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doi: 10.3934/jimo.2021206
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Trace minimization method via penalty for linear response eigenvalue problems

 1 China National Clearing Center, Beijing 100048, China 2 School of Economics and Management, University of the Chinese Academy of Sciences, Beijing 100190, China 3 School of Applied Mathematics, Nanjing University of Finance & Economics, Nanjing 210023, China 4 Hefei 168 Middle School, Hefei 230031, China

*Corresponding author: Yuan Shen

Received  June 2021 Revised  August 2021 Early access December 2021

Fund Project: The second author is supported by the National Social Science Foundation of China under Grants 19AZD018, 20BGL028, 19BGL205, 17BTQ063

In various applications, such as the computation of energy excitation states of electrons and molecules, and the analysis of interstellar clouds, the linear response eigenvalue problem, which is a special type of the Hamiltonian eigenvalue problem, is frequently encountered. However, traditional eigensolvers may not be applicable to this problem owing to its inherently large scale. In fact, we are usually more interested in computing some of the smallest positive eigenvalues. To this end, a trace minimum principle optimization model with orthogonality constraint has been proposed. On this basis, we propose an unconstrained surrogate model called trace minimization via penalty, and we establish its equivalence with the original constrained model, provided that the penalty parameter is larger than a certain threshold. By avoiding the orthogonality constraint, we can use a gradient-type method to solve this model. Specifically, we use the gradient descent method with Barzilai–Borwein step size. Moreover, we develop a restarting strategy for the proposed algorithm whereby higher accuracy and faster convergence can be achieved. This is verified by preliminary experimental results.

Citation: Yadan Chen, Yuan Shen, Shanshan Liu. Trace minimization method via penalty for linear response eigenvalue problems. Journal of Industrial & Management Optimization, doi: 10.3934/jimo.2021206
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References:
Normalized residual norm vs iteration with $k = 5$ (upper left: $n = 1000$; upper right: $n = 2000$; lower left: $n = 4000$; lower right: $n = 6000$)
Normalized residual norm vs iteration with $k = 50$ (upper left: $n = 1000$; upper right: $n = 2000$; lower left: $n = 4000$; lower right: $n = 6000$)
Frobenius norm of gradient vs iteration with $k = 5$ (upper left: $n = 1000$; upper right: $n = 2000$; lower left: $n = 4000$; lower right: $n = 6000$)
Frobenius norm of gradient vs iteration with $k = 50$ (upper left: $n = 1000$; upper right: $n = 2000$; lower left: $n = 4000$; lower right: $n = 6000$)
Normalized residual norm via iteration with $k = 5$ (left: n = 1000; middle: n = 2000; right: n = 4000)
Frobenius norm of gradient via iteration with $k = 5$ (left: n = 1000; middle: n = 2000; right: n = 4000)
Numerical results on random problems
 ($n, k$) LOBP4dCG Bcheb-dav Alg. 1 NRN time NRN time NRN time (1000, 5) 3.374e-7 5.158 4.370e-7 1.213 9.953e-7 1.391 (2000, 5) 2.792e-6 16.156 5.319e-7 5.425 9.973e-7 2.529 (4000, 5) 2.052e-7 46.707 7.597e-7 29.476 9.355e-7 4.848 (6000, 5) 2.964e-7 219.598 7.230e-7 127.189 6.736e-7 17.503
 ($n, k$) LOBP4dCG Bcheb-dav Alg. 1 NRN time NRN time NRN time (1000, 5) 3.374e-7 5.158 4.370e-7 1.213 9.953e-7 1.391 (2000, 5) 2.792e-6 16.156 5.319e-7 5.425 9.973e-7 2.529 (4000, 5) 2.052e-7 46.707 7.597e-7 29.476 9.355e-7 4.848 (6000, 5) 2.964e-7 219.598 7.230e-7 127.189 6.736e-7 17.503
Numerical results on random problems
 ($n, k$) Bcheb-dav Alg. 1 NRN time NRN time (1000, 50) 8.179e-7 1.924 6.617e-6 5.286 (2000, 50) 1.351e-7 10.493 9.445e-7 27.038 (4000, 50) 1.713e-7 45.975 9.239e-7 55.971 (6000, 50) 4.060e-7 237.142 9.587e-7 189.574
 ($n, k$) Bcheb-dav Alg. 1 NRN time NRN time (1000, 50) 8.179e-7 1.924 6.617e-6 5.286 (2000, 50) 1.351e-7 10.493 9.445e-7 27.038 (4000, 50) 1.713e-7 45.975 9.239e-7 55.971 (6000, 50) 4.060e-7 237.142 9.587e-7 189.574
Numerical results on random problems with smaller $k$
 ($n, k$) LOBP4dCG Bcheb-dav Alg. 1 NRN time NRN time NRN time (1000, 2) 9.353e-10 1.034 2.347e-7 1.920 8.977e-7 0.522 (2000, 2) 2.639e-9 4.906 8.093e-7 5.481 9.122e-7 0.928 (4000, 2) 1.283e-9 21.372 9.518e-7 35.761 9.729e-7 1.159 (6000, 2) 2.050e-9 59.513 9.235e-7 218.249 6.868e-7 5.190
 ($n, k$) LOBP4dCG Bcheb-dav Alg. 1 NRN time NRN time NRN time (1000, 2) 9.353e-10 1.034 2.347e-7 1.920 8.977e-7 0.522 (2000, 2) 2.639e-9 4.906 8.093e-7 5.481 9.122e-7 0.928 (4000, 2) 1.283e-9 21.372 9.518e-7 35.761 9.729e-7 1.159 (6000, 2) 2.050e-9 59.513 9.235e-7 218.249 6.868e-7 5.190
Numerical results on random problems with larger $k$
 ($n, k$) Bcheb-dav Alg. 1 NRN time NRN time (500, 50) 1.721e-8 0.572 9.607e-6 2.462 (750, 75) 2.133e-8 1.740 9.994e-6 6.769 (1000, 100) 1.299e-8 2.945 9.902e-6 16.409 (1500, 150) 4.218e-8 10.376 9.999e-6 24.900 (2000, 200) 2.782e-8 19.507 7.717e-6 64.872 ($n, k$) Bcheb-dav Alg. 1 NRN time NRN time (500, 100) 1.713e-8 1.557 9.989e-6 7.355 (750, 150) 1.606e-8 2.310 9.996e-6 20.053 (1000, 200) 2.837e-8 6.128 9.993e-6 46.477 (1500, 300) 2.647e-8 15.524 9.815e-6 85.150 (2000, 400) 1.692e-8 28.886 4.970e-6 415.430
 ($n, k$) Bcheb-dav Alg. 1 NRN time NRN time (500, 50) 1.721e-8 0.572 9.607e-6 2.462 (750, 75) 2.133e-8 1.740 9.994e-6 6.769 (1000, 100) 1.299e-8 2.945 9.902e-6 16.409 (1500, 150) 4.218e-8 10.376 9.999e-6 24.900 (2000, 200) 2.782e-8 19.507 7.717e-6 64.872 ($n, k$) Bcheb-dav Alg. 1 NRN time NRN time (500, 100) 1.713e-8 1.557 9.989e-6 7.355 (750, 150) 1.606e-8 2.310 9.996e-6 20.053 (1000, 200) 2.837e-8 6.128 9.993e-6 46.477 (1500, 300) 2.647e-8 15.524 9.815e-6 85.150 (2000, 400) 1.692e-8 28.886 4.970e-6 415.430
Numerical results on random problems ($tol = 10^{-9}$)
 ($n, k$) LOBP4dCG Bcheb-dav Alg. 2 NRN time NRN time NRN time (1000, 5) 1.741e-6 4.556 5.682e-10 1.641 1.969e-8 29.810 (2000, 5) 5.263e-8 13.573 6.082e-9 6.988 9.740e-10 80.980 (4000, 5) 3.956e-8 77.154 6.694e-9 44.313 9.257e-10 315.360
 ($n, k$) LOBP4dCG Bcheb-dav Alg. 2 NRN time NRN time NRN time (1000, 5) 1.741e-6 4.556 5.682e-10 1.641 1.969e-8 29.810 (2000, 5) 5.263e-8 13.573 6.082e-9 6.988 9.740e-10 80.980 (4000, 5) 3.956e-8 77.154 6.694e-9 44.313 9.257e-10 315.360

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