• Previous Article
    Formal analysis of the Schulz matrix inversion algorithm: A paradigm towards computer aided verification of general matrix flow solvers
  • NACO Home
  • This Issue
  • Next Article
    Quasilinear iterative method for the boundary value problem of nonlinear fractional differential equation
June  2020, 10(2): 165-175. doi: 10.3934/naco.2019046

Quadratic optimization with two ball constraints

Department of Applied Mathematics, Faculty of Mathematical Sciences, University of Guilan, Rasht, Iran

* Corresponding author: Maziar Salahi

Received  December 2018 Revised  July 2019 Published  September 2019

Fund Project: The authors are grateful to the reviewers for their comments and suggestions and University of Guilan for partially supporting this research

In this paper, the minimization of a general quadratic function subject to two ball constraints, called two ball trust-region subproblem (TBTRS), is studied. It is shown that the global optimal solution can be found by solving two extended trust-region subproblems. Strong duality conditions for two special cases are discussed. Finally, a comparison of results of the new algorithm with the other two recently proposed algorithms and CVX software are presented for several classes of randomly generated test problems.

Citation: Saeid Ansary Karbasy, Maziar Salahi. Quadratic optimization with two ball constraints. Numerical Algebra, Control & Optimization, 2020, 10 (2) : 165-175. doi: 10.3934/naco.2019046
References:
[1]

S. AdachiS. IwataY. Nakatsukasa and A. Takeda, Solving the trust-region subproblem by a generalized eigenvalue problem, SIAM Journal on Optimization, 27 (2017), 269-291.  doi: 10.1137/16M1058200.  Google Scholar

[2]

S. Ansary Karbasy and M. Salahi, A hybrid algorithm for the two-trust-region subproblem, Computational and Applied Mathematics, https://doi.org/10.1007/s40314-019-0864-y doi: 10.1007/s40314-019-0864-y.  Google Scholar

[3]

H. T. Banks and K. Kunisch, Estimation Techniques for Distributed Parameter Systems, Springer Science and Business Media, 2012. doi: 10.1007/978-1-4612-3700-6.  Google Scholar

[4]

A. Beck and Y. C. Eldar, Strong duality in nonconvex quadratic optimization with two quadratic constraints, SIAM Journal on Optimization, 17 (2006), 844-860.  doi: 10.1137/050644471.  Google Scholar

[5]

Im. Bomze and Ml. Overton, Narrowing the difficulty gap for the Celis-Dennis-Tapia problem, Mathematical Programming, 151 (2015), 459-476.  doi: 10.1007/s10107-014-0836-3.  Google Scholar

[6]

S. Burer and K. M. Anstreicher, Second-order-cone constraints for extended trust-region subproblems, SIAM Journal on Optimization, 23 (2013), 432-451.  doi: 10.1137/110826862.  Google Scholar

[7]

S. Burer and B. Yang, The trust-region subproblem with non-intersecting linear constraints, Mathematical Programming, 149 (2015), 253-264.  doi: 10.1007/s10107-014-0749-1.  Google Scholar

[8]

M. R. Celis, J. E. Dennis and R. A. Tapia, A trust-region strategy for nonlinear equality constrained optimization, Numerical Optimization, (1984), 71–82.  Google Scholar

[9]

A. R. Conn, N. I. Gould and P. L. Toint, Trust-Region Methods, SIAM Philadelphia, Vol: 1, 2000. doi: 10.1137/1.9780898719857.  Google Scholar

[10]

J. E. Dennis and R. B. Schnabel, Numerical Methods for Unconstrained Optimization and Nonlinear Equations, SIAM publisher, Vol: 16, 1996. doi: 10.1137/1.9781611971200.  Google Scholar

[11]

S. Fallahi, M. Salahi and S. Ansary Karbasy, On SOCP/SDP formulation of the extended trust-region subproblem, to appear in Iranian Journal of Operations Research, 2019. doi: 10.1007/s40314-019-0864-y.  Google Scholar

[12]

M. Grant and S. Boyd, CVX: Matlab software for disciplined convex programming, version 2.1, March 2017. Available from: http://cvxr.com/cvx. Google Scholar

[13]

S. GugercinA. C. Antoulas and H. P. Zhang, An approach to identification for robust control, IEEE Transactions on Automatic Control, 48 (2003), 1109-1115.  doi: 10.1109/TAC.2003.812821.  Google Scholar

[14]

M. Heinkenschloss, Mesh independence for nonlinear least squares problems with norm constraints, SlAM Journal on Optimization, 3 (1993), 81-117. doi: 10.1137/0803005.  Google Scholar

[15]

M. Heinkenschloss, On the solution of a two ball trust-region subproblem, Mathematical Programming, 64 (1994), 249-276.  doi: 10.1007/BF01582576.  Google Scholar

[16]

V. Jeyakumar and G. Y. Li, Trust-region problems with linear inequality constraints: Exact SDP relaxation, global optimality and robust optimization, Mathematical Programming, 147 (2014), 171-206.  doi: 10.1007/s10107-013-0716-2.  Google Scholar

[17]

B. KaltenbacherF. Rendl and E. Resmerita, Computing quasisolutions of nonlinear inverse problems via efficient minimization of trust region problems, Journal of Inverse and Ill-Posed Problems, 24 (2016), 435-447.  doi: 10.1515/jiip-2015-0087.  Google Scholar

[18]

C. Kravaris and J. H. Seinfeld, Identification of parameters in distributed parameter systems by regularization, SIAM Journal on Control and Optimization, 23 (1985), 217-241.  doi: 10.1137/0323017.  Google Scholar

[19]

G. Kristensson, Inverse problems for acoustic waves using the penalised likelihood method, Inverse Problems, 2 (1986), 461.  Google Scholar

[20]

J. M. Martínez, Local minimizers of quadratic functions on Euclidean balls and spheres, SIAM Journal on Optimization, 4 (1994), 159-176.  doi: 10.1137/0804009.  Google Scholar

[21]

Y. Nesterov, H. Wolkowicz and Y. Ye, Semidefinite Programming Relaxations of Nonconvex Quadratic Optimization, Handbook of Semidefinite Programming, Springer, Boston, (2000), 361–419. doi: 10.1007/978-1-4615-4381-7_13.  Google Scholar

[22] S. Omatu and J. H. Seinfeld, Distributed Parameter Systems: Theory and Applications,, Clarendon Press, 1989.   Google Scholar
[23]

F. O'Sullivan and G. Wahba, A cross validated Bayesian retrieval algorithm for nonlinear remote sensing experiments, Journal of Computational Physics, 59 (1985), 441-455.   Google Scholar

[24]

J.-M. Peng and Y. Yuan, Optimality conditions for the minimization of a quadratic with two quadratic constraints, SIAM Journal on Optimization, 7 (1997), 579-594.  doi: 10.1137/S1052623494261520.  Google Scholar

[25]

S. SakaueY. NakatsukasaA. Takeda and S. Iwata, Solving generalized CDT problems via two-parameter eigenvalues, SIAM Journal on Optimization, 26 (2016), 1669-1694.  doi: 10.1137/15100624X.  Google Scholar

[26]

M. Salahi and A. Taati, A fast eigenvalue approach for solving the trust-region subproblem with an additional linear inequality, Computational and Applied Mathematics, 37 (2018), 329-347.  doi: 10.1007/s40314-016-0347-3.  Google Scholar

[27]

M. SalahiA. Taati and H. Wolkowicz, Local nonglobal minima for solving large scale extended trust-region subproblems, Computational Optimization and Applications, 66 (2016), 223-244.  doi: 10.1007/s10589-016-9867-4.  Google Scholar

[28]

M. Salah and S. Fallahi, Trust-region subproblem with an additional linear inequality constraint, Optimization Letters, 10 (2016), 821-832.  doi: 10.1007/s11590-015-0957-5.  Google Scholar

[29]

J. F. Sturm and S. Zhang, On cones of nonnegative quadratic functions, Mathematics of Operations Research, 28 (2003), 246-267.  doi: 10.1287/moor.28.2.246.14485.  Google Scholar

[30]

C. R. Vogel, A constrained least squares regularization method for nonlinear iii-posed problems, SIAM Journal on Control and Optimization, 28 (1990), 34-49.  doi: 10.1137/0328002.  Google Scholar

[31]

A. Zhang and S. Hayashi, Celis-Dennis-Tapia based approach to quadratic fractional programming problems with two quadratic constraints, Numerical Algebra, Control and Optimization, 1 (2011), 83-98.  doi: 10.3934/naco.2011.1.83.  Google Scholar

show all references

References:
[1]

S. AdachiS. IwataY. Nakatsukasa and A. Takeda, Solving the trust-region subproblem by a generalized eigenvalue problem, SIAM Journal on Optimization, 27 (2017), 269-291.  doi: 10.1137/16M1058200.  Google Scholar

[2]

S. Ansary Karbasy and M. Salahi, A hybrid algorithm for the two-trust-region subproblem, Computational and Applied Mathematics, https://doi.org/10.1007/s40314-019-0864-y doi: 10.1007/s40314-019-0864-y.  Google Scholar

[3]

H. T. Banks and K. Kunisch, Estimation Techniques for Distributed Parameter Systems, Springer Science and Business Media, 2012. doi: 10.1007/978-1-4612-3700-6.  Google Scholar

[4]

A. Beck and Y. C. Eldar, Strong duality in nonconvex quadratic optimization with two quadratic constraints, SIAM Journal on Optimization, 17 (2006), 844-860.  doi: 10.1137/050644471.  Google Scholar

[5]

Im. Bomze and Ml. Overton, Narrowing the difficulty gap for the Celis-Dennis-Tapia problem, Mathematical Programming, 151 (2015), 459-476.  doi: 10.1007/s10107-014-0836-3.  Google Scholar

[6]

S. Burer and K. M. Anstreicher, Second-order-cone constraints for extended trust-region subproblems, SIAM Journal on Optimization, 23 (2013), 432-451.  doi: 10.1137/110826862.  Google Scholar

[7]

S. Burer and B. Yang, The trust-region subproblem with non-intersecting linear constraints, Mathematical Programming, 149 (2015), 253-264.  doi: 10.1007/s10107-014-0749-1.  Google Scholar

[8]

M. R. Celis, J. E. Dennis and R. A. Tapia, A trust-region strategy for nonlinear equality constrained optimization, Numerical Optimization, (1984), 71–82.  Google Scholar

[9]

A. R. Conn, N. I. Gould and P. L. Toint, Trust-Region Methods, SIAM Philadelphia, Vol: 1, 2000. doi: 10.1137/1.9780898719857.  Google Scholar

[10]

J. E. Dennis and R. B. Schnabel, Numerical Methods for Unconstrained Optimization and Nonlinear Equations, SIAM publisher, Vol: 16, 1996. doi: 10.1137/1.9781611971200.  Google Scholar

[11]

S. Fallahi, M. Salahi and S. Ansary Karbasy, On SOCP/SDP formulation of the extended trust-region subproblem, to appear in Iranian Journal of Operations Research, 2019. doi: 10.1007/s40314-019-0864-y.  Google Scholar

[12]

M. Grant and S. Boyd, CVX: Matlab software for disciplined convex programming, version 2.1, March 2017. Available from: http://cvxr.com/cvx. Google Scholar

[13]

S. GugercinA. C. Antoulas and H. P. Zhang, An approach to identification for robust control, IEEE Transactions on Automatic Control, 48 (2003), 1109-1115.  doi: 10.1109/TAC.2003.812821.  Google Scholar

[14]

M. Heinkenschloss, Mesh independence for nonlinear least squares problems with norm constraints, SlAM Journal on Optimization, 3 (1993), 81-117. doi: 10.1137/0803005.  Google Scholar

[15]

M. Heinkenschloss, On the solution of a two ball trust-region subproblem, Mathematical Programming, 64 (1994), 249-276.  doi: 10.1007/BF01582576.  Google Scholar

[16]

V. Jeyakumar and G. Y. Li, Trust-region problems with linear inequality constraints: Exact SDP relaxation, global optimality and robust optimization, Mathematical Programming, 147 (2014), 171-206.  doi: 10.1007/s10107-013-0716-2.  Google Scholar

[17]

B. KaltenbacherF. Rendl and E. Resmerita, Computing quasisolutions of nonlinear inverse problems via efficient minimization of trust region problems, Journal of Inverse and Ill-Posed Problems, 24 (2016), 435-447.  doi: 10.1515/jiip-2015-0087.  Google Scholar

[18]

C. Kravaris and J. H. Seinfeld, Identification of parameters in distributed parameter systems by regularization, SIAM Journal on Control and Optimization, 23 (1985), 217-241.  doi: 10.1137/0323017.  Google Scholar

[19]

G. Kristensson, Inverse problems for acoustic waves using the penalised likelihood method, Inverse Problems, 2 (1986), 461.  Google Scholar

[20]

J. M. Martínez, Local minimizers of quadratic functions on Euclidean balls and spheres, SIAM Journal on Optimization, 4 (1994), 159-176.  doi: 10.1137/0804009.  Google Scholar

[21]

Y. Nesterov, H. Wolkowicz and Y. Ye, Semidefinite Programming Relaxations of Nonconvex Quadratic Optimization, Handbook of Semidefinite Programming, Springer, Boston, (2000), 361–419. doi: 10.1007/978-1-4615-4381-7_13.  Google Scholar

[22] S. Omatu and J. H. Seinfeld, Distributed Parameter Systems: Theory and Applications,, Clarendon Press, 1989.   Google Scholar
[23]

F. O'Sullivan and G. Wahba, A cross validated Bayesian retrieval algorithm for nonlinear remote sensing experiments, Journal of Computational Physics, 59 (1985), 441-455.   Google Scholar

[24]

J.-M. Peng and Y. Yuan, Optimality conditions for the minimization of a quadratic with two quadratic constraints, SIAM Journal on Optimization, 7 (1997), 579-594.  doi: 10.1137/S1052623494261520.  Google Scholar

[25]

S. SakaueY. NakatsukasaA. Takeda and S. Iwata, Solving generalized CDT problems via two-parameter eigenvalues, SIAM Journal on Optimization, 26 (2016), 1669-1694.  doi: 10.1137/15100624X.  Google Scholar

[26]

M. Salahi and A. Taati, A fast eigenvalue approach for solving the trust-region subproblem with an additional linear inequality, Computational and Applied Mathematics, 37 (2018), 329-347.  doi: 10.1007/s40314-016-0347-3.  Google Scholar

[27]

M. SalahiA. Taati and H. Wolkowicz, Local nonglobal minima for solving large scale extended trust-region subproblems, Computational Optimization and Applications, 66 (2016), 223-244.  doi: 10.1007/s10589-016-9867-4.  Google Scholar

[28]

M. Salah and S. Fallahi, Trust-region subproblem with an additional linear inequality constraint, Optimization Letters, 10 (2016), 821-832.  doi: 10.1007/s11590-015-0957-5.  Google Scholar

[29]

J. F. Sturm and S. Zhang, On cones of nonnegative quadratic functions, Mathematics of Operations Research, 28 (2003), 246-267.  doi: 10.1287/moor.28.2.246.14485.  Google Scholar

[30]

C. R. Vogel, A constrained least squares regularization method for nonlinear iii-posed problems, SIAM Journal on Control and Optimization, 28 (1990), 34-49.  doi: 10.1137/0328002.  Google Scholar

[31]

A. Zhang and S. Hayashi, Celis-Dennis-Tapia based approach to quadratic fractional programming problems with two quadratic constraints, Numerical Algebra, Control and Optimization, 1 (2011), 83-98.  doi: 10.3934/naco.2011.1.83.  Google Scholar

Figure 1.   $ + $: global solution of $ Pc_1 $ and $ Pc_2 $, $ \Box $: LNGMs of $ Pc_1 $ and $ Pc_2 $
Table 1.  Notations in the tables
Notation Description
n Dimension of problem
Den Density of A
CPU Run time
$ F_{TB} $ Objective value of the new algorithm
$ F_{AD} $ Objective value of ADMM algorithm [2]
$ F_{SD} $ Objective value of SDP relaxation
$ F_{SA} $ Objective value of Sakaue et. al's algorithm [25]
$ x_{TB}^* $ Optimal solution of the new algorithm
$ x_{SA}^* $ Optimal solution of Sakaue et. al's algorithm [25]
Notation Description
n Dimension of problem
Den Density of A
CPU Run time
$ F_{TB} $ Objective value of the new algorithm
$ F_{AD} $ Objective value of ADMM algorithm [2]
$ F_{SD} $ Objective value of SDP relaxation
$ F_{SA} $ Objective value of Sakaue et. al's algorithm [25]
$ x_{TB}^* $ Optimal solution of the new algorithm
$ x_{SA}^* $ Optimal solution of Sakaue et. al's algorithm [25]
Table 2.  Comparison of the new algorithm with ADMM algorithm [2] (first class)
n Den CPU(TBTRS) CPU(ADMM) $ F_{TB}-F_{AD} $
50 1 0.23 0.36 -9.31e-11
100 1 0.28 0.47 -1.39 e-10
200 1 0.55 0.79 2.33e-11
300 1 0.80 1.75 1.86 e-10
400 1 1.58 2.63 4.66e-10
500 1 2.72 3.99 8.73e-10
700 0.1 1.49 5.174 4.54e-10
1000 0.1 3.27 20.64 3.49e-10
2000 0.01 2.48 7.64 2.79e-10
5000 0.001 2.74 6.59 -1.16e-10
10000 0.0001 4.62 7.29 -1.46e-11
n Den CPU(TBTRS) CPU(ADMM) $ F_{TB}-F_{AD} $
50 1 0.23 0.36 -9.31e-11
100 1 0.28 0.47 -1.39 e-10
200 1 0.55 0.79 2.33e-11
300 1 0.80 1.75 1.86 e-10
400 1 1.58 2.63 4.66e-10
500 1 2.72 3.99 8.73e-10
700 0.1 1.49 5.174 4.54e-10
1000 0.1 3.27 20.64 3.49e-10
2000 0.01 2.48 7.64 2.79e-10
5000 0.001 2.74 6.59 -1.16e-10
10000 0.0001 4.62 7.29 -1.46e-11
Table 3.  Comparison of the new algorithm with Sakaue et. al's algorithm [25] (first class)
n CPU(TBTRS) CPU(Sakaue et. al's algorithm [25]) $ F_{TB}-F_{SA} $ $ ||x^*_{TB}-x^*_{SA}|| $
5 0.10 0.07 -2.27e-14 3.24e-14
10 0.12 0.46 -1.71e-11 1.45e-13
15 0.23 5.94 -3.87e-12 1.78e-14
20 0.18 33.12 -1.82e-09 8.06e-08
25 0.23 130.96 -2.96e-11 9.50e-14
30 0.22 428.21 -6.58e-09 1.26e-10
n CPU(TBTRS) CPU(Sakaue et. al's algorithm [25]) $ F_{TB}-F_{SA} $ $ ||x^*_{TB}-x^*_{SA}|| $
5 0.10 0.07 -2.27e-14 3.24e-14
10 0.12 0.46 -1.71e-11 1.45e-13
15 0.23 5.94 -3.87e-12 1.78e-14
20 0.18 33.12 -1.82e-09 8.06e-08
25 0.23 130.96 -2.96e-11 9.50e-14
30 0.22 428.21 -6.58e-09 1.26e-10
Table 4.  Comparison of the new algorithm with ADMM algorithm [2] (second class)
n Den CPU(TBTRS) CPU(ADMM) $ F_{TB}-F_{AD} $
50 1 0.091 2.46 -3.69e-08
100 1 0.12 3.0022 -1.63e-07
200 1 0.15 4.70 -3.81e-07
300 1 0.24 6.71 -4.61e-07
500 1 0.68 13.5 -6.15e-07
700 1 1.17 21.43 -2.05e-06
1000 1 2.82 47.35 -1.69e-06
2000 0.1 2.39 39.16 -9.22e-07
5000 0.01 2.96 55.92 -1.61e-06
10000 0.001 3.14 132.33 -4.85e-06
n Den CPU(TBTRS) CPU(ADMM) $ F_{TB}-F_{AD} $
50 1 0.091 2.46 -3.69e-08
100 1 0.12 3.0022 -1.63e-07
200 1 0.15 4.70 -3.81e-07
300 1 0.24 6.71 -4.61e-07
500 1 0.68 13.5 -6.15e-07
700 1 1.17 21.43 -2.05e-06
1000 1 2.82 47.35 -1.69e-06
2000 0.1 2.39 39.16 -9.22e-07
5000 0.01 2.96 55.92 -1.61e-06
10000 0.001 3.14 132.33 -4.85e-06
Table 5.  Comparison of the new algorithm with Sakaue et. al's algorithm [25] (second class)
n CPU(TBTRS) CPU(Sakaue et. al's algorithm [25]) $ F_{TB}-F_{SA} $ $ ||x^*_{TB}-x^*_{SA}|| $
5 0.06 0.051 1.42e-11 1.22e-06
10 0.06 0.42 2.58e-10 3.69e-06
15 0.12 6.18 -1.72e-10 2.89e-06
20 0.08 35.03 2.84e-09 1.07e-05
25 0.13 139.93 2.96e-09 1.02e-05
30 0.14 449.08 4.73e-08 3.45e-05
n CPU(TBTRS) CPU(Sakaue et. al's algorithm [25]) $ F_{TB}-F_{SA} $ $ ||x^*_{TB}-x^*_{SA}|| $
5 0.06 0.051 1.42e-11 1.22e-06
10 0.06 0.42 2.58e-10 3.69e-06
15 0.12 6.18 -1.72e-10 2.89e-06
20 0.08 35.03 2.84e-09 1.07e-05
25 0.13 139.93 2.96e-09 1.02e-05
30 0.14 449.08 4.73e-08 3.45e-05
Table 6.  Comparison of the new algorithm with SDP relaxation (third class)
n Den CPU(TBTRS) CPU(ADMM) CPU(CVX) $ F_{TB}-F_{AD} $ $ F_{TB}-F_{SD} $
50 1 0.09 2.04 0.97 2.30e-09 -1.58e-05
100 1 0.18 2.01 2.93 1.75e-08 -7.33e-05
200 1 0.16 4.05 5.41 3.82e-07 -9.45e-04
300 1 0.24 6.16 14.39 5.71e-07 -2.23e-4
400 1 0.41 10.78 38.06 1.35e-06 -3.78e-4
500 1 0.67 11.87 63.26 4.67e-07 -4.67e-4
700 0.1 0.32 8.39 132.96 1.43e-07 -2.75e-4
800 0.1 0.39 9.02 206.75 2.42e-07 -2.904e-4
900 0.1 0.44 9.67 268.47 8.91e-07 -4.88e-4
1000 0.1 0.52 11.21 427.06 3.29e-07 -5.71e-4
2000 0.1 2.69 39.09 $ - $ 5.28e-05 $ - $
3000 0.1 5.27 82.52 $ - $ 3.85e-05 $ - $
5000 0.01 2.99 57.99 $ - $ 6.16e-05 $ - $
10000 0.001 2.34 121.13 $ - $ 7.12e-05 $ - $
n Den CPU(TBTRS) CPU(ADMM) CPU(CVX) $ F_{TB}-F_{AD} $ $ F_{TB}-F_{SD} $
50 1 0.09 2.04 0.97 2.30e-09 -1.58e-05
100 1 0.18 2.01 2.93 1.75e-08 -7.33e-05
200 1 0.16 4.05 5.41 3.82e-07 -9.45e-04
300 1 0.24 6.16 14.39 5.71e-07 -2.23e-4
400 1 0.41 10.78 38.06 1.35e-06 -3.78e-4
500 1 0.67 11.87 63.26 4.67e-07 -4.67e-4
700 0.1 0.32 8.39 132.96 1.43e-07 -2.75e-4
800 0.1 0.39 9.02 206.75 2.42e-07 -2.904e-4
900 0.1 0.44 9.67 268.47 8.91e-07 -4.88e-4
1000 0.1 0.52 11.21 427.06 3.29e-07 -5.71e-4
2000 0.1 2.69 39.09 $ - $ 5.28e-05 $ - $
3000 0.1 5.27 82.52 $ - $ 3.85e-05 $ - $
5000 0.01 2.99 57.99 $ - $ 6.16e-05 $ - $
10000 0.001 2.34 121.13 $ - $ 7.12e-05 $ - $
Table 7.  Comparison of the new algorithm with Sakaue et. al's algorithm [25] (third class)
n CPU(TBTRS) CPU(Sakaue et. al's algorithm [25]) $ F_{AD}-F_{SA} $ $ ||x^*_{AD}-x^*_{SA}|| $
5 0.08 0.059 1.32e-13 9.59e-08
10 0.06 0.36 1.86e-09 1.08e-05
15 0.06 5.69 5.12e-09 1.66e-05
20 0.07 32.62 1.25e-09 6.09e-06
25 0.14 130.46 7.24e-10 3.75e-06
30 0.14 419.58 8.38e-09 1.72e-05
n CPU(TBTRS) CPU(Sakaue et. al's algorithm [25]) $ F_{AD}-F_{SA} $ $ ||x^*_{AD}-x^*_{SA}|| $
5 0.08 0.059 1.32e-13 9.59e-08
10 0.06 0.36 1.86e-09 1.08e-05
15 0.06 5.69 5.12e-09 1.66e-05
20 0.07 32.62 1.25e-09 6.09e-06
25 0.14 130.46 7.24e-10 3.75e-06
30 0.14 419.58 8.38e-09 1.72e-05
[1]

Jing Zhou, Cheng Lu, Ye Tian, Xiaoying Tang. A socp relaxation based branch-and-bound method for generalized trust-region subproblem. Journal of Industrial & Management Optimization, 2021, 17 (1) : 151-168. doi: 10.3934/jimo.2019104

[2]

Huu-Quang Nguyen, Ya-Chi Chu, Ruey-Lin Sheu. On the convexity for the range set of two quadratic functions. Journal of Industrial & Management Optimization, 2020  doi: 10.3934/jimo.2020169

[3]

Min Xi, Wenyu Sun, Jun Chen. Survey of derivative-free optimization. Numerical Algebra, Control & Optimization, 2020, 10 (4) : 537-555. doi: 10.3934/naco.2020050

[4]

Predrag S. Stanimirović, Branislav Ivanov, Haifeng Ma, Dijana Mosić. A survey of gradient methods for solving nonlinear optimization. Electronic Research Archive, 2020, 28 (4) : 1573-1624. doi: 10.3934/era.2020115

[5]

Wolfgang Riedl, Robert Baier, Matthias Gerdts. Optimization-based subdivision algorithm for reachable sets. Journal of Computational Dynamics, 2021, 8 (1) : 99-130. doi: 10.3934/jcd.2021005

[6]

Xinpeng Wang, Bingo Wing-Kuen Ling, Wei-Chao Kuang, Zhijing Yang. Orthogonal intrinsic mode functions via optimization approach. Journal of Industrial & Management Optimization, 2021, 17 (1) : 51-66. doi: 10.3934/jimo.2019098

[7]

Alberto Bressan, Sondre Tesdal Galtung. A 2-dimensional shape optimization problem for tree branches. Networks & Heterogeneous Media, 2020  doi: 10.3934/nhm.2020031

[8]

Yi An, Bo Li, Lei Wang, Chao Zhang, Xiaoli Zhou. Calibration of a 3D laser rangefinder and a camera based on optimization solution. Journal of Industrial & Management Optimization, 2021, 17 (1) : 427-445. doi: 10.3934/jimo.2019119

[9]

Haodong Yu, Jie Sun. Robust stochastic optimization with convex risk measures: A discretized subgradient scheme. Journal of Industrial & Management Optimization, 2021, 17 (1) : 81-99. doi: 10.3934/jimo.2019100

[10]

Ripeng Huang, Shaojian Qu, Xiaoguang Yang, Zhimin Liu. Multi-stage distributionally robust optimization with risk aversion. Journal of Industrial & Management Optimization, 2021, 17 (1) : 233-259. doi: 10.3934/jimo.2019109

[11]

Yasmine Cherfaoui, Mustapha Moulaï. Biobjective optimization over the efficient set of multiobjective integer programming problem. Journal of Industrial & Management Optimization, 2021, 17 (1) : 117-131. doi: 10.3934/jimo.2019102

[12]

Liping Tang, Ying Gao. Some properties of nonconvex oriented distance function and applications to vector optimization problems. Journal of Industrial & Management Optimization, 2021, 17 (1) : 485-500. doi: 10.3934/jimo.2020117

[13]

Cheng He, Changzheng Qu. Global weak solutions for the two-component Novikov equation. Electronic Research Archive, 2020, 28 (4) : 1545-1562. doi: 10.3934/era.2020081

[14]

M. S. Lee, H. G. Harno, B. S. Goh, K. H. Lim. On the bang-bang control approach via a component-wise line search strategy for unconstrained optimization. Numerical Algebra, Control & Optimization, 2021, 11 (1) : 45-61. doi: 10.3934/naco.2020014

[15]

Mohammed Abdulrazaq Kahya, Suhaib Abduljabbar Altamir, Zakariya Yahya Algamal. Improving whale optimization algorithm for feature selection with a time-varying transfer function. Numerical Algebra, Control & Optimization, 2021, 11 (1) : 87-98. doi: 10.3934/naco.2020017

[16]

Ahmad Z. Fino, Wenhui Chen. A global existence result for two-dimensional semilinear strongly damped wave equation with mixed nonlinearity in an exterior domain. Communications on Pure & Applied Analysis, 2020, 19 (12) : 5387-5411. doi: 10.3934/cpaa.2020243

[17]

Peng Luo. Comparison theorem for diagonally quadratic BSDEs. Discrete & Continuous Dynamical Systems - A, 2020  doi: 10.3934/dcds.2020374

[18]

Djamel Aaid, Amel Noui, Özen Özer. Piecewise quadratic bounding functions for finding real roots of polynomials. Numerical Algebra, Control & Optimization, 2021, 11 (1) : 63-73. doi: 10.3934/naco.2020015

[19]

Nicolas Rougerie. On two properties of the Fisher information. Kinetic & Related Models, , () : -. doi: 10.3934/krm.2020049

[20]

Haiyu Liu, Rongmin Zhu, Yuxian Geng. Gorenstein global dimensions relative to balanced pairs. Electronic Research Archive, 2020, 28 (4) : 1563-1571. doi: 10.3934/era.2020082

 Impact Factor: 

Metrics

  • PDF downloads (191)
  • HTML views (543)
  • Cited by (0)

Other articles
by authors

[Back to Top]