3,309 research outputs found
Hardness Amplification of Optimization Problems
In this paper, we prove a general hardness amplification scheme for optimization problems based on the technique of direct products.
We say that an optimization problem ? is direct product feasible if it is possible to efficiently aggregate any k instances of ? and form one large instance of ? such that given an optimal feasible solution to the larger instance, we can efficiently find optimal feasible solutions to all the k smaller instances. Given a direct product feasible optimization problem ?, our hardness amplification theorem may be informally stated as follows:
If there is a distribution D over instances of ? of size n such that every randomized algorithm running in time t(n) fails to solve ? on 1/?(n) fraction of inputs sampled from D, then, assuming some relationships on ?(n) and t(n), there is a distribution D\u27 over instances of ? of size O(n??(n)) such that every randomized algorithm running in time t(n)/poly(?(n)) fails to solve ? on 99/100 fraction of inputs sampled from D\u27.
As a consequence of the above theorem, we show hardness amplification of problems in various classes such as NP-hard problems like Max-Clique, Knapsack, and Max-SAT, problems in P such as Longest Common Subsequence, Edit Distance, Matrix Multiplication, and even problems in TFNP such as Factoring and computing Nash equilibrium
An Efficient Representation for Filtrations of Simplicial Complexes
A filtration over a simplicial complex is an ordering of the simplices of
such that all prefixes in the ordering are subcomplexes of . Filtrations
are at the core of Persistent Homology, a major tool in Topological Data
Analysis. In order to represent the filtration of a simplicial complex, the
entire filtration can be appended to any data structure that explicitly stores
all the simplices of the complex such as the Hasse diagram or the recently
introduced Simplex Tree [Algorithmica '14]. However, with the popularity of
various computational methods that need to handle simplicial complexes, and
with the rapidly increasing size of the complexes, the task of finding a
compact data structure that can still support efficient queries is of great
interest.
In this paper, we propose a new data structure called the Critical Simplex
Diagram (CSD) which is a variant of the Simplex Array List (SAL) [Algorithmica
'17]. Our data structure allows one to store in a compact way the filtration of
a simplicial complex, and allows for the efficient implementation of a large
range of basic operations. Moreover, we prove that our data structure is
essentially optimal with respect to the requisite storage space. Finally, we
show that the CSD representation admits fast construction algorithms for Flag
complexes and relaxed Delaunay complexes.Comment: A preliminary version appeared in SODA 201
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