object procurement under a hard budget constraint
A dynamic auction for multi-object procurement under a hard budget constraint Ludwig Ensthaler Humboldt University at Berlin DIW Berlin Thomas Giebe Humboldt University at Berlin March 3, 2010 Abstract
More informationOnline and Offline Selling in Limit Order Markets
Online and Offline Selling in Limit Order Markets Kevin L. Chang 1 and Aaron Johnson 2 1 Yahoo Inc. klchang@yahoo-inc.com 2 Yale University ajohnson@cs.yale.edu Abstract. Completely automated electronic
More informationVickrey-Dutch Procurement Auction for Multiple Items
Vickrey-Dutch Procurement Auction for Multiple Items Debasis Mishra Dharmaraj Veeramani First version: August 2004, This version: March 2006 Abstract We consider a setting where there is a manufacturer
More informationU.C. Berkeley CS276: Cryptography Handout 0.1 Luca Trevisan January, 2009. Notes on Algebra
U.C. Berkeley CS276: Cryptography Handout 0.1 Luca Trevisan January, 2009 Notes on Algebra These notes contain as little theory as possible, and most results are stated without proof. Any introductory
More informationOnline Algorithms: Learning & Optimization with No Regret.
Online Algorithms: Learning & Optimization with No Regret. Daniel Golovin 1 The Setup Optimization: Model the problem (objective, constraints) Pick best decision from a feasible set. Learning: Model the
More informationThe Taxman Game. Robert K. Moniot September 5, 2003
The Taxman Game Robert K. Moniot September 5, 2003 1 Introduction Want to know how to beat the taxman? Legally, that is? Read on, and we will explore this cute little mathematical game. The taxman game
More informationTiers, Preference Similarity, and the Limits on Stable Partners
Tiers, Preference Similarity, and the Limits on Stable Partners KANDORI, Michihiro, KOJIMA, Fuhito, and YASUDA, Yosuke February 7, 2010 Preliminary and incomplete. Do not circulate. Abstract We consider
More informationNear Optimal Solutions
Near Optimal Solutions Many important optimization problems are lacking efficient solutions. NP-Complete problems unlikely to have polynomial time solutions. Good heuristics important for such problems.
More informationBayesian Nash Equilibrium
. Bayesian Nash Equilibrium . In the final two weeks: Goals Understand what a game of incomplete information (Bayesian game) is Understand how to model static Bayesian games Be able to apply Bayes Nash
More informationAnalysis of Approximation Algorithms for k-set Cover using Factor-Revealing Linear Programs
Analysis of Approximation Algorithms for k-set Cover using Factor-Revealing Linear Programs Stavros Athanassopoulos, Ioannis Caragiannis, and Christos Kaklamanis Research Academic Computer Technology Institute
More informationA Near-linear Time Constant Factor Algorithm for Unsplittable Flow Problem on Line with Bag Constraints
A Near-linear Time Constant Factor Algorithm for Unsplittable Flow Problem on Line with Bag Constraints Venkatesan T. Chakaravarthy, Anamitra R. Choudhury, and Yogish Sabharwal IBM Research - India, New
More informationCompletion Time Scheduling and the WSRPT Algorithm
Completion Time Scheduling and the WSRPT Algorithm Bo Xiong, Christine Chung Department of Computer Science, Connecticut College, New London, CT {bxiong,cchung}@conncoll.edu Abstract. We consider the online
More informationSeller-Focused Algorithms for Online Auctioning
Seller-Focused Algorithms for Online Auctioning Amitabha Bagchi, Amitabh Chaudhary, Rahul Garg, Michael T. Goodrich, and Vijay Kumar Dept of Computer Science, Johns Hopkins University, 3400 N Charles St,
More informationChristmas Gift Exchange Games
Christmas Gift Exchange Games Arpita Ghosh 1 and Mohammad Mahdian 1 Yahoo! Research Santa Clara, CA, USA. Email: arpita@yahoo-inc.com, mahdian@alum.mit.edu Abstract. The Christmas gift exchange is a popular
More informationCOMP 250 Fall 2012 lecture 2 binary representations Sept. 11, 2012
Binary numbers The reason humans represent numbers using decimal (the ten digits from 0,1,... 9) is that we have ten fingers. There is no other reason than that. There is nothing special otherwise about
More informationChapter 9. Auctions. 9.1 Types of Auctions
From the book Networks, Crowds, and Markets: Reasoning about a Highly Connected World. By David Easley and Jon Kleinberg. Cambridge University Press, 2010. Complete preprint on-line at http://www.cs.cornell.edu/home/kleinber/networks-book/
More informationOnline Financial Algorithms: Competitive Analysis
Online Financial Algorithms: Competitive Analysis Sandeep Kumar, Deepak Garg Thapar University, Patiala ABSTRACT Analysis of algorithms with complete knowledge of its inputs is sometimes not up to our
More information17.6.1 Introduction to Auction Design
CS787: Advanced Algorithms Topic: Sponsored Search Auction Design Presenter(s): Nilay, Srikrishna, Taedong 17.6.1 Introduction to Auction Design The Internet, which started of as a research project in
More informationLecture 22: November 10
CS271 Randomness & Computation Fall 2011 Lecture 22: November 10 Lecturer: Alistair Sinclair Based on scribe notes by Rafael Frongillo Disclaimer: These notes have not been subjected to the usual scrutiny
More informationCHAPTER 2 Estimating Probabilities
CHAPTER 2 Estimating Probabilities Machine Learning Copyright c 2016. Tom M. Mitchell. All rights reserved. *DRAFT OF January 24, 2016* *PLEASE DO NOT DISTRIBUTE WITHOUT AUTHOR S PERMISSION* This is a
More informationLecture 2: Universality
CS 710: Complexity Theory 1/21/2010 Lecture 2: Universality Instructor: Dieter van Melkebeek Scribe: Tyson Williams In this lecture, we introduce the notion of a universal machine, develop efficient universal
More informationFactoring & Primality
Factoring & Primality Lecturer: Dimitris Papadopoulos In this lecture we will discuss the problem of integer factorization and primality testing, two problems that have been the focus of a great amount
More informationThe Relative Worst Order Ratio for On-Line Algorithms
The Relative Worst Order Ratio for On-Line Algorithms Joan Boyar 1 and Lene M. Favrholdt 2 1 Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark, joan@imada.sdu.dk
More informationLecture 3: Finding integer solutions to systems of linear equations
Lecture 3: Finding integer solutions to systems of linear equations Algorithmic Number Theory (Fall 2014) Rutgers University Swastik Kopparty Scribe: Abhishek Bhrushundi 1 Overview The goal of this lecture
More information1. The Fly In The Ointment
Arithmetic Revisited Lesson 5: Decimal Fractions or Place Value Extended Part 5: Dividing Decimal Fractions, Part 2. The Fly In The Ointment The meaning of, say, ƒ 2 doesn't depend on whether we represent
More information3 Some Integer Functions
3 Some Integer Functions A Pair of Fundamental Integer Functions The integer function that is the heart of this section is the modulo function. However, before getting to it, let us look at some very simple
More information3. Mathematical Induction
3. MATHEMATICAL INDUCTION 83 3. Mathematical Induction 3.1. First Principle of Mathematical Induction. Let P (n) be a predicate with domain of discourse (over) the natural numbers N = {0, 1,,...}. If (1)
More informationHybrid Auctions Revisited
Hybrid Auctions Revisited Dan Levin and Lixin Ye, Abstract We examine hybrid auctions with affiliated private values and risk-averse bidders, and show that the optimal hybrid auction trades off the benefit
More informationInformation Theory and Coding Prof. S. N. Merchant Department of Electrical Engineering Indian Institute of Technology, Bombay
Information Theory and Coding Prof. S. N. Merchant Department of Electrical Engineering Indian Institute of Technology, Bombay Lecture - 17 Shannon-Fano-Elias Coding and Introduction to Arithmetic Coding
More informationHow To Find An Optimal Search Protocol For An Oblivious Cell
The Conference Call Search Problem in Wireless Networks Leah Epstein 1, and Asaf Levin 2 1 Department of Mathematics, University of Haifa, 31905 Haifa, Israel. lea@math.haifa.ac.il 2 Department of Statistics,
More informationMATH10212 Linear Algebra. Systems of Linear Equations. Definition. An n-dimensional vector is a row or a column of n numbers (or letters): a 1.
MATH10212 Linear Algebra Textbook: D. Poole, Linear Algebra: A Modern Introduction. Thompson, 2006. ISBN 0-534-40596-7. Systems of Linear Equations Definition. An n-dimensional vector is a row or a column
More informationGames of Incomplete Information
Games of Incomplete Information Jonathan Levin February 00 Introduction We now start to explore models of incomplete information. Informally, a game of incomplete information is a game where the players
More informationClass constrained bin covering
Class constrained bin covering Leah Epstein Csanád Imreh Asaf Levin Abstract We study the following variant of the bin covering problem. We are given a set of unit sized items, where each item has a color
More informationAN ANALYSIS OF A WAR-LIKE CARD GAME. Introduction
AN ANALYSIS OF A WAR-LIKE CARD GAME BORIS ALEXEEV AND JACOB TSIMERMAN Abstract. In his book Mathematical Mind-Benders, Peter Winkler poses the following open problem, originally due to the first author:
More informationHow to Price Shared Optimizations in the Cloud
How to Price Shared Optimizations in the Cloud Prasang Upadhyaya, Magdalena Balazinska, and Dan Suciu Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA {prasang,
More informationCMSC 858T: Randomized Algorithms Spring 2003 Handout 8: The Local Lemma
CMSC 858T: Randomized Algorithms Spring 2003 Handout 8: The Local Lemma Please Note: The references at the end are given for extra reading if you are interested in exploring these ideas further. You are
More informationA Note on Maximum Independent Sets in Rectangle Intersection Graphs
A Note on Maximum Independent Sets in Rectangle Intersection Graphs Timothy M. Chan School of Computer Science University of Waterloo Waterloo, Ontario N2L 3G1, Canada tmchan@uwaterloo.ca September 12,
More informationVictor Shoup Avi Rubin. fshoup,rubing@bellcore.com. Abstract
Session Key Distribution Using Smart Cards Victor Shoup Avi Rubin Bellcore, 445 South St., Morristown, NJ 07960 fshoup,rubing@bellcore.com Abstract In this paper, we investigate a method by which smart
More informationSensitivity Analysis 3.1 AN EXAMPLE FOR ANALYSIS
Sensitivity Analysis 3 We have already been introduced to sensitivity analysis in Chapter via the geometry of a simple example. We saw that the values of the decision variables and those of the slack and
More informationCh. 13.2: Mathematical Expectation
Ch. 13.2: Mathematical Expectation Random Variables Very often, we are interested in sample spaces in which the outcomes are distinct real numbers. For example, in the experiment of rolling two dice, we
More informationAn Approximation Algorithm for Bounded Degree Deletion
An Approximation Algorithm for Bounded Degree Deletion Tomáš Ebenlendr Petr Kolman Jiří Sgall Abstract Bounded Degree Deletion is the following generalization of Vertex Cover. Given an undirected graph
More informationPh.D. Thesis. Judit Nagy-György. Supervisor: Péter Hajnal Associate Professor
Online algorithms for combinatorial problems Ph.D. Thesis by Judit Nagy-György Supervisor: Péter Hajnal Associate Professor Doctoral School in Mathematics and Computer Science University of Szeged Bolyai
More informationInformation in Mechanism Design
Dirk Bergemann and Juuso Valimaki Econometric Society World Congress August 2005 Mechanism Design Economic agents have private information that is relevant for a social allocation problem. Information
More informationCompetitive Analysis of QoS Networks
Competitive Analysis of QoS Networks What is QoS? The art of performance analysis What is competitive analysis? Example: Scheduling with deadlines Example: Smoothing real-time streams Example: Overflow
More informationThe Goldberg Rao Algorithm for the Maximum Flow Problem
The Goldberg Rao Algorithm for the Maximum Flow Problem COS 528 class notes October 18, 2006 Scribe: Dávid Papp Main idea: use of the blocking flow paradigm to achieve essentially O(min{m 2/3, n 1/2 }
More informationOptimal Online-list Batch Scheduling
Optimal Online-list Batch Scheduling Jacob Jan Paulus a,, Deshi Ye b, Guochuan Zhang b a University of Twente, P.O. box 217, 7500AE Enschede, The Netherlands b Zhejiang University, Hangzhou 310027, China
More informationLinear Programming Notes VII Sensitivity Analysis
Linear Programming Notes VII Sensitivity Analysis 1 Introduction When you use a mathematical model to describe reality you must make approximations. The world is more complicated than the kinds of optimization
More information1 Solving LPs: The Simplex Algorithm of George Dantzig
Solving LPs: The Simplex Algorithm of George Dantzig. Simplex Pivoting: Dictionary Format We illustrate a general solution procedure, called the simplex algorithm, by implementing it on a very simple example.
More informationNotes on Complexity Theory Last updated: August, 2011. Lecture 1
Notes on Complexity Theory Last updated: August, 2011 Jonathan Katz Lecture 1 1 Turing Machines I assume that most students have encountered Turing machines before. (Students who have not may want to look
More informationExample 1. Consider the following two portfolios: 2. Buy one c(s(t), 20, τ, r) and sell one c(s(t), 10, τ, r).
Chapter 4 Put-Call Parity 1 Bull and Bear Financial analysts use words such as bull and bear to describe the trend in stock markets. Generally speaking, a bull market is characterized by rising prices.
More informationMODELING RANDOMNESS IN NETWORK TRAFFIC
MODELING RANDOMNESS IN NETWORK TRAFFIC - LAVANYA JOSE, INDEPENDENT WORK FALL 11 ADVISED BY PROF. MOSES CHARIKAR ABSTRACT. Sketches are randomized data structures that allow one to record properties of
More informationAn Introduction to Competitive Analysis for Online Optimization. Maurice Queyranne University of British Columbia, and IMA Visitor (Fall 2002)
An Introduction to Competitive Analysis for Online Optimization Maurice Queyranne University of British Columbia, and IMA Visitor (Fall 2002) IMA, December 11, 2002 Overview 1 Online Optimization sequences
More informationThis article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
IEEE/ACM TRANSACTIONS ON NETWORKING 1 A Greedy Link Scheduler for Wireless Networks With Gaussian Multiple-Access and Broadcast Channels Arun Sridharan, Student Member, IEEE, C Emre Koksal, Member, IEEE,
More informationOn the Interaction and Competition among Internet Service Providers
On the Interaction and Competition among Internet Service Providers Sam C.M. Lee John C.S. Lui + Abstract The current Internet architecture comprises of different privately owned Internet service providers
More informationMATH10040 Chapter 2: Prime and relatively prime numbers
MATH10040 Chapter 2: Prime and relatively prime numbers Recall the basic definition: 1. Prime numbers Definition 1.1. Recall that a positive integer is said to be prime if it has precisely two positive
More informationOnline Algorithms for Market Clearing
Online Algorithms for Market Clearing AVRIM BLUM AND TUOMAS SANDHOLM Carnegie Mellon University, Pittsburgh, Pennsylvania AND MARTIN ZINKEVICH University of Alberta, Edmonton, Alberta, Canada Abstract.
More informationDiscrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 2
CS 70 Discrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 2 Proofs Intuitively, the concept of proof should already be familiar We all like to assert things, and few of us
More informationONLINE DEGREE-BOUNDED STEINER NETWORK DESIGN. Sina Dehghani Saeed Seddighin Ali Shafahi Fall 2015
ONLINE DEGREE-BOUNDED STEINER NETWORK DESIGN Sina Dehghani Saeed Seddighin Ali Shafahi Fall 2015 ONLINE STEINER FOREST PROBLEM An initially given graph G. s 1 s 2 A sequence of demands (s i, t i ) arriving
More informationCompetitive Analysis of On line Randomized Call Control in Cellular Networks
Competitive Analysis of On line Randomized Call Control in Cellular Networks Ioannis Caragiannis Christos Kaklamanis Evi Papaioannou Abstract In this paper we address an important communication issue arising
More informationMASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 5 9/17/2008 RANDOM VARIABLES
MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 5 9/17/2008 RANDOM VARIABLES Contents 1. Random variables and measurable functions 2. Cumulative distribution functions 3. Discrete
More informationMathematical Induction
Mathematical Induction (Handout March 8, 01) The Principle of Mathematical Induction provides a means to prove infinitely many statements all at once The principle is logical rather than strictly mathematical,
More informationNan Kong, Andrew J. Schaefer. Department of Industrial Engineering, Univeristy of Pittsburgh, PA 15261, USA
A Factor 1 2 Approximation Algorithm for Two-Stage Stochastic Matching Problems Nan Kong, Andrew J. Schaefer Department of Industrial Engineering, Univeristy of Pittsburgh, PA 15261, USA Abstract We introduce
More informationAdaptive Online Gradient Descent
Adaptive Online Gradient Descent Peter L Bartlett Division of Computer Science Department of Statistics UC Berkeley Berkeley, CA 94709 bartlett@csberkeleyedu Elad Hazan IBM Almaden Research Center 650
More informationA Simple Characterization for Truth-Revealing Single-Item Auctions
A Simple Characterization for Truth-Revealing Single-Item Auctions Kamal Jain 1, Aranyak Mehta 2, Kunal Talwar 3, and Vijay Vazirani 2 1 Microsoft Research, Redmond, WA 2 College of Computing, Georgia
More informationPractical Guide to the Simplex Method of Linear Programming
Practical Guide to the Simplex Method of Linear Programming Marcel Oliver Revised: April, 0 The basic steps of the simplex algorithm Step : Write the linear programming problem in standard form Linear
More informationPolarization codes and the rate of polarization
Polarization codes and the rate of polarization Erdal Arıkan, Emre Telatar Bilkent U., EPFL Sept 10, 2008 Channel Polarization Given a binary input DMC W, i.i.d. uniformly distributed inputs (X 1,...,
More informationThe Value of Knowing a Demand Curve: Bounds on Regret for On-line Posted-Price Auctions
The Value of Knowing a Demand Curve: Bounds on Regret for On-line Posted-Price Auctions Robert Kleinberg F. Thomson Leighton Abstract We consider the revenue-maximization problem for a seller with an unlimited
More informationUsing Generalized Forecasts for Online Currency Conversion
Using Generalized Forecasts for Online Currency Conversion Kazuo Iwama and Kouki Yonezawa School of Informatics Kyoto University Kyoto 606-8501, Japan {iwama,yonezawa}@kuis.kyoto-u.ac.jp Abstract. El-Yaniv
More informationExpressive Auctions for Externalities in Online Advertising
Expressive Auctions for Externalities in Online Advertising ABSTRACT Arpita Ghosh Yahoo! Research Santa Clara, CA, USA arpita@yahoo-inc.com When online ads are shown together, they compete for user attention
More informationPrivate Approximation of Clustering and Vertex Cover
Private Approximation of Clustering and Vertex Cover Amos Beimel, Renen Hallak, and Kobbi Nissim Department of Computer Science, Ben-Gurion University of the Negev Abstract. Private approximation of search
More information1. Prove that the empty set is a subset of every set.
1. Prove that the empty set is a subset of every set. Basic Topology Written by Men-Gen Tsai email: b89902089@ntu.edu.tw Proof: For any element x of the empty set, x is also an element of every set since
More informationA Secure Protocol for the Oblivious Transfer (Extended Abstract) M. J. Fischer. Yale University. S. Micali Massachusetts Institute of Technology
J, Cryptoiogy (1996) 9:191-195 Joumol of CRYPTOLOGY O 1996 International Association for Cryptologic Research A Secure Protocol for the Oblivious Transfer (Extended Abstract) M. J. Fischer Yale University
More informationRegrets Only! Online Stochastic Optimization under Time Constraints
Regrets Only! Online Stochastic Optimization under Time Constraints Russell Bent and Pascal Van Hentenryck Brown University, Providence, RI 02912 {rbent,pvh}@cs.brown.edu Abstract This paper considers
More information1 Introduction. 2 Prediction with Expert Advice. Online Learning 9.520 Lecture 09
1 Introduction Most of the course is concerned with the batch learning problem. In this lecture, however, we look at a different model, called online. Let us first compare and contrast the two. In batch
More informationExact Nonparametric Tests for Comparing Means - A Personal Summary
Exact Nonparametric Tests for Comparing Means - A Personal Summary Karl H. Schlag European University Institute 1 December 14, 2006 1 Economics Department, European University Institute. Via della Piazzuola
More informationAn Empirical Study of Two MIS Algorithms
An Empirical Study of Two MIS Algorithms Email: Tushar Bisht and Kishore Kothapalli International Institute of Information Technology, Hyderabad Hyderabad, Andhra Pradesh, India 32. tushar.bisht@research.iiit.ac.in,
More informationStatistical Machine Translation: IBM Models 1 and 2
Statistical Machine Translation: IBM Models 1 and 2 Michael Collins 1 Introduction The next few lectures of the course will be focused on machine translation, and in particular on statistical machine translation
More information1 Review of Least Squares Solutions to Overdetermined Systems
cs4: introduction to numerical analysis /9/0 Lecture 7: Rectangular Systems and Numerical Integration Instructor: Professor Amos Ron Scribes: Mark Cowlishaw, Nathanael Fillmore Review of Least Squares
More informationHow to Price Shared Optimizations in the Cloud
How to Price Shared Optimizations in the Cloud Prasang Upadhyaya Department of Computer Science and Engineering University of Washington Seattle, WA, USA prasang@cs.uw.edu Magdalena Balazinska Department
More informationInteger Programming Formulation
Integer Programming Formulation 1 Integer Programming Introduction When we introduced linear programs in Chapter 1, we mentioned divisibility as one of the LP assumptions. Divisibility allowed us to consider
More information1.7 Graphs of Functions
64 Relations and Functions 1.7 Graphs of Functions In Section 1.4 we defined a function as a special type of relation; one in which each x-coordinate was matched with only one y-coordinate. We spent most
More information6.207/14.15: Networks Lectures 19-21: Incomplete Information: Bayesian Nash Equilibria, Auctions and Introduction to Social Learning
6.207/14.15: Networks Lectures 19-21: Incomplete Information: Bayesian Nash Equilibria, Auctions and Introduction to Social Learning Daron Acemoglu and Asu Ozdaglar MIT November 23, 25 and 30, 2009 1 Introduction
More informationAlok Gupta. Dmitry Zhdanov
RESEARCH ARTICLE GROWTH AND SUSTAINABILITY OF MANAGED SECURITY SERVICES NETWORKS: AN ECONOMIC PERSPECTIVE Alok Gupta Department of Information and Decision Sciences, Carlson School of Management, University
More information