Figure 1.
h(x,ω0) and l(x,ω0) in box case
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2020, 16(5): 2351-2367.
doi: 10.3934/jimo.2019057
A new concave reformulation and its application in solving DC programming globally under uncertain environment
School of Mathematics, Shanghai University of Finance and Economics, Shanghai 200433, China |
In this paper, a new concave reformulation is proposed on a convex hull of some given points. Based on its properties, we attempt to solve DC Programming problems globally under uncertain environment by using Robust optimization method and CVaR method. A global optimization algorithm is developed for the Robust counterpart and CVaR model with two kinds of special convex hulls: simplex set and box set. The global solution is obtained by solving a sequence of convex relaxation programming on the original constraint sets or divided subsets with branch and bound method. Finally, numerical experiments are given for DC programs under uncertain environment with two kinds of constraints: simplex and box sets. Simulation results show the feasibility and efficiency of the proposed global optimization algorithm.
Keywords:
Concave reformulation,
DC programming,
global optimization,
convex relaxation,
branch and bound.
Mathematics Subject Classification:
Primary: 65K05; Secondary: 90C90.
Citation:
Yanjun Wang, Kaiji Shen. A new concave reformulation and its application in solving DC programming globally under uncertain environment. Journal of Industrial and Management Optimization,
2020, 16
(5)
: 2351-2367.
doi: 10.3934/jimo.2019057
![]()
References:
| [1] |
A. Ben-Tal and A. Nemirovski,
Robust convex optimization, Mathematics of Operations Research, 23 (1998), 769-805.
doi: 10.1287/moor.23.4.769. |
| [2] |
A. Ben-Tal and A. Nemirovski,
Robust solutions of uncertain linear programs, Operations research letters, 25 (1999), 1-13.
doi: 10.1016/S0167-6377(99)00016-4. |
| [3] |
A. Ben-Tal and A. Nemirovski,
Robust solutions of linear programming problems contaminated with uncertain data, Mathematical Programming, 88 (2000), 411-424.
doi: 10.1007/PL00011380. |
| [4] |
T. P. Dinh,
The DC (difference of convex functions) programming and DCA revisited with DC models of real world nonconvex optimization problems, Annals of Operations Research, 113 (2005), 23-46.
doi: 10.1007/s10479-004-5022-1. |
| [5] |
T. P. Dinh and A. Le Thi Hoai,
A DC optimization algorithm for solving the trust-region subproblem, SIAM J. Optim., 8 (1998), 476-505.
doi: 10.1137/S1052623494274313. |
| [6] |
A. Edward, H. F. Xu and D. L. Zhang, Confidence levels for cvar risk measures and minimax limits, Business Analytics, 2014. |
| [7] |
R. Horst and H. Tuy, Global Optimization: Deterministic Approaches, Second edition. Springer-Verlag, Berlin, 1993.
doi: 10.1007/978-3-662-02947-3. |
| [8] |
C. D. Maranas and C. A. Floudas,
Global optimization in generalized geometric programming, Computers & Chemical Engineering, 21 (1997), 351-369.
|
| [9] |
G. C. Pflug,
Some remarks on the value-at-risk and the conditional value-at-risk, Probabilistic constrained optimization, 8 (2000), 272-281.
doi: 10.1007/978-1-4757-3150-7_15. |
| [10] |
H. Reiner and T. Nguyen V,
Robust solutions of linear programming problems contaminated with uncertain data, Journal of Optimization Theory and Applications, 103 (1999), 1-43.
|
| [11] |
R. T. Rockafellar, Convex Analysis, Princeton University Press, Princeton, N.J., 1970.
|
| [12] |
R. T. Rockafellar and S. Uryasev,
Optimization of conditional value-at-risk, Journal of Risk, 2 (2000), 21-41.
doi: 10.21314/JOR.2000.038. |
| [13] |
R. T. Rockafellar and S. Uryasev,
Conditional value-at-risk for general loss distributions, Journal of Banking & Finance, 26 (2002), 1443-1471.
|
| [14] |
R. T. Rockafellar,
Coherent approaches to risk in optimization under uncertainty, OR Tools and Applications: Glimpses of Future Technologies, 8 (2007), 38-61.
doi: 10.1287/educ.1073.0032. |
| [15] |
P. P. Shen and C. F. Wang,
Global optimization for sum of linear ratios problem with coefficients, Applied Mathematics and Computation, 176 (2006), 219-229.
doi: 10.1016/j.amc.2005.09.047. |
| [16] |
Y. J. Wang and Y. Lan,
Global optimization for special reverse convex programming, Computers & Mathematics with Applications, 55 (2008), 1154-1163.
doi: 10.1016/j.camwa.2007.04.046. |
show all references
References:
| [1] |
A. Ben-Tal and A. Nemirovski,
Robust convex optimization, Mathematics of Operations Research, 23 (1998), 769-805.
doi: 10.1287/moor.23.4.769. |
| [2] |
A. Ben-Tal and A. Nemirovski,
Robust solutions of uncertain linear programs, Operations research letters, 25 (1999), 1-13.
doi: 10.1016/S0167-6377(99)00016-4. |
| [3] |
A. Ben-Tal and A. Nemirovski,
Robust solutions of linear programming problems contaminated with uncertain data, Mathematical Programming, 88 (2000), 411-424.
doi: 10.1007/PL00011380. |
| [4] |
T. P. Dinh,
The DC (difference of convex functions) programming and DCA revisited with DC models of real world nonconvex optimization problems, Annals of Operations Research, 113 (2005), 23-46.
doi: 10.1007/s10479-004-5022-1. |
| [5] |
T. P. Dinh and A. Le Thi Hoai,
A DC optimization algorithm for solving the trust-region subproblem, SIAM J. Optim., 8 (1998), 476-505.
doi: 10.1137/S1052623494274313. |
| [6] |
A. Edward, H. F. Xu and D. L. Zhang, Confidence levels for cvar risk measures and minimax limits, Business Analytics, 2014. |
| [7] |
R. Horst and H. Tuy, Global Optimization: Deterministic Approaches, Second edition. Springer-Verlag, Berlin, 1993.
doi: 10.1007/978-3-662-02947-3. |
| [8] |
C. D. Maranas and C. A. Floudas,
Global optimization in generalized geometric programming, Computers & Chemical Engineering, 21 (1997), 351-369.
|
| [9] |
G. C. Pflug,
Some remarks on the value-at-risk and the conditional value-at-risk, Probabilistic constrained optimization, 8 (2000), 272-281.
doi: 10.1007/978-1-4757-3150-7_15. |
| [10] |
H. Reiner and T. Nguyen V,
Robust solutions of linear programming problems contaminated with uncertain data, Journal of Optimization Theory and Applications, 103 (1999), 1-43.
|
| [11] |
R. T. Rockafellar, Convex Analysis, Princeton University Press, Princeton, N.J., 1970.
|
| [12] |
R. T. Rockafellar and S. Uryasev,
Optimization of conditional value-at-risk, Journal of Risk, 2 (2000), 21-41.
doi: 10.21314/JOR.2000.038. |
| [13] |
R. T. Rockafellar and S. Uryasev,
Conditional value-at-risk for general loss distributions, Journal of Banking & Finance, 26 (2002), 1443-1471.
|
| [14] |
R. T. Rockafellar,
Coherent approaches to risk in optimization under uncertainty, OR Tools and Applications: Glimpses of Future Technologies, 8 (2007), 38-61.
doi: 10.1287/educ.1073.0032. |
| [15] |
P. P. Shen and C. F. Wang,
Global optimization for sum of linear ratios problem with coefficients, Applied Mathematics and Computation, 176 (2006), 219-229.
doi: 10.1016/j.amc.2005.09.047. |
| [16] |
Y. J. Wang and Y. Lan,
Global optimization for special reverse convex programming, Computers & Mathematics with Applications, 55 (2008), 1154-1163.
doi: 10.1016/j.camwa.2007.04.046. |
Figure 2.
h(x,ω0) and l(x,ω0) in simplex case
Figure 3.
Optimal value comparison of Robust Model and CVaR model
| α | CPU(s) | Step | Nodes | Opt Solution | Opt Value | Opt* |
| 0.70 | 356.11 | 87 | 69 | (0.0000, 1.1250, 1.8750)T | -2.2690 | -2.2689 |
| 0.75 | 956.11 | 163 | 101 | (0.0000, 1.0547, 1.5703)T | -2.1214 | -2.1214 |
| 0.80 | 847.73 | 163 | 106 | (0.0000, 0.7969, 1.1191)T | -1.9628 | -1.9628 |
| 0.85 | 516.92 | 132 | 106 | (0.0000, 0.6211, 0.8789)T | -1.7508 | -1.7508 |
| 0.90 | 518.31 | 122 | 107 | (0.0000, 0.4569, 0.6797)T | -1.5661 | -1.5663 |
| 0.95 | 321.43 | 78 | 68 | (0.0000, 0.3580, 0.5326)T | -1.4453 | -1.4452 |
| 0.97 | 143.17 | 31 | 27 | (0.0000, 0.3387, 0.5038)T | -1.4228 | -1.4228 |
| 0.98 | 160.39 | 30 | 24 | (0.0104, 0.3437, 0.5156)T | -1.4222 | -1.4222 |
| 0.99 | 167.81 | 31 | 26 | (0.0023, 0.3281, 0.5000)T | -1.4221 | -1.4221 |
| Robust | 218.18 | 52 | 51 | (0.0080, 0.3515, 0.5002)T | -1.4221 | -1.4221 |
| α | CPU(s) | Step | Nodes | Opt Solution | Opt Value | Opt* |
| 0.70 | 356.11 | 87 | 69 | (0.0000, 1.1250, 1.8750)T | -2.2690 | -2.2689 |
| 0.75 | 956.11 | 163 | 101 | (0.0000, 1.0547, 1.5703)T | -2.1214 | -2.1214 |
| 0.80 | 847.73 | 163 | 106 | (0.0000, 0.7969, 1.1191)T | -1.9628 | -1.9628 |
| 0.85 | 516.92 | 132 | 106 | (0.0000, 0.6211, 0.8789)T | -1.7508 | -1.7508 |
| 0.90 | 518.31 | 122 | 107 | (0.0000, 0.4569, 0.6797)T | -1.5661 | -1.5663 |
| 0.95 | 321.43 | 78 | 68 | (0.0000, 0.3580, 0.5326)T | -1.4453 | -1.4452 |
| 0.97 | 143.17 | 31 | 27 | (0.0000, 0.3387, 0.5038)T | -1.4228 | -1.4228 |
| 0.98 | 160.39 | 30 | 24 | (0.0104, 0.3437, 0.5156)T | -1.4222 | -1.4222 |
| 0.99 | 167.81 | 31 | 26 | (0.0023, 0.3281, 0.5000)T | -1.4221 | -1.4221 |
| Robust | 218.18 | 52 | 51 | (0.0080, 0.3515, 0.5002)T | -1.4221 | -1.4221 |
| α | CPU(s) | Step | Nodes | Opt Solution | Opt Value | Opt* |
| 0.70 | 264.90 | 24 | 22 | (0.0000, 0.6719, 0.9844)T | -2.2410 | -2.2410 |
| 0.75 | 105.19 | 25 | 29 | (0.0000, 0.6641, 0.9844)T | -2.1214 | -2.1215 |
| 0.80 | 217.65 | 24 | 19 | (0.0000, 0.7187, 0.9844)T | -1.9520 | -1.9520 |
| 0.85 | 516.92 | 55 | 40 | (0.0000, 0.6250, 0.8750)T | -1.7506 | -1.7505 |
| 0.90 | 350.26 | 39 | 34 | (0.0000, 0.4531, 0.6741)T | -1.5661 | -1.5661 |
| 0.95 | 208.85 | 38 | 32 | (0.0000, 0.3580, 0.5326)T | -1.4452 | -1.4452 |
| 0.97 | 143.17 | 31 | 27 | (0.0000, 0.3437, 0.5156)T | -1.4228 | -1.4228 |
| 0.98 | 167.81 | 30 | 26 | (0.0104, 0.3437, 0.5156)T | -1.4222 | -1.4222 |
| 0.99 | 160.39 | 31 | 24 | (0.0023, 0.3281, 0.5000)T | -1.4221 | -1.4221 |
| Robust | 216.98 | 35 | 27 | (0.0080, 0.3515, 0.5002)T | -1.4221 | -1.4221 |
| α | CPU(s) | Step | Nodes | Opt Solution | Opt Value | Opt* |
| 0.70 | 264.90 | 24 | 22 | (0.0000, 0.6719, 0.9844)T | -2.2410 | -2.2410 |
| 0.75 | 105.19 | 25 | 29 | (0.0000, 0.6641, 0.9844)T | -2.1214 | -2.1215 |
| 0.80 | 217.65 | 24 | 19 | (0.0000, 0.7187, 0.9844)T | -1.9520 | -1.9520 |
| 0.85 | 516.92 | 55 | 40 | (0.0000, 0.6250, 0.8750)T | -1.7506 | -1.7505 |
| 0.90 | 350.26 | 39 | 34 | (0.0000, 0.4531, 0.6741)T | -1.5661 | -1.5661 |
| 0.95 | 208.85 | 38 | 32 | (0.0000, 0.3580, 0.5326)T | -1.4452 | -1.4452 |
| 0.97 | 143.17 | 31 | 27 | (0.0000, 0.3437, 0.5156)T | -1.4228 | -1.4228 |
| 0.98 | 167.81 | 30 | 26 | (0.0104, 0.3437, 0.5156)T | -1.4222 | -1.4222 |
| 0.99 | 160.39 | 31 | 24 | (0.0023, 0.3281, 0.5000)T | -1.4221 | -1.4221 |
| Robust | 216.98 | 35 | 27 | (0.0080, 0.3515, 0.5002)T | -1.4221 | -1.4221 |
Table 3.
Simplex feasible
| CPU Time(s) | Nodes | SEED | |
| RDC | 91 | 11 | 1 |
| Floudas | 227 | 42 | 1 |
| RDC | 100 | 15 | 2 |
| Floudas | 204 | 40 | 2 |
| RDC | 120 | 25 | 3 |
| Floudas | 280 | 75 | 3 |
| CPU Time(s) | Nodes | SEED | |
| RDC | 91 | 11 | 1 |
| Floudas | 227 | 42 | 1 |
| RDC | 100 | 15 | 2 |
| Floudas | 204 | 40 | 2 |
| RDC | 120 | 25 | 3 |
| Floudas | 280 | 75 | 3 |
Table 4.
Box feasible
| CPU Time(s) | Nodes | SEED | |
| RDC | 88 | 8 | 1 |
| Floudas | 220 | 40 | 1 |
| RDC | 105 | 12 | 2 |
| Floudas | 207 | 36 | 2 |
| RDC | 117 | 20 | 3 |
| Floudas | 281 | 68 | 3 |
| CPU Time(s) | Nodes | SEED | |
| RDC | 88 | 8 | 1 |
| Floudas | 220 | 40 | 1 |
| RDC | 105 | 12 | 2 |
| Floudas | 207 | 36 | 2 |
| RDC | 117 | 20 | 3 |
| Floudas | 281 | 68 | 3 |
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