Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adaptive Robust Optimization with Nearly Submodular Structure   Constrained submodular maximization has been extensively studied in the
recent years. In this paper, we study adaptive robust optimization with nearly
submodular structure (ARONSS). Our objective is to randomly select a subset of
items that maximizes the worst-case value of several reward functions
simultaneously. Our work differs from existing studies in two ways: (1) we
study the robust optimization problem under the adaptive setting, i.e., one
needs to adaptively select items based on the feedback collected from picked
items, and (2) our results apply to a broad range of reward functions
characterized by $\epsilon$-nearly submodular function. We first analyze the
adaptvity gap of ARONSS and show that the gap between the best adaptive
solution and the best non-adaptive solution is bounded. Then we propose a
approximate solution to this problem when all reward functions are submodular.
Our algorithm achieves approximation ratio $(1-1/e)$ when considering matroid
constraint. At last, we present two heuristics for the general case. All
proposed solutions are non-adaptive which are easy to implement.

25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int