External Memory algorithms play a key role in database management systems and large scale processing systems. External memory algorithms are typically tuned for efficient performance given a fixed, statically allocate...
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External Memory algorithms play a key role in database management systems and large scale processing systems. External memory algorithms are typically tuned for efficient performance given a fixed, statically allocated amount of internal memory. However, with the advent of real-time database system and database systems based upon administratively defined goals, algorithms must increasingly be able to adapt in an online manner when the amount of internal memory allocated to them changes dynamically and unpredictably. In this paper, we present a theoretical and applicable framework for memory-adaptive algorithms (or simply MA algorithms). We define the competitive worst-case notion of what it means for an MA algorithm to be dynamically optimal and prove fundamental lower bounds on the performance of MA algorithms for problems such as sorting, standard matrix multiplication, and several related problems. Our main tool for proving dynamic optimality is the notion of resource consumption, which measures how efficiently an MA algorithm adapts itself to memory fluctuations. We present the first dynamically optimal algorithm for sorting (based upon mergesort), permuting, FFT, permutation networks, buffer trees, (standard) matrix multiplication, and LU decomposition. In each case, dynamic optimality is demonstrated via a potential function argument showing that the algorithm's resource consumption is within a constant factor of optimal.
adaptive algorithms in general yield slow convergence rate while identifying systems with colored input. In this context, the adaptive Conjugate Gradient (ACG) algorithm shows fast convergence for colored input. Howev...
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This paper describes physical and digital design strategies for the Feedback-Actuated Augmented Bass – a self-contained feedback double bass with embedded DSP capabilities. A primary goal of the research project is t...
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The goal of this work is to improve the resource sharing and management level of higher mathematics teaching courses by proposing a higher mathematics teaching system based on the particle swarm optimisation algorithm...
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Subsymbolic approaches have been adopted in attempting to solve many AI problems. In order to find a near optimal solution to the problem a procedure is needed by which the subsymbolic components can be manipulated. I...
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Blind source separation attempts to recover unknown independent sources from a given set of observed mixtures. The two adaptive algorithms -EASI and RLS are introduced in this chapter, the separation simulation of a s...
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This paper provides insights into the practical applications of adaptive algorithms with optimum step sizes derived from Taylor Series expansion. Previously, this technique has been adopted for several applications, i...
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ISBN:
(纸本)9781617820663
This paper provides insights into the practical applications of adaptive algorithms with optimum step sizes derived from Taylor Series expansion. Previously, this technique has been adopted for several applications, including adaptive filtering and Independent Component Analysis (ICA). In addition, it is applicable in an extremely broad range of optimization problems. To achieve satisfying performance using this method, the proper set up for relevant algorithm parameters is crucial. Here we address three important issues, i.e., the shifting of data block between iterations, the approximation error due to the truncation of Taylor Series, and the use of prior knowledge of signal statistics. To facilitate presentation, two example algorithms are discussed, namely, the Optimum Block Adaptation with Individual adaptation parameters (OBAI) and Optimum Block Adaptation ICA (OBA-ICA).
The paper is developed in two parts where we discuss a new approach to self-organization in a single-layer linear feed-forward network. First, two novel algorithms for self-organization are derived from a two-layer li...
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ISBN:
(纸本)0262100657
The paper is developed in two parts where we discuss a new approach to self-organization in a single-layer linear feed-forward network. First, two novel algorithms for self-organization are derived from a two-layer linear hetero-associative network performing a one-of-m classification, and trained with the constrained least-mean-squared classification error criterion. Second, two adaptive algorithms are derived from these self-organizing procedures to compute the principal generalized eigenvectors of two correlation matrices from two sequences of random vectors. These novel adaptive algorithms can be implemented in a single-layer linear feed-forward network. We give a rigorous convergence analysis of the adaptive algorithms by using stochastic approximation theory. As an example, we consider a problem of online signal detection in digital mobile communications.
In this paper, we design and implement a k-multipath routing algorithm that allows a given source node send samples of data to a given sink node in a large scale sensor networks. Construction and dynamic selection of ...
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A new approach is introduced for formulating the near-field to far-field transformation of the scattered field. The scattered field is expanded as a series of summation in terms of modal wave functions. For the transf...
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A new approach is introduced for formulating the near-field to far-field transformation of the scattered field. The scattered field is expanded as a series of summation in terms of modal wave functions. For the transformation of the scattered field the expansion coefficients should be determined. In this work, we applied the adaptive solution algorithms for extracting unknown expansion coefficients, because of the application simplicity, noise exemption and computational efficiency. These unknown coefficients can be found by the minimization of the adaptive algorithm error using the limited limited number of measurement points around the object. Then, the scattered fields at the desired points can be calculated by using these determined coefficients. We choose the least-mean-square adaptation (LMS) algorithm and apply it to the scattered fields of different objects for noisy and noise free cases. We obtained that the adaptive algorithm transforms the scattered field with more accuracy and less computation time the harmonic expansion technique.
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