# Algorithms and Complexity Seminar

 Siu On Chan: Approximate Constraint Satisfaction Requires Large LP Relaxations Wednesday, October 16, 2013 - 4:00pm to 5:00pmWe prove super-polynomial lower bounds on the size of linear programming relaxations for approximation versions of constraint satisfaction problems. Huy L. Nguyen: Cutting corners cheaply, or how to remove Steiner points Wednesday, October 9, 2013 - 4:00pm to 5:00pmOur main result is that the Steiner Point Removal (SPR) problem can always be solved with polylogarithmic distortion, which resolves in the affirmative a question posed by Chan, Xia, Konjevod, and Richa (2006). Private Analysis of GraphsWednesday, October 2, 2013 - 4:00pm to 5:00pmWe discuss algorithms for the private analysis of network data. Such algorithms work on data sets that contain sensitive relationship information (for example, romantic ties). Ruta Mehta: (Essentially) Resolving the Complexity of Constant Rank Bimatrix Games Wednesday, November 6, 2013 - 4:00pm to 5:00pmThe rank of a bimatrix game (A, B) is defined as the rank of (A+B). For zero-sum games, i.e., rank-0, von Neumann (1928) showed that Nash equilibrium are min-max strategies, which is equivalent to the linear programming duality. Property Testing and Communication ComplexityWednesday, September 11, 2013 - 4:00pm to 5:00pmProperty testing, pioneered by Blum, Goldreich, Goldwasser, Luby, Ron, Rubinfeld and Sudan, is the study of extremely fast randomized algorithms for approximate decision making. One algorithm to rule them all: One join query at a time Friday, July 12, 2013 - 4:00pm to 5:00pmWe present a recent algorithm (PODS 2012) that is the first provably optimal (worst-case) algorithm to compute database joins. The Acquaintance Time of a GraphThursday, June 20, 2013 - 11:00am to 12:00pmWe define the following parameter of connected graphs. For a given graph $G = (V,E)$ we place one agent in each vertex $v \in V$. Every pair of agents sharing a common edge are said to be acquainted. Testing Probability Distributions Using Conditional Samples Wednesday, June 12, 2013 - 1:30pm to 2:30pmOne of the most fundamental problem paradigms in statistics is that of inferring some information about an unknown probability distribution D, given access to independent samples drawn from it. More than a decade ago, Batu et al. Approximating Large Frequency Moments with Pick-and-Drop SamplingWednesday, May 22, 2013 - 4:00pm to 5:00pmGiven data stream $D = \{p_1,p_2,...,p_m\}$ of size $m$ of numbers from $\{1,..., n\}$, the frequency of $i$ is defined as $f_i = |\{j: p_j = i\}|$. The $k$-th \emph{frequency moment} of $D$ is defined as \$F_k = IP = PSPACE using error correcting codes Wednesday, May 15, 2013 - 4:00pm to 5:00pmThe IP theorem, which asserts that IP = PSPACE (Lund et. al., and Shamir, in J. ACM 39(4)), is one of the major achievements of complexity theory. The known proofs of the theorem are based on the arithmetization technique, which transforms a quantified Boolean formula into a related polynomial.