Abstract
We outline a relatively new research agenda aiming at building a new approximation paradigm by matching two distinct domains, the polynomial approximation and the exact solution of NP-hard problems by algorithms with guaranteed and non-trivial upper complexity bounds. We show how one can design approximation algorithms achieving ratios that are “forbidden” in polynomial time (unless a very unlikely complexity conjecture is confirmed) with worst-case complexity much lower than that of an exact computation.
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Notes
All the problems mentioned in the paper are defined in “Appendix”.
A polynomial time approximation schema is a sequence of algorithms parameterized by \(\epsilon > 0\) achieving approximation ratio \(1\pm \epsilon \) (depending on the optimization goal of the problem handled), for any \(\epsilon > 0\).
These are graph-problems, a feasible solution of which is a subset of vertices of the input-graph inducing a subgraph \(G'\) that satisfies some non-trivial hereditary property. A graph-property \(\pi \) is hereditary, if every subgraph of \(G'\) satisfies \(\pi \) whenever \(G'\) satisfies \(\pi \); it is non-trivial, if it is satisfied for infinitely many graphs and is false for infinitely many graphs.
The Exponential Time Hypothesis claims, informally, that for any k, k -sat cannot be solved in subexponential time.
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The very constructive suggestions and comments of two anonymous reviewers are gratefully acknowledged.
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This is an updated version of the paper that appeared in 4OR, 13(3), 227–245 (2015).
Appendix
Appendix
1.1 A list of combinatorial problems
- max independent set::
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Given a graph G(V, E), max independent set consists of finding a set \(S \subseteq V\) of maximum size such that for any \((u,v) \in S \times S\), \((u,v) \notin E\).
- max clique::
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Given a graph G(V, E), max clique consists of finding a set \(K \subseteq V\) of maximum size such that for any \((u,v) \in K \times K\), \((u,v) \in E\).
- min set cover::
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Given a set C of cardinality n and a system \(\mathcal {S} = \{S_1, \ldots , S_m\} \subset 2^C\), min set cover consists of determining a minimum size subsystem \(\mathcal {S}'\) such that \(\cup _{S \in \mathcal {S}'}S = C\).
- min vertex cover::
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Given a graph G(V, E), min vertex cover consists of finding a set \(C \subseteq V\) of minimum size such that, for every \((u,v) \in E\), either u, or v belongs to C.
- min coloring::
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Given a graph G(V, E), min coloring consists of partitioning the vertex-set V of G into a minimum number of independent sets.
- min independent dominating set::
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Given a graph G(V, E), min independent dominating set consists of finding the smallest independent set of G that is maximal for the inclusion.
- capacitated dominating set::
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Given a graph G(V, E) with each of its vertex v equipped with a number c(v) that represents the number of the other vertices that v can dominate, a set \(S \subset V\) is a capacitated dominating set if there exists a function \(f_{S}: V \setminus S \rightarrow S\) such that \(f_S(v)\) is a neighbor of v for each \(v \in V \setminus S\) and \(|f^{-1}_S(v)| \leqslant c(v)\). The goal here is to determine a capacitated dominating set of the smallest possible size.
- min bandwidth::
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Given a graph G(V, E), the min bandwidth problem consists of labeling the vertices of V with distinct integers \(f(v_i)\), in such a way that the quantity \(\max \{|f(v_i) - f(v_j)|: v_iv_j \in E\}\) is minimized.
- max tsp::
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Given a complete edge-weighted graph G, the objective is to determine a maximum-weight Hamiltonian cycle of G.
- bin packing::
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Given an infinite number of bins \(B_1, \ldots ,\) with capacity 1, and a list of n items with sizes \(a_1,\dots ,a_n \in ~[0, 1]\), the objective is to pack them into a minimum number of bins with the constraint \(\sum _{i \in B_j}a_i \leqslant 1\).
- max sat::
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Given a set of m disjunctive clauses over a set of n variables, max sat consists of finding a truth assignment for the variables that maximizes the number of satisfied clauses.
- k-sat::
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Given a set of m disjunctive clauses, each of them containing at most k literals), over a set of n variables, does there exist a truth-assignment on the variables simultaneously satisfying all of them?
- hamiltonian path::
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Given a graph G(V, E), does there exist a Hamiltonian path (i.e., a simple path that visits all the vertices of V) in G?
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Paschos, V.T. When polynomial approximation meets exact computation. Ann Oper Res 271, 87–103 (2018). https://doi.org/10.1007/s10479-018-2986-9
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DOI: https://doi.org/10.1007/s10479-018-2986-9