An improved partial bundle method for linearly constrained minimax problems

  • Chunming Tang Guangxi University
  • Huangyue Chen Guangxi University
  • Jinbao Jian Yulin Normal University
Keywords: Minimax problems, Bundle method, Partial cutting-planes model, Global convergence, Subgradient aggregation


In this paper, we propose an improved partial bundle method for solving linearly constrained minimax problems. In order to reduce the number of component function evaluations, we utilize a partial cutting-planes model to substitute for the traditional one. At each iteration, only one quadratic programming subproblem needs to be solved to obtain a new trial point. An improved descent test criterion is introduced to simplify the algorithm. The method produces a sequence of feasible trial points, and ensures that the objective function is monotonically decreasing on the sequence of stability centers. Global convergence of the algorithm is established. Moreover, we utilize the subgradient aggregation strategy to control the size of the bundle and therefore overcome the difficulty of computation and storage. Finally, some preliminary numerical results show that the proposed method is effective.


I. Averbakh and O. Berman, Minimax p-traveling salemen location problems on a tree, Annals of Operations Research, vol. 110, pp. 55-68, 2002.

A. Baums, Minimax method in optimizing energy consumption in real-time embedded systems, Automatic Control and Computer Sciences, vol. 43, pp. 57-62, 2009.

J. F. Bonnans, J. C. Lemaréchal and C. Sagastizábal, Numerical Optimization: Theoretical and Practical Aspects, Second ed, Springer-Verlag, 2006.

J. V. Burke, A. S. Lewis and M. L. Overton, A robust gradient sampling algorithm for nonsmooth,nonconvex optimization, SIAM Journal on Optimization, vol. 15, pp. 751-779, 2005.

E. W. Cheney and A. A. Goldstein, Newton’s method for convex programming and Tchebycheff approximations, Numerische Mathematik, vol. 1, pp. 253-268, 1959.

M. Gaudioso, G. Giallombardo and G. Miglionico, An incremental method for solving convex finite min-max problems, Mathematics of Operations Research, vol. 31, pp. 173-187, 2006.

W. Hare and M. Macklem, Derivative-free optimization methods for finite minimax problems, Optimization Methods and Software, vol. 28, pp. 300-312, 2013.

W. Hare and J. Nutini, A derivative-free approximate gradient sampling algorithm for finite minimax problems, Computational Optimization and Applications, vol. 56, pp. 1-38, 2013.

J. B. Jian, C. M. Tang and F. Tang, A feasible descent bundle method for inequality constrained minimax problems (in Chinese), Science China: Mathematics, vol. 45, pp. 2001-2024, 2015.

J. E. Kelley, The cutting-plane method for solving convex programs, Journal of the society for Industrial and Applied Mathematics, vol. 8, pp. 703-712, 1960.

K. C. Kiwiel, Method of Descent for Nondifferentiable Optimization, Lecture Note in Mathematics 1133, Springer-Verlag, 1985.

K. C. Kiwiel, Proximity control in bundle methods for convex nondifferentiable minimization, Mathematical Programming, vol. 46, pp. 105-122, 1990.

K. C. Kiwiel, A projection-proximal bundle method for convex nondifferentiable minimization, In M. Théra et al. (Eds), Ill-posed Variational Problems and Regularization Techniques, Lecture Notes in Econom. Math. Systems 477, pp. 137-150, Berlin: Springer-Verlag, 1999.

K. C. Kiwiel, A proximal bundle method with approximate subgradient linearizations, SIAM Journal on Optimization, vol. 16, pp. 1007-1023, 2006.

K. C. Kiwiel, A proximal-projection bundle method for Lagrangian relaxation, including semidefinite programming, SIAM Journal on Optimization, vol. 17, pp. 1015-1034, 2006.

C. Lemaréchal, An extention of Davidon methods to nondifferentiable problems, Mathematical Programming Study, vol. 3, pp. 95-109, 1975.

X. S. Li and S. C. Fang, On the entropic regularization method for solving min-max problems with applications, Mathematical Method of Operations Research, vol. 46, pp. 119-130, 1997.

G. Liuzzi, S. Lucidi and M. Sciandrone, A derivative-free algorithm for linearly constrained finite minimax problems, SIAM Journal on Optimization, vol. 16, pp. 1054-1075, 2006.

L. Luksan and J. Vlcek, Test problems for nonsmooth unconstrained and linearly constrained optimization, Technical Report No.798, Institute of Computer Science, Academy of Science of the Czech Republic, Prague, 2000.

MOSEK The MOSEK optimization tooltox for MATLAB manual, Version 7.1 (Revision 30) MOSEK ApS, Denmark,, 2015.

M. L. Overton, Algorithms for nonlinear ℓ1and ℓ∞fitting, In M.J.D.Powell (Ed.), Nonlinear Optimization, pp. 213-221, 1982.

C. M. Tang, J. B. Jian and G. Y. Li, A proximal-projection partial bundle method for convex constrained minimax problems, Manuscript, 2015.

F. Y. Wang, Y. Yamamoto and M. Yu, A minimax rule for portfolio selection in frictional markets, Mathematical Methods of Operations Research, vol. 57, pp. 141-155, 2003.

F. S. Wang and K. C. Zhang, A hybrid algorithm for nonlinear minimax problems, Annals of Operations Research, vol. 164, pp. 167-191, 2008.

F. S. Wang and K. C. Zhang, A hybrid algorithm for linearly constrained minimax problems, Annals of Operations Research, vol. 206, pp. 501-525, 2013.

G. A. Waston, The minimax solution of an overdetermined system of nonlinear equations, Journal of the Institute of Mathematics and its Applications, vol. 23, pp. 167-180, 1979.

P. Wolf, A method of conjugate subgradient for minimizing nondifferentiable function, Mathematical Programming Study, vol. 3, pp. 145-173, 1975.

How to Cite
Tang, C., Chen, H., & Jian, J. (2016). An improved partial bundle method for linearly constrained minimax problems. Statistics, Optimization & Information Computing, 4(1), 84-98.
Research Articles