activity
20022024
most citedSmoothed analysis of algorithms

68 citations · 163 across the 20 of their papers we have counts for

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7 papers · 1 filter

cs.GT2009

Spending is not Easier than Trading: On the Computational Equivalence of Fisher and Arrow-Debreu Equilibria

Xi Chen, Shang-Hua Teng

It is a common belief that computing a market equilibrium in Fisher's spending model is easier than computing a market equilibrium in Arrow-Debreu's exchange model. This belief is…

cs.GT20082 cited

Preference Games and Personalized Equilibria, with Applications to Fractional BGP

Laura J. Poplawski, Rajmohan Rajaraman, Ravi Sundaram +1

We study the complexity of computing equilibria in two classes of network games based on flows - fractional BGP (Border Gateway Protocol) games and fractional BBC (Bounded Budget C…

cs.GT20081 cited

Bounded Budget Connection (BBC) Games or How to make friends and influence people, on a budget

Nikolaos Laoutaris, Laura J. Poplawski, Rajmohan Rajaraman +2

Motivated by applications in social networks, peer-to-peer and overlay networks, we define and study the Bounded Budget Connection (BBC) game - we have a collection of n players or…

cs.GT20074 cited

Settling the Complexity of Computing Two-Player Nash Equilibria

Xi Chen, Xiaotie Deng, Shang-Hua Teng

We settle a long-standing open question in algorithmic game theory. We prove that Bimatrix, the problem of finding a Nash equilibrium in a two-player game, is complete for the comp…

cs.GT2007

Games on the Sperner Triangle

Kyle Burke, Shang-Hua Teng

We create a new two-player game on the Sperner Triangle based on Sperner's lemma. Our game has simple rules and several desirable properties. First, the game is always certain to h…

cs.GT20072 cited

Paths Beyond Local Search: A Nearly Tight Bound for Randomized Fixed-Point Computation

Xi Chen, Shang-Hua Teng

In 1983, Aldous proved that randomization can speedup local search. For example, it reduces the query complexity of local search over [1:n]^d from Theta (n^{d-1}) to O (d^{1/2}n^{d…