FOCS 2011
TechTalks from event: FOCS 2011
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Markov LayoutConsider the following problem of laying out a set of $n$ images that match a query onto the nodes of a $\sqrt{n}\times\sqrt{n}$ grid. We are given a score for each image, as well as the distribution of patterns by which a user's eye scans the nodes of the grid and we wish to maximize the expected total score of images selected by the user. This is a special case of the \emph{Markov layout problem}, in which we are given a Markov chain $M$ together with a set o f objects to be placed at the states of the Markov chain. Each object has a utility to the user if viewed, as well as a stopping probability with which the user ceases to look further at objects. We point out that this layout problem is prototypical in a number of applications in web search and advertising, particularly in the emerging genre of search results pages from major engines. In a different class of applications, the states of the Markov chain are web pages at a publishers website and the objects are advertisements. In this paper we study the approximability of the Markov layout problem. Our main result is an $O(\log n)$ approximation algorithm for the most general version of the problem. The core idea behind the algorithm is to transform an optimization problem over partial permutations into an optimization problem over sets by losing a logarithmic factor in approximation; the latter problem is then shown to be submodular with two matroid constraints, which admits a constantfactor approximation. In contrast, we also show the problem is APXhard via a reduction from {\sc Cubic MaxBisection}. We then study harder variants of the problem in which no \emph{gaps}  states of $M$ with no object placed on them  are allowed. By exploiting the geometry, we obtain an $O(\log^{3/2} n)$ approximation algorithm when the digraph underlying $M$ is a grid and an $O(\log n)$ approximation algorithm when it is a tree. These special cases are especially appropriate for our applications.

Limitations of Randomized Mechanisms for Combinatorial AuctionsThe design of computationally efficient and incentive compatible mechanisms that solve or approximate fundamental resource allocation problems is the main goal of algorithmic mechanism design. A central example in both theory and practice is welfaremaximization in combinatorial auctions. Recently, a randomized mechanism has been discovered for combinatorial auctions that is truthful in expectation and guarantees a $(11/e)$approximation to the optimal social welfare when players have coverage valuations \cite{DRY11}. This approximation ratio is the best possible even for nontruthful algorithms, assuming $P \neq NP$ \cite{KLMM05}. Given the recent sequence of negative results for combinatorial auctions under more restrictive notions of incentive compatibility \cite{DN07,BDFKMPSSU10,Dobzin11}, this development raises a natural question: Are truthfulinexpectation mechanisms compatible with polynomialtime approximation in a way that deterministic or universally truthful mechanisms are not? In particular, can polynomialtime truthfulinexpectation mechanisms guarantee a nearoptimal approximation ratio for more general variants of combinatorial auctions? We prove that this is not the case. Specifically, the result of \cite{DRY11} cannot be extended to combinatorial auctions with submodular valuations in the value oracle model. (Absent strategic considerations, a $(11/e)$approximation is still achievable in this setting \cite{V08}.) More precisely, we prove that there is a constant $\gamma>0$ such that there is no randomized mechanism that is truthfulinexpectation  or even approximately truthfulinexpectation  and guarantees an $m^{\gamma}$approximation to the optimal social welfare for combinatorial auctions with submodular valuations in the value oracle model. We also prove an analogous result for the flexible combinatorial public projects (CPP) problem, where a truthfulinexpectation $(11/e)$approximation for coverage valuations has been recently developed \cite{Dughmi11}. We show that there is no truthfulinexpectation  or even approximately truthfulinexpectation  mechanism that achieves an $m^{\gamma}$approximation to the optimal social welfare for combinatorial public projects with submodular valuations in the value oracle model. Both our results present an unexpected separation between coverage functions and submodular functions, which does not occur for these problems without strategic considerations.

Bayesian Combinatorial Auctions: Expanding Single Buyer Mechanisms to Many BuyersFor Bayesian combinatorial auctions, we present a general framework for reducing the mechanism design problem for many buyers to the mechanism design problem for one buyer. Our generic reduction works for any separable objective (e.g., welfare, revenue, etc) and any space of valuations (e.g. submodular, additive, etc) and any distribution of valuations as long as valuations of different buyers are distributed independently (not necessarily identically). Roughly speaking, we present two generic $n$buyer mechanisms that use $1$buyer mechanisms as black boxes. We show that if we have an $\alpha$approximate $1$buyer mechanism for each buyer\footnote{Note that we can use different $1$buyer mechanisms for different buyers.} then our generic $n$buyer mechanisms are $\frac{1}{2}\alpha$approximation of the optimal $n$buyer mechanism. Furthermore, if we have several copies of each item and no buyer ever needs more than $\frac{1}{k}$ of all copies of each item then our generic $n$buyer mechanisms are $\gamma_k \alpha$approximation of the optimal $n$buyer mechanism where $\gamma_k \ge 1\frac{1}{\sqrt{k+3}}$. Observe that $\gamma_k$ is at least $\frac{1}{2}$ and approaches $1$ as $k$ increases. Applications of our main theorem include the following improvements on results from the literature. For each of the following models we construct a $1$buyer mechanism and then apply our generic expansion: For revenue maximization in combinatorial auctions with hard budget constraints, \cite{BGGM10} presented a $\frac{1}{4}$approximate BIC mechanism for additive/correlated valuations and an $O(1)$approximate\footnote{$O(1)=\frac{1}{96}$} sequential posted pricing mechanism for additive/independent valuations. We improve this to a $\gamma_k$approximate BIC mechanism and a $\gamma_k (1\frac{1}{e})$approximate sequential posted pricing mechanism respectively. For revenue maximization in combinatorial auctions with unit demand buyers, \cite{CHMS10} presented a $\frac{1}{6.75}$approximate sequential posted pricing mechanism. We improve this to a $\frac{1}{2} \gamma_k$ approximate sequential posted pricing mechanism. We also present a $\gamma_k$approximate sequential posted pricing mechanism for unitdemand multiunit auctions(homogeneous) with hardbudget constraints. Furthermore, our sequential posted pricing mechanisms assume no control or prior information about the order in which buyers arrive.

ExtremeValue Theorems for Optimal Multidimensional PricingWe provide a Polynomial Time Approximation Scheme for the {\em multidimensional unitdemand pricing problem}, when the buyer's values are independent (but not necessarily identically distributed.) For all $\epsilon>0$, we obtain a $(1+\epsilon)$factor approximation to the optimal revenue in time polynomial, when the values are sampled from Monotone Hazard Rate (MHR) distributions, quasipolynomial, when sampled from regular distributions, and polynomial in $n^{{\rm poly}(\log r)}$, when sampled from general distributions supported on a set $[u_{min}, r u_{min}]$. We also provide an additive PTAS for all bounded distributions.Our algorithms are based on novel extreme value theorems for MHR and regular distributions, and apply probabilistic techniques to understand the statistical properties of revenue distributions, as well as to reduce the size of the search space of the algorithm. As a byproduct of our techniques, we establish structural properties of optimal solutions. We show that, for all $\epsilon >0$, $g(1/\epsilon)$ distinct prices suffice to obtain a $(1+\epsilon)$factor approximation to the optimal revenue for MHR distributions, where $g(1/\epsilon)$ is a quasilinear function of $1/\epsilon$ that does not depend on the number of items. Similarly, for all $\epsilon>0$ and $n>0$, $g(1/\epsilon \cdot \log n)$ distinct prices suffice for regular distributions, where $n$ is the number of items and $g(\cdot)$ is a polynomial function. Finally, in the i.i.d. MHR case, we show that, as long as the number of items is a sufficiently large function of $1/\epsilon$, a single price suffices to achieve a $(1+\epsilon)$factor approximation.

Efficient computation of approximate pure Nash equilibria in congestion gamesCongestion games constitute an important class of games in which computing an exact or even approximate pure Nash equilibrium is in general {\sf PLS}complete. We present a surprisingly simple polynomialtime algorithm that computes $O(1)$approximate Nash equilibria in these games. In particular, for congestion games with linear latency functions, our algorithm computes $(2+\epsilon)$approximate pure Nash equilibria in time polynomial in the number of players, the number of resources and $1/\epsilon$. It also applies to games with polynomial latency functions with constant maximum degree $d$; there, the approximation guarantee is $d^{O(d)}$. The algorithm essentially identifies a polynomially long sequence of bestresponse moves that lead to an approximate equilibrium; the existence of such short sequences is interesting in itself. These are the first positive algorithmic results for approximate equilibria in nonsymmetric congestion games. We strengthen them further by proving that, for congestion games that deviate from our mild assumptions, computing $\rho$approximate equilibria is {\sf PLS}complete for any polynomialtime computable $\rho$.
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On Range Searching in the Group Model and Combinatorial DiscrepancyIn this paper we establish an intimate connection between dynamic range searching in the group model and combinatorial discrepancy. Our result states that, for a broad class of range searching data structures (including all known upper bounds), it must hold that $t_u t_q = \Omega(\disc^2/\lg n)$ where $t_u$ is the worst case update time, $t_q$ the worst case query time and $\disc$ is the combinatorial discrepancy of the range searching problem in question. This relation immediately implies a whole range of exceptionally high and neartight lower bounds for all of the basic range searching problems. We list a few of them in the following: \begin{itemize} \item For halfspace range searching in $d$dimensional space, we get a lower bound of $t_u t_q = \Omega(n^{11/d}/\lg n)$. This comes within a $\lg n \lg \lg n$ factor of the best known upper bound. \item For orthogonal range searching in $d$dimensional space, we get a lower bound of $t_u t_q = \Omega(\lg^{d2+\mu(d)}n)$, where $\mu(d)>0$ is some small but strictly positive function of $d$. \item For ball range searching in $d$dimensional space, we get a lower bound of $t_u t_q = \Omega(n^{11/d}/\lg n)$. \end{itemize} We note that the previous highest lower bound for any explicit problem, due to Patrascu [STOC'07], states that $t_q =\Omega((\lg n/\lg(\lg n+t_u))^2)$, which does however hold for a less restrictive class of data structures. Our result also has implications for the field of combinatorial discrepancy. Using textbook range searching solutions, we improve on the best known discrepancy upper bound for axisaligned rectangles in dimensions $d \geq 3$.

A Randomized Rounding Approach to the Traveling Salesman ProblemFor some positive constant $\epsilon_0$, we give a $(\frac{3}{2}\epsilon_0$)approximation algorithm for the following problem: given a graph $G_0=(V,E_0)$, find the shortest tour that visits every vertex at least once. This is a special case of the metric traveling salesman problem when the underlying metric is defined by shortest path distances in $G_0$. The result improves on the $\frac{3}{2}$approximation algorithm due to Christofides for this special case.Similar to Christofides, our algorithm finds a spanning tree whose cost is upper bounded by the optimum, then it finds the minimum cost Eulerian augmentation (or Tjoin) of that tree. The main difference is in the selection of the spanning tree. Except in certain cases where the solution of LP is nearly integral, we select the spanning tree randomly by sampling from a maximum entropy distribution defined by the linear programming relaxation. Despite the simplicity of the algorithm, the analysis builds on a variety of ideas such as properties of strongly Rayleigh measures from probability theory, graph theoretical results on the structure of near minimum cuts, and the integrality of the Tjoin polytope from polyhedral theory. Also, as a byproduct of our result, we show new properties of the near minimum cuts of any graph, which may be of independent interest.

Approximating Graphic TSP by MatchingsWe present a framework for approximating the metric TSP based on a novel use of matchings. Traditionally, matchings have been used to add edges in order to make a given graph Eulerian, whereas our approach also allows for the removal of certain edges leading to a decreased cost. For the TSP on graphic metrics (graphTSP), the approach yields a 1.461approximation algorithm with respect to the HeldKarp lower bound. For graphTSP restricted to a class of graphs that contains degree three bounded and clawfree graphs, we show that the integrality gap of the HeldKarp relaxation matches the conjectured ratio 4/3. The framework allows for generalizations in a natural way and also leads to a 1.586approximation algorithm for the traveling salesman path problem on graphic metrics where the start and end vertices are prespecified.