- Research Article
- Open Access
On Two Iterative Methods for Mixed Monotone Variational Inequalities
© Xiwen Lu et al. 2010
- Received: 22 September 2009
- Accepted: 23 November 2009
- Published: 7 December 2009
A mixed monotone variational inequality (MMVI) problem in a Hilbert space is formulated to find a point such that for all , where is a monotone operator and is a proper, convex, and lower semicontinuous function on . Iterative algorithms are usually applied to find a solution of an MMVI problem. We show that the iterative algorithm introduced in the work of Wang et al., (2001) has in general weak convergence in an infinite-dimensional space, and the algorithm introduced in the paper of Noor (2001) fails in general to converge to a solution.
- Variational Inequality
- Convergence Result
- Monotone Operator
- Maximal Monotone
- Maximal Monotone Operator
Let be a real Hilbert space with inner product and norm and let be an operator with domain and range in . Recall that is monotone if its graph is a monotone set in . This means that is monotone if and only if
It is well known (cf. ) that is a maximal monotone operator.
If , we write for It is known that is monotone if and only of for each the resolvent is nonexpansive, and is maximal monotone if and only of for each , the resolvent is nonexpansive and defined on the entire space . Recall that a self-mapping of a closed convex subset of is said to be
Variational inequalities have extensively been studied; see the monographs by Baiocchi and Capelo , Cottle et al. , Glowinski et al. , Giannessi and Maugeri , and Kinderlehrer and Stampacciha .
Let and . The inexact implicit method introduced in  generates a sequence defined in the following way. Once has been constructed, the next iterate is implicitly constructed satisfying the equation
and the algorithm (2.2) is thus reduced to a special case of the Eckastein-Bertsekas algorithm 
where If , then algorithm (2.2) is reduced to a special case of Rockafellar's proximal point algorithm 
Theorem 5.1 of Wang et al.  holds true only in the finite-dimensional setting. This is because in the infinite-dimensional setting, a bounded sequence fails, in general, to have a norm-convergent subsequence. As a matter of fact, in the infinite-dimensional case, the special case of (2.2) where and corresponds to Rockafellar's proximal point algorithm (2.11) which fails to converge in the norm topology, in general, in the infinite-dimensional setting; see Güler's counterexample . This infinite-dimensionality problem occurred in several papers by Noor (see, e.g., [16–26]).
In the infinite-dimensional setting, whether or not Wang et al.'s implicit algorithm (2.2) converges even in the weak topology remains an open question. We will provide a partial answer by showing that if the operator is weak-to-strong continuous (i.e., takes weakly convergent sequences to strongly convergent sequences), then the implicit algorithm (2.2) does converge weakly.
We next collect the (correct) results proved in .
Since algorithm (2.2) is, in general, not strongly convergent, we turn to investigate its weak convergence. It is however unclear if the algorithm is weakly convergent (if the space is infinite dimensional). We present a partial answer below. But first recall that an operator is said to be weak-to-strong continuous if the weak convergence of a sequence to a point implies the strong convergence of the sequence to the point .
which is in turn equivalent to the fixed point equation
then MMVI (1.3) is reduced to the classical variational inequality (VI)
Noor  proved a convergence result for his algorithm (3.6) as follows.
Theorem 3.1 (see [27, page 38]).
We however found that the conclusion stated in the above theorem is incorrect. It is true that solves MMVI (1.3) if and only if solves the fixed point equation (3.2). The reason that led Noor to his mistake is his claim that solves MMVI (1.3) if and only if solves the following iterated fixed point equation:
(We therefore conclude that equation is not equivalent to MMVI (1.3), as claimed by Noor .)
Now take the initial guess for . Then and we have that algorithm (3.6) generates a constant sequence for all . However, is not a solution of MMVI (3.11). This shows that algorithm (3.6) may generate a sequence that fails to converge to a solution of MMVI (1.3) and Noor's result in  is therefore false.
The authors are grateful to the anonymous referees for their comments and suggestions which improved the presentation of this manuscript. This paper is dedicated to Professor Wataru Takahashi on the occasion of his retirement. The second author supported in part by NSC 97-2628-M-110-003-MY3, and by DGES MTM2006-13997-C02-01.
- Brezis H: Operateurs Maximaux Monotones et Semi-Groups de Contraction dans les Espaces de Hilbert. North-Holland, Amsterdam, The Netherlands; 1973.MATHGoogle Scholar
- Baiocchi C, Capelo A: Variational and Quasivariational Inequalities: Applications to Free Boundary Problems, A Wiley-Interscience Publication. John Wiley & Sons, New York, NY, USA; 1984:ix+452.Google Scholar
- Cottle RW, Giannessi F, Lions JL: Variational Inequalities and Complementarity Problems: Theory and Applications. John Wiley & Sons, New York, NY, USA; 1980.Google Scholar
- Glowinski R, Lions J-L, Trémolières R: Numerical Analysis of Variational Inequalities, Studies in Mathematics and Its Applications. Volume 8. North-Holland, Amsterdam, The Netherlands; 1981:xxix+776.Google Scholar
- Giannessi F, Maugeri A: Variational Inequalities and Network Equilibrium Problems. Plenum Press, New York, NY, USA; 1995.View ArticleMATHGoogle Scholar
- Kinderlehrer D, Stampacchia G: An Introduction to Variational Inequalities and Their Applications, Pure and Applied Mathematics. Volume 88. Academic Press, New York, NY, USA; 1980:xiv+313.MATHGoogle Scholar
- Wang SL, Yang H, He B: Inexact implicit method with variable parameter for mixed monotone variational inequalities. Journal of Optimization Theory and Applications 2001,111(2):431–443. 10.1023/A:1011942620208MathSciNetView ArticleMATHGoogle Scholar
- He B: Inexact implicit methods for monotone general variational inequalities. Mathematical Programming 1999,86(1):199–217. 10.1007/s101070050086MathSciNetView ArticleMATHGoogle Scholar
- Han D, He B: A new accuracy criterion for approximate proximal point algorithms. Journal of Mathematical Analysis and Applications 2001,263(2):343–354. 10.1006/jmaa.2001.7535MathSciNetView ArticleMATHGoogle Scholar
- Eckstein J, Bertsekas DP: On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators. Mathematical Programming 1992,55(3):293–318. 10.1007/BF01581204MathSciNetView ArticleMATHGoogle Scholar
- Rockafellar RT: Monotone operators and the proximal point algorithm. SIAM Journal on Control and Optimization 1976,14(5):877–898. 10.1137/0314056MathSciNetView ArticleMATHGoogle Scholar
- Solodov MV, Svaiter BF: Forcing strong convergence of proximal point iterations in a Hilbert space. Mathematical Programming, Series A 2000,87(1):189–202.MathSciNetMATHGoogle Scholar
- Xu H-K: Iterative algorithms for nonlinear operators. Journal of the London Mathematical Society 2002,66(1):240–256. 10.1112/S0024610702003332MathSciNetView ArticleMATHGoogle Scholar
- Marino G, Xu H-K: Convergence of generalized proximal point algorithms. Communications on Pure and Applied Analysis 2004,3(4):791–808.MathSciNetView ArticleMATHGoogle Scholar
- Güler O: On the convergence of the proximal point algorithm for convex minimization. SIAM Journal on Control and Optimization 1991,29(2):403–419. 10.1137/0329022MathSciNetView ArticleMATHGoogle Scholar
- Noor MA: Monotone mixed variational inequalities. Applied Mathematics Letters 2001,14(2):231–236. 10.1016/S0893-9659(00)00141-5MathSciNetView ArticleMATHGoogle Scholar
- Noor MA: An implicit method for mixed variational inequalities. Applied Mathematics Letters 1998,11(4):109–113. 10.1016/S0893-9659(98)00066-4MathSciNetView ArticleMATHGoogle Scholar
- Noor MA: A modified projection method for monotone variational inequalities. Applied Mathematics Letters 1999,12(5):83–87. 10.1016/S0893-9659(99)00061-0MathSciNetView ArticleMATHGoogle Scholar
- Noor MA: Some iterative techniques for general monotone variational inequalities. Optimization 1999,46(4):391–401. 10.1080/02331939908844464MathSciNetView ArticleMATHGoogle Scholar
- Noor MA: Some algorithms for general monotone mixed variational inequalities. Mathematical and Computer Modelling 1999,29(7):1–9. 10.1016/S0895-7177(99)00058-8MathSciNetView ArticleMATHGoogle Scholar
- Noor MA: Splitting algorithms for general pseudomonotone mixed variational inequalities. Journal of Global Optimization 2000,18(1):75–89. 10.1023/A:1008322118873MathSciNetView ArticleMATHGoogle Scholar
- Noor MA: An iterative method for general mixed variational inequalities. Computers & Mathematics with Applications 2000,40(2–3):171–176. 10.1016/S0898-1221(00)00151-6MathSciNetView ArticleMATHGoogle Scholar
- Noor MA: Splitting methods for pseudomonotone mixed variational inequalities. Journal of Mathematical Analysis and Applications 2000,246(1):174–188. 10.1006/jmaa.2000.6776MathSciNetView ArticleMATHGoogle Scholar
- Noor MA: A class of new iterative methods for general mixed variational inequalities. Mathematical and Computer Modelling 2000,31(13):11–19. 10.1016/S0895-7177(00)00108-4MathSciNetView ArticleMATHGoogle Scholar
- Noor MA: Solvability of multivalued general mixed variational inequalities. Journal of Mathematical Analysis and Applications 2001,261(1):390–402. 10.1006/jmaa.2001.7533MathSciNetView ArticleMATHGoogle Scholar
- Noor MA, Al-Said EA: Wiener-Hopf equations technique for quasimonotone variational inequalities. Journal of Optimization Theory and Applications 1999,103(3):705–714. 10.1023/A:1021796326831MathSciNetView ArticleMATHGoogle Scholar
- Noor MA: Iterative schemes for quasimonotone mixed variational inequalities. Optimization 2001,50(1–2):29–44. 10.1080/02331930108844552MathSciNetView ArticleMATHGoogle Scholar
- Clarke FH: Optimization and Nonsmooth Analysis, Classics in Applied Mathematics. Volume 5. 2nd edition. SIAM, Philadelphia, Pa, USA; 1990:xii+308.View ArticleMATHGoogle Scholar
- Noor MA: An extraresolvent method for monotone mixed variational inequalities. Mathematical and Computer Modelling 1999,29(3):95–100. 10.1016/S0895-7177(99)00033-3MathSciNetView ArticleMATHGoogle Scholar
- Noor MA: A modified extragradient method for general monotone variational inequalities. Computers & Mathematics with Applications 1999,38(1):19–24. 10.1016/S0898-1221(99)00164-9MathSciNetView ArticleMATHGoogle Scholar
- Noor MA: Projection type methods for general variational inequalities. Soochow Journal of Mathematics 2002,28(2):171–178.MathSciNetMATHGoogle Scholar
- Noor MA: Modified projection method for pseudomonotone variational inequalities. Applied Mathematics Letters 2002,15(3):315–320. 10.1016/S0893-9659(01)00137-9MathSciNetView ArticleMATHGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.