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Article

A Class of Novel Mann-Type Subgradient Extragradient Algorithms for Solving Quasimonotone Variational Inequalities

by
Nopparat Wairojjana
1,
Ioannis K. Argyros
2,*,
Meshal Shutaywi
3,
Wejdan Deebani
3 and
Christopher I. Argyros
4
1
Applied Mathematics Program, Faculty of Science and Technology, Valaya Alongkorn Rajabhat University under the Royal Patronage (VRU), 1 Moo 20 Phaholyothin Road, Klong Neung, Klong Luang, Pathumthani 13180, Thailand
2
Department of Mathematical Sciences, Cameron University, Lawton, OK 73505, USA
3
Department of Mathematics College of Science & Arts, King Abdulaziz University, P.O. Box 344, Rabigh 21911, Saudi Arabia
4
Department of Computer Science, University of Oklahoma, Norman, OK 73071, USA
*
Author to whom correspondence should be addressed.
Symmetry 2021, 13(7), 1108; https://doi.org/10.3390/sym13071108
Submission received: 14 April 2021 / Revised: 26 May 2021 / Accepted: 5 June 2021 / Published: 22 June 2021
(This article belongs to the Section Mathematics)

Abstract

:
Symmetries play an important role in the dynamics of physical systems. As an example, quantum physics and microworld are the basis of symmetry principles. These problems are reduced to solving inequalities in general. That is why in this article, we study the numerical approximation of solutions to variational inequality problems involving quasimonotone operators in an infinite-dimensional real Hilbert space. We prove that the iterative sequences generated by the proposed iterative schemes for solving variational inequalities with quasimonotone mapping converge strongly to some solution. The main advantage of the proposed iterative schemes is that they use a monotone and non-monotone step size rule based on operator knowledge rather than a Lipschitz constant or some line search method. We present a number of numerical experiments for the proposed algorithms.

1. Introduction

Our main concern here is to study the different iterative algorithms that are used to evaluate the numerical solution of the variational inequality problem (shortly, (1)) involving quasimonotone operators in any real Hilbert space E . Let E be a real Hilbert space and K be a nonempty closed and convex subset of a real Hilbert space E . Let T : E E be an operator. The variational inequality problem for T on  K is defined as follows [1,2]:
Find u * K such that T ( u * ) , y u * 0 , y K
In order to prove the strong convergence, it is assumed that the following conditions are satisfied:
( T 1)
The solution set for the problem (1) is denoted by V I ( K , T ) and it is nonempty;
( T 2)
A mapping T : E E is said to be quasimonotone if
T ( u 1 ) , u 2 u 1 > 0 T ( u 2 ) , u 1 u 2 0 , u 1 , u 2 K ;
( T 3)
A mapping T : E E is said to be Lipschitz continuous if there exists a constant L > 0 such that
T ( u 1 ) T ( u 2 ) L u 1 u 2 , u 1 , u 2 K ;
( T 4)
A mapping T : E E is said to be weakly sequentially continuous if { T ( u n ) } weakly converges to  T ( u ) for each sequence { u n } that weakly converges to  u .
It is well-established that the problem (1) is a key problem in nonlinear analysis. It is an important mathematical model that incorporates many important topics in pure and applied mathematics, such as a nonlinear system of equations, optimization conditions for problems with the optimization process, complementarity problems, network equilibrium problems and finance (see [3,4,5,6,7,8,9,10,11,12,13] and others in [14,15,16,17,18,19]). As a result, this problem has a number of applications in engineering, mathematical programming, network economics, transportation analysis, game theory and software engineering. Moreover, symmetries play an important role in the dynamics of physical systems. The basis of symmetry principles, for example, is quantum physics and the microworld. These problems are reduced to solving inequalities in general.
Most solution techniques for problem (1) are iterative since analytic or explicit solutions are hard to find. The class of regularized methods and the projection methods are two well-known and common techniques for evaluating a numerical solution to variational inequalities. It should also be noted that the first approach is most commonly used to solve the variational inequalities involving the class of monotone operators. In the class of regularized methods, the regularized sub-problem is strongly monotone and its unique solution exists. In this study, we focus on projection methods that are well-known and widely used due to their simple numerical computations.
The gradient projection method was the first well-established projection method for solving variational inequalities, and it was followed by several other projection methods, including the well-known extragradient method [20], the subgradient extragradient method [21,22] and others in [23,24,25,26,27]. These methods are used to solve variational inequalities involving strongly monotone, monotone or inverse monotone operators. These methods have serious flaws. The first is the constant step size, which requires knowledge or approximation of the Lipschitz constant of the relevant operator and only converges weakly in Hilbert spaces. In most cases, the Lipschitz constants are undefined or impossible to compute. Estimating the Lipschitz constant a priori may be difficult from a computational point of view, which may affect the convergence rate and applicability of the method.
The objective of this study is to solve the variational inequalities involving a quasimonotone operator in an infinite-dimensional Hilbert space. We prove that the proposed iterative sequence generated by the subgradient extragradient algorithms for solving variational inequalities involving quasimonotone operators converges strongly to a solution. Subgradient extragradient algorithms use both the monotone and the new non-monotone variable step size rule. For the proposed algorithms, we present a set of computational experiments.
The paper is arranged in the following way. In Section 2, some basic results are presented. Section 3 presents two new algorithms and proves their convergence analysis. Finally, Section 4 presents some numerical results to explain the practical efficiency of the proposed methods.

2. Preliminaries

For each u , y E , the following inequality holds:
u + y 2 = u 2 + 2 u , y + y 2 .
A metric projection  P K ( u 1 ) of  u 1 E is defined by:
P K ( u 1 ) = arg min { u 1 u 2 : u 2 K } .
Lemma 1.
[28] For any u 1 , u 2 E and R . Then:
(i)
u 1 + ( 1 ) u 2 2 = u 1 2 + ( 1 ) u 2 2 ( 1 ) u 1 u 2 2 .
(ii)
u 1 + u 2 2 u 1 2 + 2 u 2 , u 1 + u 2 .
Lemma 2.
[29] Assume that { p n } [ 0 , + ) is a sequence satisfying the following inequality:
p n + 1 ( 1 q n ) p n + q n r n , n N .
Moreover, two sequences { q n } ( 0 , 1 ) and { r n } R meet the following conditions:
lim n + q n = 0 , n = 1 + q n = + and lim sup n + r n 0 .
Then, lim n + p n = 0 .
Lemma 3.
[30] Let { p n } be a sequence of real numbers and there exists a subsequence { n i } of  { n } such that
p n i < p n i + 1 , i N .
Then, there exists a nondecreasing sequence m k N such that m k + as  k + , and the following inequality for k N is satisfied:
p m k p m k + 1 and p k p m k + 1 .
Indeed, m k = max { j k : p j p j + 1 } .

3. Main Results

In this section, we propose two new methods for solving quasimonotone variational inequalities in real Hilbert spaces and prove strong convergence theorems. Both methods use the monotonic self-adaptive step rule to make the method independent of the Lipschitz constant.
Lemma 4.
A sequence { γ n }  generated by (2) is convergent and monotonically decreasing.
Proof. 
Due to the Lipschitz continuity mapping T , there exists a real fixed number L > 0 . Suppose that T ( u n ) T ( y n ) , z n y n > 0 , such that:
μ ( u n y n 2 + z n y n 2 ) 2 T ( u n ) T ( y n ) , z n y n 2 μ u n y n z n y n 2 T ( u n ) T ( y n ) z n y n 2 μ u n y n z n y n 2 L u n y n z n y n μ L .
We can easily see that the sequence { γ n } is monotonically decreasing and bounded. Hence, sequence { γ n } is convergent to some γ > 0 .    □
Lemma 5.
Let a mapping T : E E satisfy the conditions ( T 1)( T 4) and a sequence { u n } be generated by Algorithm 1. For each u * V I ( K , T ) , we have:
z n u * 2 u n u * 2 1 μ γ n γ n + 1 u n y n 2 1 μ γ n γ n + 1 z n y n 2 .
Algorithm 1 (Monotonic Explicit Mann-Type Subgradient Extragradient Method)
  • Step 0: Choose u 1 K , μ ( 0 , 1 ) and γ 1 > 0 . Moreover, { α n } ( a , b ) ( 0 , 1 β n ) and { β n } ( 0 , 1 ) satisfying the following conditions:
    lim n + β n = 0 and n = 1 + β n = + .
  • Step 1: Compute
    y n = P K ( u n γ n T ( u n ) ) .
    If u n = y n , then STOP. Otherwise, go to Step 2.
  • Step 2: First construct a set
    E n = { z E : u n γ n T ( u n ) y n , z y n 0 }
    and compute
    z n = P E n ( u n γ n T ( y n ) ) .
  • Step 3: Compute
    u n + 1 = ( 1 α n β n ) u n + α n z n .
  • Step 4: Compute
    γ n + 1 = min γ n , μ u n y n 2 + μ z n y n 2 2 T ( u n ) T ( y n ) , z n y n if T ( u n ) T ( y n ) , z n y n > 0 , γ n , otherwise .
    Set n : = n + 1 and go back to Step 1.
Proof. 
Consider that:
z n u * 2 = P E n [ u n γ n T ( y n ) ] u * 2 = P E n [ u n γ n T ( y n ) ] + [ u n γ n T ( y n ) ] [ u n γ n T ( y n ) ] u * 2 = [ u n γ n T ( y n ) ] u * 2 + P E n [ u n γ n T ( y n ) ] [ u n γ n T ( y n ) ] 2 + 2 P E n [ u n γ n T ( y n ) ] [ u n γ n T ( y n ) ] , [ u n γ n T ( y n ) ] u * .
Since u * V I ( K , T ) K E n , we have:
P E n [ u n γ n T ( y n ) ] [ u n γ n T ( y n ) ] 2 + P E n [ u n γ n T ( y n ) ] [ u n γ n T ( y n ) ] , [ u n γ n T ( y n ) ] u * = [ u n γ n T ( y n ) ] P E n [ u n γ n T ( y n ) ] , u * P E n [ u n γ n T ( y n ) ] 0 .
The above expression implies that:
P E n [ u n γ n T ( y n ) ] [ u n γ n T ( y n ) ] , [ u n γ n T ( y n ) ] u * P E n [ u n γ n T ( y n ) ] [ u n γ n T ( y n ) ] 2 .
From Expressions (3) and (5), we obtain:
z n u * 2 u n γ n T ( y n ) u * 2 P E n [ u n γ n T ( y n ) ] [ u n γ n T ( y n ) ] 2 u n u * 2 u n z n 2 + 2 γ n T ( y n ) , u * z n .
Since u * V I ( K , T ) , we have:
T ( u * ) , y u * 0 , y K .
Due to the definition of  T on  K , we obtain:
T ( y n ) , y n u * 0 .
Thus, we have:
T ( y n ) , u * z n = T ( y n ) , u * y n + T ( y n ) , y n z n T ( y n ) , y n z n .
Combining Expressions (6) and (7), we obtain:
z n u * 2 u n u * 2 u n z n 2 + 2 γ n T ( y n ) , y n z n u n u * 2 u n y n + y n z n 2 + 2 γ n T ( y n ) , y n z n u n u * 2 u n y n 2 y n z n 2 + 2 u n γ n T ( y n ) y n , z n y n .
Now, taking z n = P E n [ u n γ n T ( y n ) ] we have:
2 u n γ n T ( y n ) y n , z n y n = 2 u n γ n T ( u n ) y n , z n y n + 2 γ n T ( u n ) T ( y n ) , z n y n γ n γ n + 1 2 γ n + 1 T ( u n ) T ( y n ) , z n y n μ γ n γ n + 1 u n y n 2 + μ γ n γ n + 1 z n y n 2 .
Combining Expressions (8) and (9), we obtain:
z n u * 2 u n u * 2 u n y n 2 y n z n 2 + γ n γ n + 1 μ u n y n 2 + μ z n y n 2 u n u * 2 1 μ γ n γ n + 1 u n y n 2 1 μ γ n γ n + 1 z n y n 2 .
   □
Lemma 6.
Let T : E E be an operator that satisfies the conditions ( T 1)( T 4). If there exists a subsequence { u n k } weakly convergent to  u ^ and lim k u n k y n k = 0 , then u ^ V I ( K , T ) .
Proof. 
Since { u n k } is weakly convergent to  u ^ and due to  lim k u n k y n k = 0 , the sequence { y n k } is also weakly convergent to  u ^ . Next, we need to prove that u ^ V I ( K , T ) . By the value of  y n , we have:
y n k = P K [ u n k γ n k T ( u n k ) ]
that is equivalent to 
u n k γ n k T ( u n k ) y n k , y y n k 0 , y K .
The above inequality implies that:
u n k y n k , y y n k γ n k T ( u n k ) , y y n k , y K .
Thus, we obtain:
1 γ n k u n k y n k , y y n k + T ( u n k ) , y n k u n k T ( u n k ) , y u n k , y K .
By the use of  lim k u n k y n k = 0 and k in (13), we have:
lim inf k T ( u n k ) , y u n k 0 , y K .
Furthermore, it implies that:
T ( y n k ) , y y n k = T ( y n k ) T ( u n k ) , y u n k + T ( u n k ) , y u n k + T ( y n k ) , u n k y n k .
Since lim k u n k y n k = 0 . Thus, we have:
lim k T ( u n k ) T ( y n k ) = 0 ,
and together with (15) and (16), we obtain:
lim inf k T ( y n k ) , y y n k 0 , y K .
Moreover, let us take a positive sequence { ϵ k } that is decreasing and convergent to zero. For each { ϵ k } there exists a least positive integer denoted by m k such that
T ( u n i ) , y u n i + ϵ k > 0 , i m k .
Since { ϵ k } is a decreasing sequence and it is easy to see that the sequence { m k } is increasing, if there exists a natural number N 0 N such that for all T ( u n m k ) 0 , n m k N 0 . Consider that:
Υ n m k = T ( u n m k ) T ( u n m k ) 2 , n m k N 0 .
Due to the above definition, we have:
T ( u n m k ) , Υ n m k = 1 , n m k N 0 .
Moreover, from Expressions (18) and (20) for all n m k N 0 , we have:
T ( u n m k ) , y + ϵ k Υ n m k u n m k > 0 .
By the definition of quasimonotone, we have:
T ( y + ϵ k Υ n m k ) , y + ϵ k Υ n m k u n m k > 0 .
For all n m k N 0 , we have
T ( y ) , y u n m k T ( y ) T ( y + ϵ k Υ n m k ) , y + ϵ k Υ n m k u n m k ϵ k T ( y ) , Υ n m k .
Due to the fact that  { u n k } converges weakly to  u ^ K with T being weakly sequentially continuous on the set K we obtain that  { T ( u n k ) } converges weakly to  T ( u ^ ) . Let T ( u ^ ) 0 ; it implies that:
T ( u ^ ) lim inf k T ( u n k ) .
Since { u n m k } { u n k } and lim k ϵ k = 0 , we have:
0 lim k ϵ k Υ n m k = lim k ϵ k T ( u n m k ) 0 T ( u ^ ) = 0 .
By letting k in (23), we obtain:
T ( y ) , y u ^ 0 , y K .
Let u K be an arbitrary element and for 0 < λ 1 , let:
u ^ λ = λ u + ( 1 λ ) u ^ .
Then u ^ λ K and from (26) we have:
λ T ( u ^ λ ) , u u ^ 0 .
Hence:
T ( u ^ λ ) , u u ^ 0 .
Let λ 0 . Then u ^ λ u ^ along a line segment. By the continuity of an operator, T ( u ^ λ ) converges to  T ( u ^ ) as  λ 0 . It follows from (29) that:
T ( u ^ ) , u u ^ 0 .
Therefore u ^ is a solution of problem (1).    □
Theorem 1.
Let T : E E be an operator that satisfies the conditions( T 1)( T 4). Then, { u n } generated by Algorithm 1 converges strongly to  u * = P V I ( K , T ) ( 0 ) .
Proof. 
By Lemma 5, we have:
z n u * 2 u n u * 2 , n n 1 .
The above expression is obtained due to  γ n γ and there exists a number ϵ ( 0 , 1 μ ) , such that:
lim n 1 μ γ n γ n + 1 = 1 μ > ϵ > 0 .
Thus, there exits a number n 1 N such that
1 μ γ n γ n + 1 > ϵ > 0 , n n 1 .
It is given that u * V I ( K , T ) , we have:
u n + 1 u * = ( 1 α n β n ) u n + α n z n u * = ( 1 α n β n ) ( u n u * ) + α n ( z n u * ) β n u * ( 1 α n β n ) ( u n u * ) + α n ( z n u * ) + β n u * .
Next, we need to compute the following:
( 1 α n β n ) ( u n u * ) + α n ( z n u * ) 2 = ( 1 α n β n ) 2 u n u * 2 + α n 2 z n u * 2 + 2 ( 1 α n β n ) ( u n u * ) , α n ( z n u * ) ( 1 α n β n ) 2 u n u * 2 + α n 2 z n u * 2 + 2 α n ( 1 α n β n ) u n u * z n u *
( 1 α n β n ) 2 u n u * 2 + α n 2 z n u * 2 + α n ( 1 α n β n ) u n u * 2 + α n ( 1 α n β n ) z n u * 2 ( 1 α n β n ) ( 1 β n ) u n u * 2 + α n ( 1 β n ) z n u * 2 .
Substituting (31) into (35), we obtain:
( 1 α n β n ) ( u n u * ) + α n ( z n u * ) 2 ( 1 α n β n ) ( 1 β n ) u n u * 2 + α n ( 1 β n ) u n u * 2 = ( 1 β n ) 2 u n u * 2 .
The above expression implies that:
( 1 α n β n ) ( u n u * ) + α n ( z n u * ) ( 1 β n ) u n u * .
Combining Expressions (33) and (37), we obtain:
u n + 1 u * ( 1 β n ) u n u * + β n u * max u n u * , u * } max u n 1 u * , u * } .
Thus, the above expression implies that { u n } is a bounded sequence. Indeed, by the use of the definition of  { u n + 1 } , we have:
u n + 1 u * 2 = ( 1 α n β n ) u n + α n z n u * 2 = ( 1 α n β n ) ( u n u * ) + α n ( z n u * ) β n u * 2 = ( 1 α n β n ) ( u n u * ) + α n ( z n u * ) 2 + β n 2 u * 2 2 ( 1 α n β n ) ( u n u * ) + α n ( z n u * ) , β n u * .
By the use of Expression (35), we have:
( 1 α n β n ) ( u n u * ) + α n ( z n u * ) 2 ( 1 α n β n ) ( 1 β n ) u n u * 2 + α n ( 1 β n ) z n u * 2 .
Combining Expressions (39) and (40) (for some K 2 > 0 ), we obtain:
u n + 1 u * 2 ( 1 α n β n ) ( 1 β n ) u n u * 2 + α n ( 1 β n ) z n u * 2 + β n K 2 ( 1 α n β n ) ( 1 β n ) u n u * 2 + β n K 2 + α n ( 1 β n ) u n u * 2 1 μ γ n γ n + 1 u n y n 2 1 μ γ n γ n + 1 z n y n 2 = ( 1 β n ) 2 u n u * 2 + β n K 2 α n ( 1 β n ) 1 μ γ n γ n + 1 u n y n 2 + 1 μ γ n γ n + 1 z n y n 2 u n u * 2 + β n K 2 α n ( 1 β n ) 1 μ γ n γ n + 1 u n y n 2 + 1 μ γ n γ n + 1 z n y n 2 .
Assume that Π n = ( 1 α n ) u n + α n z n . Thus, we obtain:
u n + 1 = Π n β n u n = ( 1 β n ) Π n β n ( u n Π n ) = ( 1 β n ) Π n β n α n ( u n z n ) .
where u n Π n = u n ( 1 α n ) u n α n z n = α n ( u n z n ) . Thus, we obtain:
u n + 1 u * 2 = ( 1 β n ) Π n + α n β n ( z n u n ) u * 2 = ( 1 β n ) ( Π n u * ) + α n β n ( z n u n ) β n u * 2 ( 1 β n ) 2 Π n u * 2 + 2 α n β n ( z n u n ) β n u * , ( 1 β n ) ( Π n u * ) + α n β n ( z n u n ) β n u * = ( 1 β n ) 2 Π n u * 2 + 2 α n β n ( z n u n ) β n u * , Π n β n Π n β n ( u n Π n ) u * = ( 1 β n ) Π n u * 2 + 2 α n β n z n u n , u n + 1 u * + 2 β n u * , u * u n + 1 ( 1 β n ) Π n u * 2 + 2 α n β n z n u n u n + 1 u * + 2 β n u * , u * u n + 1 .
Next, we need to evaluate:
Π n u * 2 = ( 1 α n ) u n + α n z n u * 2 = ( 1 α n ) ( u n u * ) + α n ( z n u * ) 2 = ( 1 α n ) 2 u n u * 2 + α n 2 z n u * 2 + 2 ( 1 α n ) ( u n u * ) , α n ( z n u * ) ( 1 α n ) 2 u n u * 2 + α n 2 z n u * 2 + 2 α n ( 1 α n ) u n u * z n u * ( 1 α n ) 2 u n u * 2 + α n 2 z n u * 2 + α n ( 1 α n ) u n u * 2 + α n ( 1 α n ) z n u * 2 = ( 1 α n ) u n u * 2 + α n z n u * 2 ( 1 α n ) u n u * 2 + α n u n u * 2 = u n u * 2 .
Combining Expressions (43) and (44), we obtain:
u n + 1 u * 2 ( 1 β n ) u n u * 2 + β n [ 2 α n z n u n u n + 1 u * + 2 u * , u * u n + 1 .
Case 1: Suppose that there exists a fixed number n 2 N ( n 2 n 1 ) such that:
u n + 1 u * u n u * , n n 2 .
Thus, lim n u n u * exists. By the use of Expression (41), we have:
α n ( 1 β n ) 1 μ γ n γ n + 1 u n y n 2 + 1 μ γ n γ n + 1 z n y n 2 u n u * 2 + β n K 2 u n + 1 u * 2 .
By the use of limit existence of  lim n + u n u * and β n 0 , we can deduce that:
lim n u n y n = lim n z n y n = 0 .
Consequently, we have:
lim n u n z n lim n u n y n + lim n y n z n = 0 .
It follows from Expression (49) and β n 0 that:
u n + 1 u n = ( 1 α n β n ) u n + α n z n u n = u n β n u n + α n z n α n u n u n α n z n u n + β n u n ,
which gives that
u n + 1 u n 0 as n + .
It is given that u * = P V I ( K , T ) ( 0 ) ; we thus have:
0 u * , y u * 0 , y V I ( K , T ) .
Since { u n } is bounded sequence and thus there exists a subsequence { u n k } that weakly converges to some u ^ E . By using Lemma 6, we have
lim sup n u * , u * u n = lim sup k u * , u * u n k = u * , u * u ^ 0 .
By the use of  lim n u n + 1 u n = 0 . It follows that:
lim sup n u * , u * u n + 1 lim sup n u * , u * u n + lim sup n u * , u n u n + 1 0 .
By the use of Expressions (45), (54) and Lemma 2 we deduce that u n u * 0 as  n + .
Case 2: Suppose that there exits a subsequence { n i } of  { n } such that:
u n i u * u n i + 1 u * , i N .
By Lemma 3 there exists a sequence { m k } N ( { m k } ) such that
u m k u * u m k + 1 u * and u k u * u m k + 1 u * , k N .
From Expression (41), we have:
α m k ( 1 β m k ) 1 μ γ m k γ m k + 1 u m k y m k 2 + 1 μ γ m k γ m k + 1 z m k y m k 2 u m k u * 2 + β m k K 2 u m k + 1 u * 2 .
Due to  β m k 0 we can deduce the following:
lim k u m k y m k = lim k z m k y m k = 0 .
It follows that:
lim k u m k z m k lim k u m k y m k + lim k y m k z m k = 0 .
It continues from that:
u m k + 1 u m k = ( 1 α m k β m k ) u m k + α m k z m k u m k = u m k β m k u m k + α m k z m k α m k u m k u m k α m k z m k u m k + β m k u m k 0 .
By using a similar argument as in Case 1, we have
lim sup k u * , u m k + 1 u * 0 .
By using Expressions (45) and (55), we have:
u m k + 1 u * 2 ( 1 β m k ) u m k u * 2 + β m k [ 2 α m k z m k u m k u m k + 1 u * + 2 u * , u * u m k + 1 ( 1 β m k ) u m k + 1 u * 2 + β m k [ 2 α m k z m k u m k u m k + 1 u * + 2 u * , u * u m k + 1 .
It follows that:
u m k + 1 u * 2 2 α m k z m k u m k u m k + 1 u * + 2 β m k u * , u * u m k + 1 .
Since β m k 0 and u m k u * is bounded. Thus, we have:
u m k + 1 u * 2 0 , as k .
By using Expressions (55) and (63), we obtain:
lim k u k u * 2 lim k u m k + 1 u * 2 0 .
As a result u n u * as  n + . This completes the proof of the theorem.    □
Theorem 2.
Let a mapping T : E E satisfy the conditions( T 1)( T 4). Then, the sequence { u n } generated by the Algorithm 2 converges strongly to  u * = P V I ( K , T ) ( 0 ) .
Algorithm 2 (Inertial Monotonic Explicit Subgradient Extragradient Method)
  • Step 0: Choose u 0 , u 1 K , μ ( 0 , 1 ) and γ 1 > 0 . Moreover, { β n } ( 0 , 1 ) satisfies the following conditions:
    lim n + β n = 0 and n = 1 + β n = + .
  • Step 1: Compute
    w n = u n + ρ n ( u n u n 1 ) β n u n + ρ n ( u n u n 1 )
    where ρ n such that
    0 ρ n ρ n ^ and ρ n ^ = min ρ 2 , ϵ n u n u n 1 if u n u n 1 , ρ 2 otherwise ,
    where ϵ n = ( β n ) is a positive sequence such that lim n + ϵ n β n = 0 .
  • Step 1: Compute
    y n = P K ( w n γ n T ( w n ) ) .
    If w n = y n , then STOP. Otherwise, go to Step 2.
  • Step 2: First construct a set
    E n = { z E : w n γ n T ( w n ) y n , z y n 0 }
    and compute
    u n + 1 = P E n ( w n γ n T ( y n ) ) .
  • Step 3: Compute
    γ n + 1 = min γ n , μ w n y n 2 + μ u n + 1 y n 2 2 T ( w n ) T ( y n ) , u n + 1 y n if T ( w n ) T ( y n ) , u n + 1 y n > 0 , γ n , otherwise .
    Set n : = n + 1 and go back to Step 1.
Proof. 
By using the definition of  { w n } we obtain:
w n u * = u n + ρ n ( u n u n 1 ) β n u n ρ n β n ( u n u n 1 ) u * = ( 1 β n ) ( u n u * ) + ( 1 β n ) ρ n ( u n u n 1 ) β n u * ( 1 β n ) u n u * + ( 1 β n ) ρ n u n u n 1 + β n u *
( 1 β n ) u n u * + β n K 1 ,
where
( 1 β n ) ρ n β n u n u n 1 + u * K 1 .
Given γ n γ , it implies that there exists a fixed number ( 0 , 1 μ ) such that:
lim n 1 μ γ n γ n + 1 = 1 μ > > 0 .
Thus, there exists a finite number N 1 N such that:
1 μ γ n γ n + 1 > > 0 , n N 1 .
By using Lemma 5, we have:
u n + 1 u * 2 w n u * 2 , n N 1 .
From Expressions (68) and (70), we have:
u n + 1 u * ( 1 β n ) u n u * + β n K 1 max u n u * , K 1 max u N 1 u * , K 1 .
Thus, we can infer that { u n } is a bounded sequence. Indeed, from Expression (68), we have:
w n u * 2 ( 1 β n ) 2 u n u * 2 + β n 2 K 1 2 + 2 K 1 β n ( 1 β n ) u n u * u n u * 2 + β n β n K 1 2 + 2 K 1 ( 1 β n ) u n u * u n u * 2 + β n K 2 ,
for some K 2 > 0 . Combining Expressions (10) and (72), we have:
u n + 1 u * 2 u n u * 2 + β n K 2 1 μ γ n γ n + 1 w n y n 2 1 μ γ n γ n + 1 u n + 1 y n 2 .
From Expression (67), we can write:
w n u * 2 = u n + ρ n ( u n u n 1 ) β n u n ρ n β n ( u n u n 1 ) u * 2 = ( 1 β n ) ( u n u * ) + ( 1 β n ) ρ n ( u n u n 1 ) β n u * 2 ( 1 β n ) ( u n u * ) + ( 1 β n ) ρ n ( u n u n 1 ) 2 + 2 β n u * , w n u * = ( 1 β n ) 2 u n u * 2 + ( 1 β n ) 2 ρ n 2 u n u n 1 2 + 2 ρ n ( 1 β n ) 2 u n u * u n u n 1 + 2 β n u * , w n u n + 1 + 2 β n u * , u n + 1 u * ( 1 β n ) u n u * 2 + ρ n 2 u n u n 1 2 + 2 ρ n ( 1 β n ) u n u * u n u n 1 + 2 β n u * w n u n + 1 + 2 β n u * , u n + 1 u * = ( 1 β n ) u n u * 2 + β n [ ρ n u n u n 1 ρ n β n u n u n 1 + 2 ( 1 β n ) u n u * ρ n β n u n u n 1 + 2 u * w n u n + 1 + 2 u * , u * u n + 1 ] .
From Expressions (70) and (74), we obtain:
u n + 1 u * 2 ( 1 β n ) u n u * 2 + β n [ ρ n u n u n 1 ρ n β n u n u n 1 + 2 ( 1 β n ) u n u * ρ n β n u n u n 1 + 2 u * w n u n + 1 + 2 u * , u * u n + 1 ] .
Case 1: Assume that there exists a fixed number N 2 N ( N 2 N 1 ) such that
u n + 1 u * u n u * , n N 2 .
The above relation implies that lim n u n u * exists and let lim n u n u * = l , for l 0 . By use of Expression (73), we have:
1 μ γ n γ n + 1 w n y n 2 + 1 μ γ n γ n + 1 u n + 1 y n 2 u n u * 2 + β n K 2 u n + 1 u * 2 .
Due to the existence of the limit of  u n u * and β n 0 , we deduce that:
w n y n 0 and u n + 1 y n 0 as n .
It continues from Expression (78) that:
lim n w n u n + 1 lim n w n y n + lim n y n u n + 1 = 0 .
Following that, we evaluate
w n u n = u n + ρ n ( u n u n 1 ) β n u n + ρ n ( u n u n 1 ) u n ρ n u n u n 1 + β n u n + ρ n β n u n u n 1 = β n ρ n β n u n u n 1 + β n u n + β n 2 ρ n β n u n u n 1 0 .
The above expression implies that:
lim n u n u n + 1 lim n u n w n + lim n w n u n + 1 = 0 .
It is given that u * = P V I ( K , T ) ( 0 ) , we thus have:
0 u * , y u * 0 , y V I ( K , T ) .
By using Lemma 6, we have:
lim sup n u * , u * u n = lim k u * , u * u n k = u * , u * u ^ 0 ,
by using the fact that lim n u n + 1 u n = 0 . Thus, using Expression (83) we obtain: 
lim sup n u * , u * u n + 1 lim sup n u * , u * u n + lim sup n u * , u n u n + 1 0 .
By the use of Expressions (75), (84) and Lemma 2 we deduce that u n u * 0 as  n + .
Case 2: Consider that there exists a subsequence { n i } of  { n } such that
u n i u * u n i + 1 u * , i N .
By using Lemma 3 there exists a sequence { m k } N as  { m k } such that
u m k u * u m k + 1 u * and u k u * u m k + 1 u * , for all k N .
Similar to Case 1, the expression (77) implies that:
1 μ γ m k γ m k + 1 w m k y m k 2 + 1 μ γ m k γ m k + 1 u m k + 1 y m k 2 u m k u * 2 + β m k K 2 u m k + 1 u * 2 .
Due to  β m k 0 , we can infer the following:
lim k w m k y m k = lim k u m k + 1 y m k = 0 .
Furthermore, it follows that:
lim k u m k + 1 w m k lim k u m k + 1 y m k + lim k y m k w m k = 0 .
Following that, we must evaluate
w m k u m k = u m k + α m k ( u m k u m k 1 ) β m k u m k + α m k ( u m k u m k 1 ) u m k α m k u m k u m k 1 + β m k u m k + α m k β m k u m k u m k 1 = β m k α m k β m k u m k u m k 1 + β m k u m k + β m k 2 α m k β m k u m k u m k 1 0 .
The above expression implies that:
lim k u m k u m k + 1 lim k u m k w m k + lim k w m k u m k + 1 = 0 .
By following the same argument as in Case 1, such that
lim sup k u * , u * u m k + 1 0 .
Combining Expressions (75) and (85), we obtain:
u m k + 1 u * 2 ( 1 β m k ) u m k u * 2 + β m k [ α m k u m k u m k 1 α m k β m k u m k u m k 1 + 2 ( 1 β m k ) u m k u * α m k β m k u m k u m k 1 + 2 u * w m k u m k + 1 + 2 u * , u * u m k + 1 ] ( 1 β m k ) u m k + 1 u * 2 + β m k [ α m k u m k u m k 1 α m k β m k u m k u m k 1 + 2 ( 1 β m k ) u m k u * α m k β m k u m k u m k 1 + 2 u * w m k u m k + 1 + 2 u * , u * u m k + 1 ] .
Thus, we obtain: 
u m k + 1 u * 2 [ α m k u m k u m k 1 α m k β m k u m k u m k 1 + 2 ( 1 β m k ) u m k u * α m k β m k u m k u m k 1 + 2 u * w m k u m k + 1 + 2 u * , u * u m k + 1 ] ,
since β m k 0 and u m k u * is a bounded sequence. Thus, following Expressions (91) and (93), we obtain:
u m k + 1 u * 2 0 as k .
The above expression implies that:
lim n u k u * 2 lim n u m k + 1 u * 2 0
as a result the sequence u n u * as  n + . This completes the proof of the theorem.    □
Next, we introduce the other variants of Algorithms 1 and 2 in which the constant step size λ is chosen adaptively and thus produces a non-monotone step size sequence λ n that does not require the knowledge of the Lipschitz-type constant L .
Lemma 7.
A sequence { γ n } generated by (99) is convergent to γ and satisfies the following inequality:
min μ L , γ 1 γ n γ 1 + P where P = n = 1 + φ n .
Proof. 
The Lipschitz continuity of a mapping T and T ( u n ) T ( y n ) , z n y n > 0 implies that:
μ ( u n y n 2 + z n y n 2 ) 2 T ( u n ) T ( y n ) , z n y n 2 μ u n y n z n y n 2 T ( u n ) T ( y n ) z n y n 2 μ u n y n z n y n 2 L u n y n z n y n μ L .
By using the mathematical induction on the definition of  γ n + 1 , we have:
min μ L , γ 1 γ n γ 1 + P .
Let
[ γ n + 1 γ n ] + = max 0 , γ n + 1 γ n
and
[ γ n + 1 γ n ] = max 0 , ( γ n + 1 γ n ) .
By the definition of  { γ n } , we have:
n = 1 + ( γ n + 1 γ n ) + = n = 1 + max 0 , γ n + 1 γ n P < + .
That is, the series n = 1 + ( γ n + 1 γ n ) + is convergent. Next, we need to prove the convergence of  n = 1 + ( γ n + 1 γ n ) . Let n = 1 + ( γ n + 1 γ n ) = + . Due to the reason that γ n + 1 γ n = ( γ n + 1 γ n ) + ( γ n + 1 γ n ) , we have:
γ k + 1 γ 1 = n = 0 k ( γ n + 1 γ n ) = n = 0 k ( γ n + 1 γ n ) + n = 0 k ( γ n + 1 γ n ) .
By letting k + in (98), we have γ k as  k + . This is a contradiction. Due to the convergence of the series n = 0 k ( γ n + 1 γ n ) + and n = 0 k ( γ n + 1 γ n ) taking k + in (98) we obtain  lim n γ n = γ . This completes the proof of the theorem.    □
Theorem 3.
Let a mapping T : E E satisfy the conditions( T 1)( T 4). Then, { u n } generated by the Algorithm 3 converges strongly to a solution  u * = P V I ( K , T ) ( 0 ) .
Proof. 
The proof is the same as for the proof of Theorem 1.    □
Algorithm 3 (Non-Monotonic Explicit Mann-Type Subgradient Extragradient Method)
  • Step 0: Let u 1 K , γ 1 > 0 , μ ( 0 , 1 ) and choose a nonnegative real sequence { φ n } such that n φ n < + . Moreover, { α n } ( a , b ) ( 0 , 1 β n ) and { β n } ( 0 , 1 ) , satisfying the following conditions:
    lim n + β n = 0 and n = 1 + β n = + .
  • Step 1: Compute
    y n = P K ( u n γ n T ( u n ) ) .
    If u n = y n , then STOP. Otherwise, go to Step 2.
  • Step 2: Firstly construct a half-space
    E n = { z E : u n γ n T ( u n ) y n , z y n 0 }
    and compute
    z n = P E n ( u n γ n T ( y n ) ) .
  • Step 3: Compute
    u n + 1 = ( 1 α n β n ) u n + α n z n .
  • Step 4: Compute
    γ n + 1 = min γ n + φ n , μ u n y n 2 + μ z n y n 2 2 T ( u n ) T ( y n ) , z n y n if T ( u n ) T ( y n ) , z n y n > 0 , γ n + φ n , otherwise .
    Set n : = n + 1 and go back to Step 1.
Theorem 4.
Let a mapping T : E E satisfy the conditions( T 1)( T 4). Then, { u n } generated by Algorithm 4 converges strongly to a solution  u * = P V I ( K , T ) ( 0 ) .
Proof. 
The proof is the same as for the proof of Theorem 2.    □
Algorithm 4 (Inertial Non-Monotonic Explicit Subgradient Extragradient Method)
  • Step 0: Let u 0 , u 1 K , μ ( 0 , 1 ) , γ 1 > 0 and choose a nonnegative real sequence { φ n } such that n φ n < + . Moreover, { β n } ( 0 , 1 ) , satisfying the following conditions:
    lim n + β n = 0 and n = 1 + β n = + .
  • Step 1: Compute
    w n = u n + ρ n ( u n u n 1 ) β n u n + ρ n ( u n u n 1 )
    where ρ n such that
    0 ρ n ρ n ^ and ρ n ^ = min ρ 2 , ϵ n u n u n 1 if u n u n 1 , ρ 2 otherwise ,
    where ϵ n = ( β n ) is a positive sequence such that lim n + ϵ n β n = 0 .
  • Step 1: Compute
    y n = P K ( w n γ n T ( w n ) ) .
    If w n = y n , then STOP. Otherwise, go to Step 2.
  • Step 2: First construct a half-space
    E n = { z E : w n γ n T ( w n ) y n , z y n 0 }
    and compute
    u n + 1 = P E n ( w n γ n T ( y n ) ) .
  • Step 3: Compute
    γ n + 1 = min γ n + φ n , μ w n y n 2 + μ u n + 1 y n 2 2 T ( w n ) T ( y n ) , u n + 1 y n if T ( w n ) T ( y n ) , u n + 1 y n > 0 , γ n + φ n , otherwise .
    Set n : = n + 1 and go back to Step 1.

4. Numerical Illustrations

This section describes the numerical performance of the proposed algorithms, in contrast to some related work in the literature, as well as the analysis of how variations in control parameters affect the numerical effectiveness of the proposed algorithms. All computations were carried out in MATLAB R2018b and run on an HP i-5 Core(TM)i5-6200 8.00 GB (7.78 GB usable) RAM laptop.
Example 1.
Let E = l 2 be a real Hilbert space with with the sequences of real numbers satisfying the following condition:
u 1 2 + u 2 2 + + u n 2 + < + .
Assume that a mapping T : K K is defined by
G ( u ) = ( 5 u ) u , u E
where K = { u E : u 3 } . We can easily see that T is weakly sequentially continuous on E and the solution set is V I ( K , T ) = { 0 } . For any u , y E , we have:
T ( u ) T ( y ) = ( 5 u ) u ( 5 y ) y = 5 ( u y ) u ( u y ) ( u y ) y 5 u y + u u y + | u y | y 5 u y + 3 u y + 3 u y 11 u y .
Hence T is L-Lipschitz continuous with L = 11 . For any u , y E and let T ( u ) , y u > 0 , such that
( 5 u ) u , y u > 0 ,
since u 3 , and it implies that
u , y u > 0 .
Consider that
T ( y ) , y u = ( 5 y ) y , y u ( 5 y ) y , y u ( 5 y ) u , y u 2 u y 2 0 .
Hence a mapping T is quasimonotone on K . Let u = ( 5 2 , 0 , 0 , , 0 , ) and y = ( 3 , 0 , 0 , , 0 , ) such that
T ( u ) T ( y ) , u y = 5 2 3 3 < 0 .
Let us consider the following projection formula:
P K ( u ) = u if u 3 , 3 u u , otherwise .
The numerical results for Example 1 are shown in Figure 1, Figure 2, Figure 3, Figure 4, Figure 5 and Figure 6 and Table 1Table 2. The control conditions are taken in the following way:
(i)
Algorithm 1 (shortly, Algorithm 1):
γ 1 = 0.22 , μ = 0.44 , β n = 1 ( n + 2 ) , α n = 1 2 ( 1 β n ) ;
(ii)
Algorithm 3 (shortly, Algorithm 3):
γ 1 = 0.22 , μ = 0.44 , β n = 1 ( n + 2 ) , α n = 1 2 ( 1 β n ) , φ n = 100 ( n + 1 ) 2 ;
(iii)
Algorithm 2 (shortly, Algorithm 2):
γ 1 = 0.22 , μ = 0.44 , ρ = 0.50 , β n = 1 ( n + 2 ) , ϵ n = 1 ( n + 1 ) 2 ;
(iv)
Algorithm 4 (shortly, Algorithm 4):
γ 1 = 0.22 , μ = 0.44 , ρ = 0.50 , β n = 1 ( n + 2 ) , ϵ n = 1 ( n + 1 ) 2 , φ n = 100 ( n + 1 ) 2 .

5. Conclusions

We formulated four explicit extragradient-type methods to find a numerical solution to the quasimonotone variational inequalities in a real Hilbert space. These methods are considered as a modification of the two-step extragradient-type method. Two strongly convergent results have been proven corresponding to the proposed methods. The numerical results were examined in order to verify the numerical advantage of the proposed algorithms. Such computational results show that the non-monotone variable step size rule continues to improve the effectiveness of the iterative sequence in this context.

Author Contributions

Conceptualization, N.W., I.K.A., M.S., W.D. and C.I.A.; methodology, N.W., I.K.A. and C.I.A.; software, N.W., I.K.A. and M.S.; validation, M.S., W.D. and C.I.A.; formal analysis, N.W., I.K.A. and C.I.A.; investigation, N.W., I.K.A. and W.D.; writing—original draft preparation, N.W., I.K.A., M.S., W.D. and C.I.A.; writing—review and editing, N.W., I.K.A., M.S. and W.D.; visualization, I.K.A., M.S. and C.I.A.; supervision and funding, N.W., I.K.A. and C.I.A. All authors have read and agree to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

We would like to thank the associate editor and referee(s) for his/her comments and suggestions on the manuscript. Nopparat Wairojjana would like to thank Valaya Alongkorn Rajabhat University under the Royal Patronage (VRU).

Conflicts of Interest

The authors declare no competing interests.

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Figure 1. Numerical illustration of Algorithms 1–4 while u 1 = ( 1 , 1 , , 1 5000 , 0 , 0 , ) .
Figure 1. Numerical illustration of Algorithms 1–4 while u 1 = ( 1 , 1 , , 1 5000 , 0 , 0 , ) .
Symmetry 13 01108 g001
Figure 2. Numerical illustration of Algorithms 1–4 while u 1 = ( 1 , 1 , , 1 5000 , 0 , 0 , ) .
Figure 2. Numerical illustration of Algorithms 1–4 while u 1 = ( 1 , 1 , , 1 5000 , 0 , 0 , ) .
Symmetry 13 01108 g002
Figure 3. Numerical illustration of Algorithms 1–4 while u 1 = ( 1 , 2 , , 5000 , 0 , 0 , ) .
Figure 3. Numerical illustration of Algorithms 1–4 while u 1 = ( 1 , 2 , , 5000 , 0 , 0 , ) .
Symmetry 13 01108 g003
Figure 4. Numerical illustration of Algorithms 1–4 while u 1 = ( 1 , 2 , , 5000 , 0 , 0 , ) .
Figure 4. Numerical illustration of Algorithms 1–4 while u 1 = ( 1 , 2 , , 5000 , 0 , 0 , ) .
Symmetry 13 01108 g004
Figure 5. Numerical illustration of Algorithms 1–4 while u 1 = ( 5 , 5 , , 5 5000 , 0 , 0 , ) .
Figure 5. Numerical illustration of Algorithms 1–4 while u 1 = ( 5 , 5 , , 5 5000 , 0 , 0 , ) .
Symmetry 13 01108 g005
Figure 6. Numerical illustration of Algorithms 1–4 while u 1 = ( 5 , 5 , , 5 5000 , 0 , 0 , ) .
Figure 6. Numerical illustration of Algorithms 1–4 while u 1 = ( 5 , 5 , , 5 5000 , 0 , 0 , ) .
Symmetry 13 01108 g006
Table 1. Numerical values for Example 1.
Table 1. Numerical values for Example 1.
Number of IterationsExecution Time in Seconds
u 1 Algorithm 1Algorithm 3Algorithm 1Algorithm 3
( 1 , 1 , , 1 5000 , 0 , 0 , ) 53434.312284800000003.35517350000000
( 1 , 2 , , 5000 , 0 , 0 , ) 69585.623107900000004.71896740000000
( 5 , 5 , , 5 10000 , 0 , 0 , ) 58414.845709400000003.57478350000000
Table 2. Numerical values for Example 1.
Table 2. Numerical values for Example 1.
Number of IterationsExecution Time in Seconds
u 0 = u 1 Algorithm 2Algorithm 4Algorithm 2Algorithm 4
( 1 , 1 , , 1 5000 , 0 , 0 , ) 19141.711268300000001.12075030000000
( 1 , 2 , , 5000 , 0 , 0 , ) 19141.719558500000001.11015910000000
( 5 , 5 , , 5 10000 , 0 , 0 , ) 19141.724443400000001.12638850000000
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Wairojjana, N.; Argyros, I.; Shutaywi, M.; Deebani, W.; Argyros, C.I. A Class of Novel Mann-Type Subgradient Extragradient Algorithms for Solving Quasimonotone Variational Inequalities. Symmetry 2021, 13, 1108. https://doi.org/10.3390/sym13071108

AMA Style

Wairojjana N, Argyros I, Shutaywi M, Deebani W, Argyros CI. A Class of Novel Mann-Type Subgradient Extragradient Algorithms for Solving Quasimonotone Variational Inequalities. Symmetry. 2021; 13(7):1108. https://doi.org/10.3390/sym13071108

Chicago/Turabian Style

Wairojjana, Nopparat, Ioannis K. Argyros, Meshal Shutaywi, Wejdan Deebani, and Christopher I. Argyros. 2021. "A Class of Novel Mann-Type Subgradient Extragradient Algorithms for Solving Quasimonotone Variational Inequalities" Symmetry 13, no. 7: 1108. https://doi.org/10.3390/sym13071108

APA Style

Wairojjana, N., Argyros, I., Shutaywi, M., Deebani, W., & Argyros, C. I. (2021). A Class of Novel Mann-Type Subgradient Extragradient Algorithms for Solving Quasimonotone Variational Inequalities. Symmetry, 13(7), 1108. https://doi.org/10.3390/sym13071108

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