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RKPACKA numerical package for solving large eigenproblems
Che-Rung Lee
University of Maryland, College Park 2112/04/19
Outline
Introduction RKPACK Experiments Conclusion
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Introduction
The residual Krylov method Shift-invert enhancement Properties and examples
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The residual Krylov method
Basic algorithm1. Let be a selected eigenpair
approximation of A.
2. Compute the residual .
3. Use r in subspace expansion.
),( z
zAzr
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Properties
The selected approximation (candidate) can converge even with errors.The allowed error || f || must be less than ||
r||, for a constant <1. The residual Krylov method can work with
an initial subspace that contains good Ritz approximations.
University of Maryland, College Park 6112/04/19
Example
A 100x100 matrix with eigenvalues 1, 0.95, …,0.9599.
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Shift-invert enhancement
Algorithm: (shift value = )1. Let be a selected eigenpair
approximation of A.
2. Compute the residual .
3. Solve the equation .
4. Use v in subspace expansion. Equation in step 3 can be solved in low
accuracy, such as 103.
),( z
zAzr rvIA )(
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Example
The same matrix
Shift value is 1.3
Linear systems are solved to 10 3.
0 5 10 15 20 25 30 35 40
100
10-5
10-10
10-15
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RKPACK
Features Computation modes Memory requirement Time complexity
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Features
Can compute several selected eigenpairs Allow imprecise computational results with
shift-invert enhancement Can start with an appropriate initial subspa
ce Use the Krylov-Schur restarting algorithm Use reverse communication
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Computation modes
Two computation modesThe normal mode:
needs matrix vector multiplication only
The imprecise shift-invert mode: needs matrix vector multiplication and linear
system solving (with low accuracy requirement) can change the shift value
Both can be initialized with a subspace.
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Memory requirement
Use the Krylov-Schur restarting algorithm to control the maximum dimension of subspace
Required memory: O(nm)+O(m2)n: the order of matrix Am: the maximum dimension of subspace
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Time complexity
The normal mode:kf (n)+kO(nm)+kO(m3)
f (n): the time for matrix vector multiplication. k: the number of iterations
The imprecise shift-invert modekf (n) + kO(nm) + kO(m3) + kg(n, )
g(n, ) : the time for solving linear system to the precision .
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Experiments
Test problem Performance of RKPACK The inexact residual Krylov method The successive inner-outer process
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Test problem
Let A be a 1000010000 matrix with first 100 eigenvalues 1, 0.95, …, 0.9599, and the rest randomly distributed in (0.25, 0.75).
Eigenvectors are randomly generated. Maximum dimension of subspace is 20. Stopping criterion: when the norm of
residual is smaller than 1013.
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Performance of the normal mode
Compute six dominant eigenpairs. Compare to the mode 1 of ARPACK
Etime: elapse time (second)MVM: number of matrix vector multiplications Iteration: number of subspace expansions
ARPACK RKPACK
Etime 25.93 24.41
MVM (Iteration) 117 142
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The imprecise shift-invert mode
Compute six smallest eigenvalues.Use GMRES to solve linear system. (shift = 0)
Compare to the mode 3 of ARPACKPrec: precision requirement of solution
ARPACK RKPACK
Iteration 68 153
ETime 4246.77 623.46
MVM 14552 4932
Prec 1013 103
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Inexact residual Krylov method
Allow increasing errors in the computation Use the normal mode with matrix A1.
The required precision of solving A1.
is the desired precision of computed eigenpairs m is the maximum dimension of subspace
rm
University of Maryland, College Park 19112/04/19
Experiment and result
Compute six smallest eigenpairs. The required precision (using GMRES)
Etime: 910.11 second MVM: 6282 Iteration: 67
r20
10 13
20 30 40 50 60 70 80
10-2
10-4
10-6
10-8
10-10
University of Maryland, College Park 20112/04/19
Successive inner-outer process
Use the convergence properties of Krylov subspace (superlinear) to minimize total number of MVM. (Golub, Zhang and Zha, 2000)
Divide the process into stages, with increasing precision requirement.
The original algorithm can only compute a single eigenpair
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Experiment and result
Compute six smallest eigenpairs. Four stages with required precision (GMRES)
103,106,109,1012.
Etime : 1188.12 MVM : 13307 Iteration : 163
20 40 60 80 100 120 140 160
100
10-2
10-4
10-6
10-8
10-10
10-12
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Conclusion
Summary Future work
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Summary
The residual Krylov method for eigenproblems allows errors in the computation, and can work on an appropriate initial subspace.
RKPACK can solve eigenproblems rapidly when uses the imprecise shift-invert enhancement, and is able to integrate other algorithms easily.
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Future work
ParallelizationData parallelism
Block version of the residual Krylov method Other eigenvector approximations
Refine Ritz vector or Harmonic Ritz vector New algorithms
Inexact methods, residual power method …
…