R. L. Burden and J. D. Faires, editors. Numerical Analysis, 5th Edition. PWS Publishing Company, New York, NY, 1993. Home/Search Document Not in Database Summary Related Articles Check |
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Computational Field Theory and Pattern Formation - Toral (Correct)
....the above integral can not be computed by analytical methods (which happens more often than desired) one has to resource to numerical methods. One must realize, however, that a simple extension of the standard methods used in small N numerical integration (Simpson rules, Gauss integration, etc. (Faires et al. 1993)) to compute numerically the value of the integral will fail for very high dimensional integrals. For instance, let us take N = 100 (and this is a rather small number for typical applications) and suppose we want to use Simpson methods. We need to generate a grid in the X space and sum up all the ....
Faires, J. and Burden, R. (1993): Numerical Methods, PWS Publishing Company, Boston.
Extending Interactive Graphical Applications with Constraints - Badros (2000) (1 citation) (Correct)
....(reducing the number of constraints and unknowns before passing them along to the NewtonRhapson solver) Newton Rhapson converges quadratically (faster than gradient descent) but relies on a sufficiently accurate initial guess and an invertible Jacobian. 8 The Levenberg Marquardt method [25] dynamically weights a combination of Newton Rhapson and gradient descent, permitting solvers to exploit the faster convergence of Newton Rhapson once in the proximity of a local minimum; this hybrid solver is used in maintaining the constraints in the Chimera editor [92, 93] Besides being ....
Richard L. Burden and J. Douglas Faires. Numerical Analysis. PWS Publishing Company, Boston, Massachusetts, fifth edition, 1985.
Efficient Sparse Gaussian Elimination with Lazy Space Allocation - Jiang (1999) (Correct)
....by the widely used Message Passing Interface(MPI) Since MPI code can be run under almost all parallel system, our software can be applicable to all machines where MPI library is installed. Generally speaking, sparse Gaussian elimination consists of three steps: analyze, factorize and solve [2, 10], compared to the dense elimination which has only the last two steps. The concepts of factorize and solve of the sparse Gaussian elimination are the same as those of the dense elimination, whereas there exist some minor differences between them. The rest of this paper is organized as follows. ....
Richard L. Burden and J. Douglas Faires. Numerical Analysis. PWS Publishing Company, fifth edition, 1993.
Similarity Search in Time Series Data Sets - Xia (1997) (4 citations) (Correct)
....Any Figure 5.5: A concept hierarchy of the time dimension: a) with the same numbers of siblings. b) According to the current time level and the chop length scheme in Table 5.2, get the corresponding chop length N . 2. To represent each bunch of data of length N , using the least square method [7] to get the best linear approximating line ax b for each bunch of data. 3. Represent each bunch of data by a corresponding symbol. a) Get the slope value of each approximating line. b) Use the corresponding symbol to represent each approximating line according to the shape definition table ....
....is the most convenient procedure for determining best linear approximation, and it is a good method from the theoretical point of view. It puts substantially more weight on a point that is out of line with the rest of data but will not allow that point to completely dominate the approximation [7]. There are also other methods to get the linear approximation, such as minimax approach and absolute deviation approach, etc. It is testified that they are not as good as the least squares method. 4. We divide the ( Gamma 2 ; 2 ) space into a number of non overlapping intervals, each ....
R. Burden and J. Faires. Numerical Analysis, 5 ed. PWS Publishing Company, 1993.
Constraints in Interactive Graphical Applications - Badros (1998) (Correct)
....(reducing the number of constraints and unknowns before passing them along to the Newton Rhapson solver) Newton Rhapson converges quadratically (faster than gradient descent) but relies on a su#ciently accurate initial guess and an invertible Jacobian. 14 The Levenberg Marquardt method [BF85] dynamically weights a combination of Newton Rhapson and gradient descent, permitting solvers to exploit the faster convergence of Newton Rhapson once in the proximity of a local minimum; this hybrid solver is used in maintaining the constraints in the Chimera editor [KF92] Besides being ....
Richard L. Burden and J. Douglas Faires. Numerical Analysis. PWS Publishing Company, Boston, Massachusetts, fifth edition, 1985.
Run-time Techniques for Exploiting Irregular Task Parallelism on.. - Cong Fu (1997) (6 citations) (Correct)
....in sparse Cholesky factorization. usually make the first row of matrices strictly diagonal dominant. For such matrices, it can be shown [6] that stable solutions can be obtained by LU (Gaussian Elimination) without pivoting. The proof in [6] is based on a modification to Theorem 6. 19 in [5]. It should be noted that parallel sparse LU with global pivoting is more complicated. for k = 1 to N F kk : Factorize A kk as L k U k for i = k 1 to N S ik : A ik = A ik U Gamma1 k S ki : A ki = L Gamma1 k A ki end for j = k 1 to N for i = k 1 to N M k ij : A ij = A ij ....
R. Burden and J. Faires. Numerical Analysis. PWS Publishing Company, fifth edition, 1993.
Periodic Pattern Search on Time-Related Data Sets - Gong (1997) (5 citations) (Correct)
....of the time series instead of the values, some preprocessing is required to convert the value based time series to trend based. There are ways of estimating the trend of a curve interval, a few of which are introduced in [37] The method we adopt in our approach is the linear least squares method [10]. The basic idea of this method is to find a best fitting curve among all curves approximating the set of data points within some interval of a time series. We call it the best fitting curve in the sense that the sum of the squares of the distances between the data points and the approximating ....
R. Burden and J. Faires. Numerical Analysis, 5 ed. PWS Publishing Company, 1993.
Run-time Techniques for Exploiting Irregular Task Parallelism on.. - Cong Fu (1997) (6 citations) (Correct)
....dominant. The boundary conditions usually make the first row of matrices strictly diagonal dominant. For such matrices, it can be shown [Cai95] that stable solutions can be obtained by LU (Gaussian Elimination) without pivoting. The proof in [Cai95] is based on a modification to Theorem 6. 19 in [BF93] It should be noted that parallel sparse LU with global pivoting is more complicated. for k = 1 to N F kk : Factorize A k;k as L k U k for i = k 1 to N S ik : A i;k = A i;k U Gamma1 k S ki : A k;i = L Gamma1 k A k;i end for j = k 1 to N for i = k 1 to N M k ij : A i;j = A ....
Richard L. Burden and J. Douglas Faires. Numerical Analysis. PWS Publishing Company, fifth edition, 1993.
The Anatomy of a Multi-Camera Video Surveillance System - Jiao, Wu, Wu, Chang, Wang (Correct)
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R. L. Burden and J. D. Faires, editors. Numerical Analysis, 5th Edition. PWS Publishing Company, New York, NY, 1993.
Simulation Models, and Processing Techniques for a Sliding.. - Newhall (1997) (Correct)
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Burden R.L., Faires J.D., Numerical Analysis, 5 ed., PWS Publishing Company, 1993.
On Grids And Solutions From Residual Minimization - Nishikawa (Correct)
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R. L. Burden and J. D. Faires. Numerical Analysis. PWS Publishing Company, 1992.
Parallel Sparse Gaussian Elimination with Partial Pivoting and 2-D .. - Jiao (1997) (2 citations) (Correct)
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Richard L. Burden and J. Douglas Faires. Numerical Analysis. PWS Publishing Company, fifth edition, 1993.
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