Topic
Average-case complexity
About: Average-case complexity is a research topic. Over the lifetime, 1749 publications have been published within this topic receiving 44972 citations.
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TL;DR: A lower bound on the linear complexity of the Naor–Reingold sequence is obtained and this result solves an open problem proposed by Igor Shparlinski and improves known results in some cases.
6 citations
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TL;DR: The notion of edge-induced vertex-cuts is introduced and the usefulness of the notion by applications by applications in network reliability and constraint satisfaction is demonstrated.
Abstract: Motivated by hypergraph decomposition algorithms, we introduce the notion of edge-induced vertex-cuts and compare it with the well-known notions of edge-cuts and vertex-cuts. We investigate the complexity of computing minimum edge-induced vertex-cuts and demonstrate the usefulness of our notion by applications in network reliability and constraint satisfaction.
6 citations
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30 Mar 2016TL;DR: Simulation results show that the ill-RCKB provides significant complexity reduction without compromising the performance; this is achieved by discarding irrelevant nodes that have distance metrics greater than a pruned radius value, which depends on the channel condition number.
Abstract: The traditional K-best sphere decoder retains the best K-nodes at each level of the search tree; these K-nodes, include irrelevant nodes which increase the complexity without improving the performance. A variant of the K-best sphere decoding algorithm for ill-conditioned MIMO channels is proposed, namely, the ill-conditioned reduced complexity K-best algorithm (ill-RCKB). The ill-RCKB provides lower complexity than the traditional K-best algorithm without sacrificing its performance; this is achieved by discarding irrelevant nodes that have distance metrics greater than a pruned radius value, which depends on the channel condition number. A hybrid-RCKB decoder is also proposed in order to balance the performance and complexity in both well and ill-conditioned channels. Complexity analysis for the proposed algorithms is provided as well. Simulation results show that the ill-RCKB provides significant complexity reduction without compromising the performance.
6 citations
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28 Dec 2015TL;DR: This paper proposes a fast algorithm for the evaluation of the ML cost function for complex-valued data over all frequencies on a Fourier grid and up to a maximum model order.
Abstract: Periodic signals are encountered in many applications. Such signals can be modelled by a weighted sum of sinusoidal components whose frequencies are integer multiples of a fundamental frequency. Given a data set, the fundamental frequency can be estimated in many ways including a maximum likelihood (ML) approach. Unfortunately, the ML estimator has a very high computational complexity, and the more inaccurate, but faster correlation-based estimators are therefore often used instead. In this paper, we propose a fast algorithm for the evaluation of the ML cost function for complex-valued data over all frequencies on a Fourier grid and up to a maximum model order. The proposed algorithm significantly reduces the computational complexity to a level not far from the complexity of the popular harmonic summation method which is an approximate ML estimator.
6 citations
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TL;DR: It is shown that the argument for polynomial expected complexity does not hold and the expected complexity of the ATSP under BnB subtour elimination isPolynomial or exponential in the number of cities.
6 citations