You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
We design new algorithms for maximizing a monotone non-negative submodular function under various constraints, which improve the state-of-the-art in time complexity and or performance guarantee. We first investigate the cardinality constrained submodular maximization problem that has been widely studied for about four decades. We design an @math -approximation algorithm that makes @math queries. To the best of our knowledge, this is the fastest known algorithm. We further answer the open problem on finding a lower bound on the number of queries. We show that, no (randomized) algorithm can achieve a ratio better than @math with @math queries. The acceleration above is achieved by our (ADT) algorithm. Based on ADT, we study the @math -system and @math knapsack constrained maximization problem. We show that an @math -approximate solution can be computed via @math queries. Note that it improves the state of the art in both time complexity and approximation ratio. We also show how to improve the ratio for a single knapsack constraint via @math queries. For maximizing a submodular function with curvature @math under matroid constraint, we show an @math -approximate algorithm that uses @math value oracle queries. Our ADT could be utilized to obtain faster algorithms in other problems. To prove our results, we introduce a general characterization between randomized complexity and deterministic complexity of approximation algorithms that could be used in other problems and may be interesting in its own right.

Cited Abstract(s):
[1]: There has been much progress recently on improved approximations for problems involving submodular objective functions, and many interesting techniques have been developed. However, the resulting algorithms are often slow and impractical. In this paper we develop algorithms that match the best known approximation guarantees, but with significantly improved running times, for maximizing a monotone submodular function f: 2[n] → R+ subject to various constraints. As in previous work, we measure the number of oracle calls to the objective function which is the dominating term in the running time. Our first result is a simple algorithm that gives a (1--1 e -- e)-approximation for a cardinality constraint using O(n e log n e) queries, and a 1 (p + 2e + 1 + e)-approximation for the intersection of a p-system and e knapsack (linear) constraints using O (n e2 log2 n e) queries. This is the first approximation for a p-system combined with linear constraints. (We also show that the factor of p cannot be improved for maximizing over a p-system.) The main idea behind these algorithms serves as a building block in our more sophisticated algorithms. Our main result is a new variant of the continuous greedy algorithm, which interpolates between the classical greedy algorithm and a truly continuous algorithm. We show how this algorithm can be implemented for matroid and knapsack constraints using O(n2) oracle calls to the objective function. (Previous variants and alternative techniques were known to use at least O(n4) oracle calls.) This leads to an O(n2 e4 log2 n e)-time (1--1 e -- e)-approximation for a matroid constraint. For a knapsack constraint, we develop a more involved (1--1 e -- e)-approximation algorithm that runs in time O(n2(1 e log n)poly(1 e)).

[2]: 

[3]: Is it possible to maximize a monotone submodular function faster than the widely used lazy greedy algorithm (also known as accelerated greedy), both in theory and practice? In this paper, we develop the first linear-time algorithm for maximizing a general monotone submodular function subject to a cardinality constraint. We show that our randomized algorithm, STOCHASTIC-GREEDY, can achieve a @math approximation guarantee, in expectation, to the optimum solution in time linear in the size of the data and independent of the cardinality constraint. We empirically demonstrate the effectiveness of our algorithm on submodular functions arising in data summarization, including training large-scale kernel methods, exemplar-based clustering, and sensor placement. We observe that STOCHASTIC-GREEDY practically achieves the same utility value as lazy greedy but runs much faster. More surprisingly, we observe that in many practical scenarios STOCHASTIC-GREEDY does not evaluate the whole fraction of data points even once and still achieves indistinguishable results compared to lazy greedy.
There is a large body of literature on submodular maximization, thus we mention only a few which is most relevant to our work. Besides the aforementioned results, there are also some other well-known results towards more practical algorithm design. For the simple cardinality constraint, there is also a stochastic greedy algorithm which uses @math value oracle queries while achieving @math -approximation [3] . As for the general matroid constraint, Badanidiyuru and Vondr @math k [1] also proposed an accelerated continuous greedy algorithm which uses @math value oracle queries and @math matroid independence queries. Later [2] improve this result while exhibit an tradeoff between the value oracle query and independence query. They also give a @math -approximation algorithm for cardinality-constrained non-monotone submodular maximizing problem, which requires @math function value query.