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Found 8 papers, 2 papers with code
Unveiling the Limits of Learned Local Search Heuristics: Are You the Mightiest of the Meek?
In recent years, combining neural networks with local search heuristics has become popular in the field of combinatorial optimization.
RELS-DQN: A Robust and Efficient Local Search Framework for Combinatorial Optimization
Combinatorial optimization (CO) aims to efficiently find the best solution to NP-hard problems ranging from statistical physics to social media marketing.
Learning Strategic Value and Cooperation in Multi-Player Stochastic Games through Side Payments
For general-sum, n-player, strategic games with transferable utility, the Harsanyi-Shapley value provides a computable method to both 1) quantify the strategic value of a player; and 2) make cooperation rational through side payments.
Scalable Distributed Algorithms for Size-Constrained Submodular Maximization in the MapReduce and Adaptive Complexity Models
Distributed maximization of a submodular function in the MapReduce (MR) model has received much attention, culminating in two frameworks that allow a centralized algorithm to be run in the MR setting without loss of approximation, as long as the centralized algorithm satisfies a certain consistency property - which had previously only been known to be satisfied by the standard greedy and continous greedy algorithms.
Best of Both Worlds: Practical and Theoretically Optimal Submodular Maximization in Parallel
For the problem of maximizing a monotone, submodular function with respect to a cardinality constraint $k$ on a ground set of size $n$, we provide an algorithm that achieves the state-of-the-art in both its empirical performance and its theoretical properties, in terms of adaptive complexity, query complexity, and approximation ratio; that is, it obtains, with high probability, query complexity of $O(n)$ in expectation, adaptivity of $O(\log(n))$, and approximation ratio of nearly $1-1/e$.
Simultaenous Sieves: A Deterministic Streaming Algorithm for Non-Monotone Submodular Maximization
In general, our algorithm achieves ratio $\alpha / (1 + \alpha) - \varepsilon$, for any $\varepsilon > 0$, where $\alpha$ is the ratio of an offline (deterministic) algorithm for SMCC used for post-processing.
Quick Streaming Algorithms for Maximization of Monotone Submodular Functions in Linear Time
In addition, we propose a deterministic, multi-pass streaming algorithm with a constant number of passes that achieves nearly the optimal ratio with linear query and time complexities.
Practical and Parallelizable Algorithms for Non-Monotone Submodular Maximization with Size Constraint
In this version, we propose a fixed and improved subroutine to add a set with high average marginal gain, ThreshSeq, which returns a solution in $O( \log(n) )$ adaptive rounds with high probability.
