Proves CLS-hardness for Nash equilibrium computation in two-team polymatrix games with zero-sum or coordination pairwise payoffs, with tight CLS membership when one team has independent adversaries, plus an ε-Nash algorithm with 1/ε² runtime dependence.
[BS18] Noam Brown and Tuomas Sandholm
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Empirical evaluation of sampling-based distinct combination estimation for multi-attribute GROUP-BY queries, including a new workload generator, tests on real datasets and TPC-H, error analysis, and recommendations for estimator design.
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The Complexity of Two-Team Polymatrix Games with Independent Adversaries
Proves CLS-hardness for Nash equilibrium computation in two-team polymatrix games with zero-sum or coordination pairwise payoffs, with tight CLS membership when one team has independent adversaries, plus an ε-Nash algorithm with 1/ε² runtime dependence.
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From Single to Multiple Attributes: Experimental Insights on Sampling-Based Distinct Combination Estimation in GROUP-BY Queries
Empirical evaluation of sampling-based distinct combination estimation for multi-attribute GROUP-BY queries, including a new workload generator, tests on real datasets and TPC-H, error analysis, and recommendations for estimator design.