Although it is acknowledged that multi-way dataflow constraints are useful in interactive applications, concerns about their tractability have hindered their acceptance. Certain local propagation algorithms that solve...
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ISBN:
(纸本)1558604804
Although it is acknowledged that multi-way dataflow constraints are useful in interactive applications, concerns about their tractability have hindered their acceptance. Certain local propagation algorithms that solve these constraints are polynomial, others (such as SkyBlue) are exponential, Every system handles a specific problem and the influence of any particular restriction on the computational complexity is not yet precisely determined. In this paper, we present three theoretical results that allow us to classify existing multi-way constraint problems. Especially, we prove that the problem handled by SkyBlue is NP-hard.
Some questions such as: Is it convenient to simulate channel realizations with sum of sinusoids or with the filtering approach? are addressed in this work in order to provide a guide on which algorithms and in what si...
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ISBN:
(纸本)0780379543
Some questions such as: Is it convenient to simulate channel realizations with sum of sinusoids or with the filtering approach? are addressed in this work in order to provide a guide on which algorithms and in what situations should be used in the development of channel simulators. The conclusions presented are gathered from the comparison of the methods proposed in the literature to simulate narrowband and wideband separable channels. The comparisons are performed from the point of view of computational complexity and two metrics of approximation error. We also discuss the properties inherent to the algorithms that could determine if the proposals are suitable for channel simulation, such as synthesis complexity and their capability to approximate the conditions encountered in real channels.
Edge modification problems in graphs have a lot of applications in different areas. Polynomial time algorithms and NP-completeness results appear in several works in the literature. In this paper, we prove new complex...
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In this brief we investigate the computational complexity of a non-uniform lapped orthogonal filterbank with time domain aliasing reduction. The computational complexity of such filterbank is crucial for its usability...
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ISBN:
(纸本)9781942220268
In this brief we investigate the computational complexity of a non-uniform lapped orthogonal filterbank with time domain aliasing reduction. The computational complexity of such filterbank is crucial for its usability in realtime systems, as well as in embedded, and mobile devices. Due to the signal-adaptive nature of the filterbank, the actual real-world complexity will be situated between two theoretical bounds and has to be estimated experimentally by processing real-world signals using a coder-decoder pipeline. Both the bounds and the real-world complexity were analyzed in this brief, and a median 14-22% increase in complexity over an adaptive uniform MDCT filterbank was found.
The paper is concerned with analyzing and comparing two exact algorithms from the viewpoint of computational complexity. Both serve for calculating fault-detection tests of digital circuits. The first one is the so-ca...
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ISBN:
(纸本)9781479982004
The paper is concerned with analyzing and comparing two exact algorithms from the viewpoint of computational complexity. Both serve for calculating fault-detection tests of digital circuits. The first one is the so-called composite justification, and the second is the Dalgorithm. The analysis will be performed on combinational logic networks at the gate level. Here single and multiple stuck-at logic faults will be considered. As a result, it is pointed out that the composite justification requires significantly less computational step than the D-algorithm and its modifications. The difference manifests itself especially in terms of multiple faults. From this fact it has been conjectured that possibly no other algorithm is available in this field with fewer computational steps. If the claim holds, then it follows directly that the test-calculation problem is of exponential time, and so are all the other NP-complete problems. It may also be expected that the minimal complexity of composite justification applies to any modeling level (either low or high) of digital circuits, just like the exponential-time solution.
Minimizing the binary classification error with a linear model leads to an NP-hard problem. In practice, surrogate loss functions are used, in particular loss functions leading to large margin classification such as t...
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Minimizing the binary classification error with a linear model leads to an NP-hard problem. In practice, surrogate loss functions are used, in particular loss functions leading to large margin classification such as the hinge loss and the ramp loss. The intuitive large margin concept is theoretically supported by generalization bounds linking the expected classification error to the empirical margin error and the complexity of the considered hypotheses class. This article addresses the fundamental question about the computational complexity of determining whether there is a hypotheses class with a hypothesis such that the upper bound on the generalization error is below a certain value. Results of this type are important for model comparison and selection. This paper takes a first step and proves that minimizing a basic margin-bound is NP-hard when considering linear hypotheses and the rho-margin loss function, which generalizes the ramp loss. This result directly implies the hardness of ramp loss minimization.
Summary form only given. The performance of software-only video codecs is often constrained by available processing power. Existing fast motion estimation algorithms are not designed to provide flexible, predictable c...
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The deployment of machine learning models on devices with limited memory and processing capabilities demands interdisciplinary expertise in the areas of data science, algorithms and computer architecture. We present a...
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ISBN:
(纸本)9798350349467;9798350349450
The deployment of machine learning models on devices with limited memory and processing capabilities demands interdisciplinary expertise in the areas of data science, algorithms and computer architecture. We present a thorough analysis for inference optimization in linear layers, which are fundamental in many models and can induce computational bottlenecks. The considerations are detached from the executing hardware, which allows for a detailed understanding of the computations involved on an arithmetic level. We conclude with appropriate metrics and expressions, which are derived based on concepts of computational complexity, and provide quantitative or implicit measures of the linear layer's performance and resource usage. The framework is systematically applied to various optimization methods, each offering a different approach to reduce the computational complexity of linear layers and thus the overall model, and can therefore be crucial for deployment on resource limited devices.
In this paper we prove that the general avalanche problem AP is in NC for the Kadanoff sandpile model in one dimension, answering an open problem of [2]. Thus adding one more item to the (slowly) growing list of dimen...
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ISBN:
(纸本)9783319188126;9783319188119
In this paper we prove that the general avalanche problem AP is in NC for the Kadanoff sandpile model in one dimension, answering an open problem of [2]. Thus adding one more item to the (slowly) growing list of dimension sensitive problems since in higher dimensions the problem is P-complete (for monotone sandpiles).
We study the computational complexity of several decision and optimization control problems arising in partially observed discrete event systems. These problems are related to the state avoidance problem where one mus...
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ISBN:
(纸本)9781424447060
We study the computational complexity of several decision and optimization control problems arising in partially observed discrete event systems. These problems are related to the state avoidance problem where one must compute a controller which prevents the system from accessing a set of bad states and which is maximal for a de ned criterion, based on inclusion of the set of states remaining reachable after the control. We focus our study on memoryless controllers.
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