17,799 research outputs found
Accurate and Efficient Expression Evaluation and Linear Algebra
We survey and unify recent results on the existence of accurate algorithms
for evaluating multivariate polynomials, and more generally for accurate
numerical linear algebra with structured matrices. By "accurate" we mean that
the computed answer has relative error less than 1, i.e., has some correct
leading digits. We also address efficiency, by which we mean algorithms that
run in polynomial time in the size of the input. Our results will depend
strongly on the model of arithmetic: Most of our results will use the so-called
Traditional Model (TM). We give a set of necessary and sufficient conditions to
decide whether a high accuracy algorithm exists in the TM, and describe
progress toward a decision procedure that will take any problem and provide
either a high accuracy algorithm or a proof that none exists. When no accurate
algorithm exists in the TM, it is natural to extend the set of available
accurate operations by a library of additional operations, such as , dot
products, or indeed any enumerable set which could then be used to build
further accurate algorithms. We show how our accurate algorithms and decision
procedure for finding them extend to this case. Finally, we address other
models of arithmetic, and the relationship between (im)possibility in the TM
and (in)efficient algorithms operating on numbers represented as bit strings.Comment: 49 pages, 6 figures, 1 tabl
Algorithmic Complexity of Power Law Networks
It was experimentally observed that the majority of real-world networks
follow power law degree distribution. The aim of this paper is to study the
algorithmic complexity of such "typical" networks. The contribution of this
work is twofold.
First, we define a deterministic condition for checking whether a graph has a
power law degree distribution and experimentally validate it on real-world
networks. This definition allows us to derive interesting properties of power
law networks. We observe that for exponents of the degree distribution in the
range such networks exhibit double power law phenomenon that was
observed for several real-world networks. Our observation indicates that this
phenomenon could be explained by just pure graph theoretical properties.
The second aim of our work is to give a novel theoretical explanation why
many algorithms run faster on real-world data than what is predicted by
algorithmic worst-case analysis. We show how to exploit the power law degree
distribution to design faster algorithms for a number of classical P-time
problems including transitive closure, maximum matching, determinant, PageRank
and matrix inverse. Moreover, we deal with the problems of counting triangles
and finding maximum clique. Previously, it has been only shown that these
problems can be solved very efficiently on power law graphs when these graphs
are random, e.g., drawn at random from some distribution. However, it is
unclear how to relate such a theoretical analysis to real-world graphs, which
are fixed. Instead of that, we show that the randomness assumption can be
replaced with a simple condition on the degrees of adjacent vertices, which can
be used to obtain similar results. As a result, in some range of power law
exponents, we are able to solve the maximum clique problem in polynomial time,
although in general power law networks the problem is NP-complete
Computational linear algebra over finite fields
We present here algorithms for efficient computation of linear algebra
problems over finite fields
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