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: Frequent Improve K-best Sphere Decoding (FIKSD) algorithm with stopping rule depending on the Manhattan metric is proposed to use with FIKSD in order to achieve the lowest complexity.
Abstract: Multiple-Input Multiple-Output (MIMO) technique is a key technology to strengthen and achieve high-speed and high-throughput wireless communications. . In recent years, it was observed that frequent detecting techniques could improve the performance (e.g., symbol error rate ‘SER’) of different modern digital communication systems. But these systems faced a problem of high complexity for the practical implementation. To solve the problem of high complexity, this work proposed Frequent Improve K-best Sphere Decoding (FIKSD) algorithm with stopping rule depending on the Manhattan metric. Manhattan metric is proposed to use with FIKSD in order to achieve the lowest complexity. FIKSD is a powerful tool to achieve a high performance close to the maximum likelihood (ML), with less complexity. The simulation results show a good reduction in computation complexity with a cost of slight performance degradation within 1dB; the proposed FIKSD requires 0% to 94% and 82% to 97% less complexity than Improved K-best Sphere Decoder (IKSD) and K-best Sphere Decoder (KSD) respectively. This makes the algorithm more suitable for implementation in wireless communication systems.
2 citations
TL;DR: In this paper, the complexity of inference with Relational Bayesian Networks as parameterized by their probability formulas was studied and it was shown that inference is pp-complete, displaying the same complexity as standard Bayesian networks (this is so even when the domain is succinctly specified in binary notation).
2 citations
TL;DR: For example, this paper proposed a dual-inheritance model for the identification of adaptive value of a trait or cultural practice in the context of cultural and dual inheritance models of evolution, which presents ambiguities not typically present in biological evolution.
Abstract: Cultural and dual-inheritance models of evolution present ambiguities not typically present in biological evolution. Criteria and the ability to specify the adaptive value of a trait or cultural practice become less clear. When niche construction is added, additional challenges and ambiguities arise. Its dynamic nature increases the difficulty of identifying adaptations, tracing the causal path between a trait and its function, and identifying the links between environmental demands and the development of adaptations.
2 citations
01 Dec 2008
TL;DR: This paper analyzes the computational complexity of set membership identification of a class of nonlinear systems consisting of the interconnection of a linear time invariant plant and a static nonlinearity and shows that, even in cases where a portion of the plant is known, the problem is generically NP-hard.
Abstract: This paper analyzes the computational complexity of set membership identification of a class of nonlinear systems consisting of the interconnection of a linear time invariant plant and a static nonlinearity. Its main result shows that, even in cases where a portion of the plant is known, the problem is generically NP-hard both in the number of experimental data points and in the number of inputs or outputs of the nonlinearity. These results provide new insight into the reasons underlying the high computational complexity of several recently proposed algorithms and point out to the need for developing computationally tractable relaxations.
2 citations
25 May 2013
TL;DR: A fast algorithm to solve the quadratic programming (QP) problem of MPC for systems with only input constraints is proposed and improves the efficiency of the fast gradient approach.
Abstract: Improving the computational efficiency for model predictive control (MPC) becomes the focus of recent researches. This paper proposes a fast algorithm to solve the quadratic programming (QP) problem of MPC for systems with only input constraints. This algorithm improves the efficiency of the fast gradient approach. Inspired by the multiplexed way, this algorithm searches a better solution of QP problem along the direction of one input channel in every iteration. Meanwhile, in order to further reduce the online computational complexity, the proposed algorithm applies the aggregation strategy to each input channel. Due to the aggregation strategy and the determined optimization direction, the proposed algorithm reduces the number of vector multiplications of each iteration, i.e. with less online computational complexity than previous works. Applying the proposed algorithm, simulations for a system with 3 inputs show that computational efficiency of this algorithm can reach a level of tens of microseconds in Matlab environment.
2 citations