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:
Suppose we are given a submodular function @math over a set of elements, and we want to maximize its value subject to certain constraints. Good approximation algorithms are known for such problems under both monotone and non-monotone submodular functions. We consider these problems in a stochastic setting, where elements are not all active and we can only get value from active elements. Each element @math is active independently with some known probability @math , but we don't know the element's status . We find it out only when we the element @math ---probing reveals whether it's active or not, whereafter we can use this information to decide which other elements to probe. Eventually, if we have a probed set @math and a subset @math of active elements in @math , we can pick any @math and get value @math . Moreover, the sequence of elements we probe must satisfy a given ---e.g., these may be given by a matroid, or an orienteering constraint, or deadline, or precedence constraint, or an arbitrary downward-closed constraint---if we can probe some sequence of elements we can probe any prefix of it. What is a good strategy to probe elements to maximize the expected value? In this paper we study the gap between adaptive and non-adaptive strategies for @math being a submodular or a fractionally subadditive (XOS) function. If this gap is small, we can focus on finding good non-adaptive strategies instead, which are easier to find as well as to represent. We show that the adaptivity gap is a constant for monotone and non-monotone submodular functions, and logarithmic for XOS functions of small . These bounds are nearly tight. Our techniques show new ways of arguing about the optimal adaptive decision tree for stochastic problems.

Cited Abstract(s):
[1]: 

[2]: We study a general stochastic probing problem defined on a universe V, where each element e in V is "active" independently with probability p_e. Elements have weights w_e and the goal is to maximize the weight of a chosen subset S of active elements. However, we are given only the p_e values-- to determine whether or not an element e is active, our algorithm must probe e. If element e is probed and happens to be active, then e must irrevocably be added to the chosen set S; if e is not active then it is not included in S. Moreover, the following conditions must hold in every random instantiation: (1) the set Q of probed elements satisfy an "outer" packing constraint, and (2) the set S of chosen elements satisfy an "inner" packing constraint. The kinds of packing constraints we consider are intersections of matroids and knapsacks. Our results provide a simple and unified view of results in stochastic matching and Bayesian mechanism design, and can also handle more general constraints. As an application, we obtain the first polynomial-time @math -approximate "Sequential Posted Price Mechanism" under k-matroid intersection feasibility constraints.

[3]: In this paper we consider the Stochastic Matching problem, which is motivated by applications in kidney exchange and online dating. We are given an undirected graph in which every edge is assigned a probability of existence and a positive profit, and each node is assigned a positive integer called timeout. We know whether an edge exists or not only after probing it. On this random graph we are executing a process, which one-by-one probes the edges and gradually constructs a matching. The process is constrained in two ways: once an edge is taken it cannot be removed from the matching, and the timeout of node @math upper-bounds the number of edges incident to @math that can be probed. The goal is to maximize the expected profit of the constructed matching. For this problem (Algorithmica 2012) provided a @math -approximation algorithm for bipartite graphs, and a @math -approximation for general graphs. In this work we improve the approximation factors to @math and @math , respectively. We also consider an online version of the bipartite case, where one side of the partition arrives node by node, and each time a node @math arrives we have to decide which edges incident to @math we want to probe, and in which order. Here we present a @math -approximation, improving on the @math -approximation of The main technical ingredient in our result is a novel way of probing edges according to a random but non-uniform permutation. Patching this method with an algorithm that works best for large probability edges (plus some additional ideas) leads to our improved approximation factors.

[4]: 

[5]: The stochastic matching problem with applications in online dating and kidney exchange was introduced by (2009) [1] together with a simple greedy strategy. They proved it is a 4-approximation, but conjectured that the greedy algorithm is in fact a 2-approximation. In this paper we confirm this hypothesis.

[6]: The growth of kidney exchange presents new challenges for the design of kidney exchange clearinghouses. The players now include directors of transplant centers, who see sets of patient-donor pairs, and can choose to reveal only difficult-to-match pairs to the clearinghouse, while withholding easy-to-match pairs to transplant locally. This reduces the number of transplants. We discuss how the incentives for hospitals to enroll all pairs in kidney exchange can be achieved, and how the concentration of hard to match pairs increases the importance of long, non-simultaneous nondirected donor chains.

[7]: 

[8]: 

[9]: In a stochastic probing problem we are given a universe E, and a probability that each element e in E is active. We determine if an element is active by probing it, and whenever a probed element is active, we must permanently include it in our solution. Moreover, throughout the process we need to obey inner constraints on the set of elements taken into the solution, and outer constraints on the set of all probed elements. All previous algorithmic results in this framework have considered only the problem of maximizing a linear function of the active elements. Here, we consider submodular objectives. We provide new, constant-factor approximations for maximizing a monotone submodular function subject to multiple matroid constraints on both the elements that may be taken and the elements that may be probed. We also obtain an improved approximation for linear objective functions, and show how our approach may be generalized to handle k-matchoid constraints.
The work of [7] (see also [5] [4] [1] [3] ) sought to maximize the size of a matching subject to @math -matching constraints; this was motivated by applications to online dating and kidney exchange. More generally, see, e.g. [4]5 [6] , for pointers to other work on kidney exchange problems. The work of [2] abstracted out the general problem of maximizing a function (in their case, the rank function of the intersection of matroids or knapsacks) subject to probing constraints (again, intersection of matroids and knapsacks). This was improved and generalized by Adamczyk, et al [4]6 to submodular objectives. All these results use LP relaxations, or non-linear geometric relaxations for the submodular settings.