On the minimum-norm solution of convex quadratic programming
RAIRO. Operations Research, Tome 55 (2021) no. 1, pp. 247-260

We discuss some basic concepts and present a numerical procedure for finding the minimum-norm solution of convex quadratic programs (QPs) subject to linear equality and inequality constraints. Our approach is based on a theorem of alternatives and on a convenient characterization of the solution set of convex QPs. We show that this problem can be reduced to a simple constrained minimization problem with a once-differentiable convex objective function. We use finite termination of an appropriate Newton’s method to solve this problem. Numerical results show that the proposed method is efficient.

DOI : 10.1051/ro/2021011
Classification : 90C05, 90C30, 15A39
Keywords: Solution set of convex problems, minimum-norm solution of convex quadratic programs, Newton’s method, theorems of alternative
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Ketabchi, Saeed; Moosaei, Hossein; Hladík, Milan. On the minimum-norm solution of convex quadratic programming. RAIRO. Operations Research, Tome 55 (2021) no. 1, pp. 247-260. doi: 10.1051/ro/2021011

[1] L. Armijo, Minimization of functions having lipschitz continuous first partial derivatives. Pac. J. Math. 16 (1966) 1–3. | MR | Zbl | DOI

[2] M. S. Bazaraa, H. D. Sherali and C. M. Shetty, Nonlinear Programming: Theory and Algorithms. John Wiley & Sons, New York (2013). | Zbl | MR

[3] A. Beck and S. Sabach, A first order method for finding minimal norm-like solutions of convex optimization problems. Math. Program. 147 (2014) 25–46. | MR | Zbl | DOI

[4] A. Callejo, F. Gholami, A. Enzenhöfer and J. Kövecses, Unique minimum norm solution to redundant reaction forces in multibody systems. Mech. Mach. Theory 116 (2017) 310–325. | DOI

[5] Y. G. Evtushenko and A. Golikov, Theorems of alternative and optimization, in Optimization and Applications, edited by N. Olenev et al. In Vol. 12422 of Lecture Note in Computer Science. Springer, Cham (2020) 86–96. | MR | Zbl | DOI

[6] Y. G. Evtushenko and A. Golikov, New perspective on the theorems of alternative. In: High Performance Algorithms and Software for Nonlinear Optimization, edited by G. Di Pillo and A. Murli. Vol. 82 of: Applied Optimization. Kluwer Academic Publishers Springer (2003) 227–241. | MR | Zbl | DOI

[7] D. Gale, The Theory of Linear Economic Models. University of Chicago Press (1989). | MR

[8] M. S. Gockenbach and E. Gorgin, On the convergence of a heuristic parameter choice rule for Tikhonov regularization. SIAM J. Sci. Comput. 40 (2018) A2694–A2719. | MR | Zbl | DOI

[9] A. I. Golikov and Y. G. Evtushenko, Theorems of the alternative and their applications in numerical methods. Comput. Math. Math. Phys. 43 (2003) 338–358. | Zbl | MR

[10] E. R. Hansen and G. W. Walster, Global Optimization Using Interval Analysis, 2nd edition. Marcel Dekker, New York (2004). | MR | Zbl

[11] J.-B. Hiriart-Urruty, J.-J. Strodiot and V. H. Nguyen, Generalized Hessian matrix and second-order optimality conditions for problems with C 1 , 1 data. Appl. Math. Optim. 11 (1984) 43–56. | MR | Zbl | DOI

[12] L. Jaulin, M. Kieffer, O. Didrit and É. Walter, Applied Interval Analysis. Springer, London (2001). | MR | Zbl | DOI

[13] C. Kanzow, H. Qi and L. Qi, On the minimum norm solution of linear programs. J. Optim. Theory App. 116 (2003) 333–345. | MR | Zbl | DOI

[14] S. Ketabchi and E. Ansari-Piri, On the solution set of convex problems and its numerical application. J. Comput. Appl. Math. 206 (2007) 288–292. | MR | Zbl | DOI

[15] S. Ketabchi and H. Moosaei, Minimum norm solution to the absolute value equation in the convex case. J. Optim. Theory App. 154 (2012) 1080–1087. | MR | Zbl | DOI

[16] S. Kindermann and K. Raik, Convergence of heuristic parameter choice rules for convex Tikhonov regularization. SIAM J. Numer. Anal. 58 (2020) 1773–1800. | MR | Zbl | DOI

[17] O. L. Mangasarian, A simple characterization of solution sets of convex programs. Oper. Res. Lett. 7 (1988) 21–26. | MR | Zbl | DOI

[18] O. L. Mangasarian, A Finite newton method for classification. Optim. Methods Softw. 17 (2002) 913–929. | MR | Zbl | DOI

[19] P. M. Pardalos, S. Ketabchi and H. Moosaei, Minimum norm solution to the positive semidefinite linear complementarity problem. Optimization 63 (2014) 359–369. | MR | Zbl | DOI

[20] O. Prokopyev, On equivalent reformulations for absolute value equations. Comput. Optim. App. 44 (2009) 363–372. | MR | Zbl | DOI

[21] J. B. Rosen, Minimum norm solution to the linear complementarity problem. In: Functional Analysis, Optimization and Mathematical Economics, edited by L. J. Leifman et al. Oxford University Press, Oxford (1990) 208–216. | MR | Zbl | DOI

[22] A. N. Tikhonov and V. I. Arsenin, Solutions of Ill-Posed Problems. Winston, Washington, DC (1977). | MR | Zbl

[23] X.-H. Vu, D. Sam-Haroud and M.-C. Silaghi, Numerical constraint satisfaction problems with non-isolated solutions. In: Global Optimization and Constraint Satisfaction, edited by C. Bliek, C. Jermann and A. Neumaier. Springer, Berlin-Heidelberg (2003) 194–210. | Zbl | DOI

[24] J. Wang and Y. Xia, A dual neural network solving quadratic programming problems. In: Vol. 1 of IJCNN’99. International Joint Conference on Neural Networks. Proceedings (Cat. No. 99CH36339). IEEE (1999) 588–593. | DOI

[25] Y. Yao, R. Chen and H.-K. Xu, Schemes for finding minimum-norm solutions of variational inequalities. Nonlinear Anal.: Theory Methods App. 72 (2010) 3447–3456. | MR | Zbl | DOI

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