Showing papers on "Sequential algorithm published in 1983"
TL;DR: The increased computational speed of the introduced algorithm stems from an alternative definition of the so-called Kalman gain vector, which takes better advantage of the relationships between forward and backward linear prediction.
Abstract: A new computationally efficient algorithm for sequential least-squares (LS) estimation is presented in this paper. This fast a posteriori error sequential technique (FAEST) requires 5p MADPR (multiplications and divisions per recursion) for AR modeling and 7p MADPR for LS FIR filtering, where p is the number of estimated parameters. In contrast the well-known fast Kalman algorithm requires 8p MADPR for AR modeling and 10p MADPR for FIR filtering. The increased computational speed of the introduced algorithm stems from an alternative definition of the so-called Kalman gain vector, which takes better advantage of the relationships between forward and backward linear prediction.
276 citations
18 Jul 1983
TL;DR: In this paper, a parallel computer with k synchronized processors P1,...,Pk sharing a common random access storage, where simultaneous access to the same storage location by two or more processors is not allowed, is considered.
Abstract: Our model of computation is a parallel computer with k synchronized processors P1,...,Pk sharing a common random access storage, where simultaneous access to the same storage location by two or more processors is not allowed. Suppose a 2–3 tree T with n leaves is implemented in the storage, suppose a1,...,ak are data that may or may not be stored in the leaves, and for all i, 1⩽i⩽k, processor Pi knows ai. We show how to search for a1,..., ak in the tree T, how to insert these data into the tree and how to delete them from the tree in O(log n+log k) steps.
100 citations
TL;DR: It is shown that a two-dimensional processor array structure can be applied to the placement problem, resulting in a substantial reduction of the processing time.
Abstract: A parallel processing algorithm for logic module placement is presented. Conventionally, such placement problems have been solved on a single processor in a sequential manner. In this paper, it is shown that a two-dimensional processor array structure can be applied to the placement problem, resulting in a substantial reduction of the processing time. This parallel processing algorithm is based on the concept that the adjacent pairwise exchange method could be expanded to the parallel processing case. By using simulation programs, it is shown that the placement results obtained by the parallel processing algorithm are a little better than those obtained by the sequential algorithm. In addition, the theoretical estimations in respect to the processing cycle iterations correspond well with the simulation results.
28 citations
TL;DR: An algorithm is given for the sequential selection of N nodes (i.e., measurement points) for the uniform approximation (recovery) of convex functions over [0, 1]2, which has almost optimal order global error, (≦c1N−1lgN), over a naturally defined class of conveX functions.
Abstract: In this paper an algorithm is given for the sequential selection ofN nodes (i.e., measurement points) for the uniform approximation (recovery) of convex functions over [0, 1]2, which has almost optimal order global error, (źc1Nź1lgN), over a naturally defined class of convex functions. This shows the essential superiority of sequential algorithms for this class of approximation problems because any simultaneous choice ofN nodes leads to a global error >c0Nź1/2. New construction and estimation methods are presented, with possible (e.g., multidimensional) generalizations.
12 citations
TL;DR: The present solution to the nonlinear inversion problem consists of a new approach, whereby the unknown samples of the input are obtained from the given samples ofThe output by means of an efficient sequential algorithm.
6 citations
01 Nov 1983
TL;DR: The QR algorithm can be regarded as the best sequential algorithm available to date because it repeatedly applies a complicated similarity transformation to the result of the previous transformation, thereby producing a sequence of matrices that converges to a diagonal form.
Abstract: : For many signal and image processing applications, such as high resolution spectral estimation, image data compression etc., eigenvalue and singular value decompositions have emerged as extremely powerful and efficient computational tools. As far as the symmetric eigenvalue problem is concerned, QL and QR algorithms ... have emerged as the most effective way of finding all the eigenvalues of a small symmetric matrix. A full matrix is first reduced to tridiagonal form by a sequence of reflections and then the QL (QR) algorithm swiftly reduces the off diagonal elements until they are negligible. The algorithm repeatedly applies a complicated similarity transformation to the result of the previous transformation, thereby producing a sequence of matrices that converges to a diagonal form. What is more, the tridiagonal form is preserved. Therefore, the QR algorithm can be regarded as the best sequential algorithm available to date. The question is whether or not the QR algorithm may retain that same effectiveness when mapped into a parallel algorithm on a square or linear multiprocessor array. In this note, the authors offer an answer to this question using the computational wavefront notion.
4 citations
01 Oct 1983
TL;DR: A phased array adaptive antenna is discussed, where sequential digital processing is used instead of the conventional sampled control Applebaum algorithm, where the sequential algorithm converges to a stable solution for a sample rate smaller than that required for the conventional approach.
Abstract: Sequential digital processing in an adaptive system permits the use of common hardware elements and high speed pipeline configurations. These architectures are advantageous for applications where prime power, weight, volume and hardware complexity are to be minimized. This paper discusses a phased array adaptive antenna, where sequential digital processing is used instead of the conventional sampled control Applebaum algorithm. The sequential algorithm converges to a stable solution for a sample rate smaller than that required for the conventional approach. If a common digital processor is used, the number of operations during the sample interval is considerably reduced, however, the adaptation settling time is proportionally increased.