In this paper, we presented two developments, the first deals with generalizing the modernization of the damped Dai-Liao formula in an optimized manner. The second development is by suggesting a monotone line search formula for our new algorithms. These new algorithms with the new monotone line search yielded good numerical results when compared to the standard Dai-Liao (DL) and minimum description length (MDL) algorithms. Through several theorems, the new algorithms proved to have a faster convergence to reach the optimum point. These comparisons are drawn in the results section for the tools (Iter, Eval-F, Time) which are a comprehensive measure of the efficiency of the new algorithms with the basic algorithms that we used in the paper.
A generalized Dai-Liao type CG-method with a new monotone line search for unconstrained optimization
1. TELKOMNIKA Telecommunication Computing Electronics and Control
Vol. 21, No. 3, June 2023, pp. 545~555
ISSN: 1693-6930, DOI: 10.12928/TELKOMNIKA.v21i3.21685 ο² 545
Journal homepage: http://telkomnika.uad.ac.id
A generalized Dai-Liao type CG-method with a new monotone
line search for unconstrained optimization
Rana Z. Al-Kawaz1
, Abbas Y. Al-Bayati2
1
Department of Mathematics, College of Basic Education, University of Telafer, Tall βAfar, Mosul, Iraq
2
College of Basic Education, University of Telafer, Tall βAfar, Mosul, Iraq
Article Info ABSTRACT
Article history:
Received Sep 07, 2022
Revised Oct 27, 2022
Accepted Nov 12, 2022
In this paper, we presented two developments, the first deals with
generalizing the modernization of the damped Dai-Liao formula in an
optimized manner. The second development is by suggesting a monotone
line search formula for our new algorithms. These new algorithms with the
new monotone line search yielded good numerical results when compared to
the standard Dai-Liao (DL) and minimum description length (MDL)
algorithms. Through several theorems, the new algorithms proved to have a
faster convergence to reach the optimum point. These comparisons are
drawn in the results section for the tools (Iter, Eval-F, Time) which are a
comprehensive measure of the efficiency of the new algorithms with the
basic algorithms that we used in the paper.
Keywords:
Dai-Liao CG-method
Global convergence property
Large-scale
Monotone line search
Optimum point This is an open access article under the CC BY-SA license.
Corresponding Author:
Rana Z. Al-Kawaz
Department of Mathematics, College of Basic Education, University of Telafer
Tall βAfar, Mosul, Iraq
Email: rana.alkawaz@yahoo.com
1. INTRODUCTION
Let us define the function (1):
πΉ: πΊ β βπ
β βπ
(1)
Then the issue that we discuss in this article is (2):
πΉ(π₯) = 0, π₯ β πΊ (2)
When the solution vector π₯ β βπ
and the function πΉ is continuous and satisfy the monotonous inequality i.e.,
[πΉ(π₯) β πΉ(π¦)]π
(π₯ β π¦) β₯ 0 (3)
There are several ways to solve this problem, such as the Newton method, and the procedure of
conjugating gradients [1], [2]. Applications around this type of method have continued to this day [3]-[5]. Here,
we talk about monotonous equations and their solving methods for their importance in many practical
applications. For more details see [6]. The projection technique is one of the important methods used to find the
solution to (1). The two researchers Solodov and Svaiter [7] gave their attention to the large-scale non-linear
equations. Recently, many researchers implemented new articles on the topic of finding solutions to some
constraints or unconstraint monotony equations. in various methods, so it had an aspect of interest as in [8]-[15].
The projection technique relies on being accelerated using a monotone case πΉ and updating the new point
using repetition:
2. ο² ISSN: 1693-6930
TELKOMNIKA Telecommun Comput El Control, Vol. 21, No. 3, June 2023: 545-555
546
π§π = π₯π + πΌπππ (4)
As an initial iterative and the hyperplane is:
π»π = {π₯ β βπ|πΉ(π§π)π(π₯ β π§π) = 0} (5)
To start using the projection technique, we use the update of the new point π₯π+1 as given in the [6]
to be the projection of π₯π onto the hyperplane π»π. So, can be evaluated (6).
π₯π+1 = ππΊ[π₯π β πππΉ(π§π)] and ππ =
πΉ(π§π)π(π₯πβπ§π)
βπΉ(π§π)β2 (6)
Specifically, this document is laid out: specifically, we present the suggested generalized damped
Dai-Liao (GDDL) method in section 2. A novel monotone line search was proposed, and the penalty of
generalized Dai-Liao (DL) parameters was determined in section 3. Section 4 proves global convergence.
In section 5 we provide the results of our numerical experiments.
2. GENERALIZED DAMPED DAI-LIAO
βThe thinking of many researchers was concerned with finding a suitable parameter for the method
of the conjugate gradient (CG) and imposing conditions on it to make the search direction conjugate and
reach the smallest point of the function faster and limited steps. One of these researchers was Dai-Liao who
provided an appropriate parameter that always makes the direction of the search in a state accompanied by a
parameter π β (0,1), for more details see [16].
π½π
π·πΏ
=
ππ+1
π
π¦π
ππ
ππ¦π
β π
ππ+1
π
π π
ππ
ππ¦π
(7)
Later Abubakar et al. [17] modified (6) by using the new formula of π‘ and the projection technique in
their formula. Fatemi [18] presented a generalized method for the Dai-Liao parameter and its derivation by
applying the penalty function to the parameter to achieve a sufficient descent condition in the search direction.
In this section we will rely on the same previous techniques with the addition of a damped quasi-Newton
condition as in the following derivation:
π(π) = ππ+1 + ππ+1
π
π +
1
2
ππ
π΅π+1π (8)
With π»π(πΌπ+1ππ+1), the gradient of the model in π₯π+2, as an estimation of ππ+2. It is easy to see that:
π»π(πΌπ+1ππ+1) = ππ+1 + πΌπ+1π΅π+1π (9)
Unfortunately, πΌπ+1 in (4) is not available in the current iteration, because ππ+1 is unknown. Thus,
we modified (4) and set.
ππ+2 = ππ+1 + π‘ π΅π+1π (10)
Where π‘ > 0 is a suitable approximation of πΌπ+1 . If the search direction of the CG-method.
ππ+1 = βππ+1 + π½πππ (11)
In an efficient nonlinear CG method, we introduce the following optimization problem based on the
penalty function:
πππ
π½π
[ππ+1
π
ππ+1 + π β [(ππ+2
π
π πβπ)2
+ (ππ+1
π
π¦πβπ)2]
π
π=1 ] (12)
If π = 1, 2, 3, 4, 5, . . , π. Now, substituting (10), (11) in (12), and using the projection technique we obtain:
πππ
π½π
[ββπΉπ+1β2
+ π½πππ+1
π
ππ + π β [(πΉπ+1
π
π πβπ)2
+ 2π‘πΉπ+1
π
π πβπ ππ+1
π
π΅π+1π πβπ + π‘2
(ππ+1
π
π΅π+1π πβπ)
2
+
π
π=1
(πΉπ+1
π
π¦πβπ)2
β 2π½ππΉπ+1
π
π¦πβπππ
π
π¦πβπ + (π½πππ
π
π¦πβπ)2
]] (13)
3. TELKOMNIKA Telecommun Comput El Control ο²
A generalized Dai-Liao type CG-method with a new monotone line search for β¦ (Rana Z. Al-Kawaz)
547
After some algebraic abbreviations, we get the:
π½π =
1
π
[βπΉπ+1
π
ππ + 2π β πΉπ+1
π
π¦πβπππ
π
π¦πβπ
π
π=1 + 2π‘2
π β πΉπ+1
π
π΅π+1π πβπππ
π
π΅π+1π πβπ
π
π=1 β
2π‘π β πΉπ+1
π
π πβπ
π
π=1 ππ
π
π΅π+1π πβπ] (14)
Where:
π = 2π‘2
π β (ππ
π
π΅π+1π πβπ)2
π
π=1 + 2π β (ππ
π
π¦πβπ)2
π
π=1
To get a new parameter, let us assume that the Hessian approximation π΅π+1 to satisfy the extended
damped quasi-Newton (QN) equation and with the incorporation of the use of projection technology we get:
π΅π+1π πβπ =
1
ππ
(πππ¦πβπ + (1 β ππ)π΅ππ πβπ) =
1
ππ
π¦πβπ
π·
= π¦
Μ πβπ
π·
(15)
And ππ is the projection step.
ππ = {
1 ππ π πβπ
π
π¦πβπ β₯ π πβπ
π
π΅ππ πβπ
π π πβπ
π
π΅ππ πβπ
π πβπ
π π΅ππ πβπβπ πβπ
π π¦πβπ
ππ π πβπ
π
π¦πβπ < π πβπ
π
π΅ππ πβπ
(16)
Then,
π½π
πππ€
=
βπΉπ+1
π
ππ
2π β (π‘2(ππ
ππ¦
Μ πβπ
π· )
2
+(ππ
ππ¦πβπ)
2
)
π
π=1
+
β πΉπ+1
π
π¦πβπππ
π
π¦πβπ
π
π=1
β (π‘2(ππ
ππ¦
Μ πβπ
π· )
2
+(ππ
ππ¦πβπ)
2
)
π
π=1
+
π‘2 β πΉπ+1
π
π¦
Μ πβπ
π·
ππ
π
π¦
Μ πβπ
π·
π
π=1
β (π‘2(ππ
ππ¦
Μ πβπ
π· )
2
+(ππ
ππ¦πβπ)
2
)
π
π=1
β
π‘ β πΉπ+1
π
π πβπ
π
π=1 ππ
π
π¦
Μ πβπ
π·
β (π‘2(ππ
ππ¦
Μ πβπ
π· )
2
+(ππ
ππ¦πβπ)
2
)
π
π=1
(17)
So, there are two possible scenarios for this parameter such that, case I: if π πβπ
π
π¦πβπ β₯ π πβπ
π
π΅ππ πβπ then ππ = 1
and π¦
Μ πβπ
π·
=
π¦πβπ
ππ
.
π½π
πππ€1
=
βπΉπ+1
π
ππ
2π β (
π‘2
ππ
2(ππ
ππ¦πβπ)
2
+(ππ
ππ¦πβπ)
2
)
π
π=1
+
β πΉπ+1
π
π¦πβπππ
π
π¦πβπ
π
π=1
β (
π‘2
ππ
2(ππ
ππ¦πβπ)
2
+(ππ
ππ¦πβπ)
2
)
π
π=1
+
π‘2
ππ
2 β πΉπ+1
π
π¦πβπππ
π
π¦πβπ
π
π=1
β (
π‘2
ππ
2(ππ
ππ¦πβπ)
2
+(ππ
ππ¦πβπ)
2
)
π
π=1
β
π‘
ππ
β πΉπ+1
π
π πβπ
π
π=1 ππ
π
π¦πβπ
β (
π‘2
ππ
2(ππ
ππ¦πβπ)
2
+(ππ
ππ¦πβπ)
2
)
π
π=1
(18)
Put π π = ππππ.
π½π
πππ€1
=
β πΉπ+1
π
π¦πβπππ
π
π¦πβπ
π
π=1
β (ππ
ππ¦πβπ)
2
π
π=1
β
πππ‘
(π‘2+1)
β πΉπ+1
π
π πβπ
π
π=1 ππ
π
π¦πβπ
β (ππ
ππ¦πβπ)
2
π
π=1
β
πππΉπ+1
π
π π
2π1(π‘2+1) β (ππ
ππ¦πβπ)
2
π
π=1
(19a)
To investigate the proposed method when π1 approaches infinity, because by making this coefficient
larger, we penalize the conjugacy condition and the orthogonality property violations more severely, thereby
forcing the minimizer of (10) closer to that of the linear CG method.
π½π
πππ€1
=
β πΉπ+1
π
π¦πβπππ
π
π¦πβπ
π
π=1
β (ππ
ππ¦πβπ)
2
π
π=1
β
πππ‘
(π‘2+1)
β πΉπ+1
π
π πβπ
π
π=1 ππ
π
π¦πβπ
β (ππ
ππ¦πβπ)
2
π
π=1
(19b)
When compared to (6), we notice that the value is:
ππ =
πππ‘
(π‘2+1)
, π β (0,
1
2
) (20)
Case II: if π πβπ
π
π¦πβπ < π πβπ
π
π΅ππ πβπ then π¦
Μ πβπ
π·
=
π¦πβπ
π·
ππ
from (12) and π π = ππππ by projection, the technique to
convert the π½π
πππ€
form to:
4. ο² ISSN: 1693-6930
TELKOMNIKA Telecommun Comput El Control, Vol. 21, No. 3, June 2023: 545-555
548
π½π
πππ€2
= [
π‘2 β πΉπ+1
π
π¦πβπ
π·
ππ
π
π¦πβπ
π·
π
π=1
β (π‘2(ππ
ππ¦πβπ
π· )
2
+ππ
2(ππ
ππ¦πβπ)
2
)
π
π=1
β
π‘ππ β πΉπ+1
π
π πβπππ
π
π¦πβπ
π·
π
π=1
β (π‘2(ππ
ππ¦πβπ
π· )
2
+ππ
2(ππ
ππ¦πβπ)
2
)
π
π=1
] + [
β πΉπ+1
π
π¦πβπππ
π
π¦πβπ
π
π=1
β (π‘2(ππ
ππ¦πβπ
π· )
2
+ππ
2(ππ
ππ¦πβπ)
2
)
π
π=1
β
ππ πΉπ+1
π
π π
2π2 β (π‘2(ππ
ππ¦πβπ
π· )
2
+ππ
2(ππ
ππ¦πβπ)
2
)
π
π=1
] (21)
Now using algebraic simplifications, we obtain:
π½π
πππ€2
=
1
ππ (β [π‘1πΉπ+1
π
π¦πβπ β π‘2πΉπ+1
π
π πβπ]
π
π=1 + β
ππ
π
π πβπ
ππ
ππ¦πβπ
π
π=1 β [π‘3πΉπ+1
π
π¦πβπ β π‘4πΉπ+1
π
π πβπ]
π
π=1 β
ππ
2π2 β ππ
ππ¦πβπ
π
π=1
πΉπ+1
π
π π) (22a)
i.e.,
ππ
= β (π‘2(ππ
π
π¦πβπ
π·
)2
+ ππ
2(ππ
π
π¦πβπ)2)
π
π=1
π‘1 = π‘2
ππ
2
+ ππ
2
and π‘2 = π‘ ππππ β π‘2
ππ(1 β ππ)
π‘3 = π‘2
ππ(1 β ππ) and π‘4 = π‘ ππ(1 β ππ) β π‘2
(1 β ππ)2
)
As we talked about (π1) then (π2) when you come close to infinity, then we use the parameter
omitted from this limit:
π½π
πππ€2
=
1
ππ (β [π‘1πΉπ+1
π
π¦πβπ β π‘2πΉπ+1
π
π πβπ]
π
π=1 + β
ππ
π
π πβπ
ππ
ππ¦πβπ
π
π=1 β [π‘3πΉπ+1
π
π¦πβπ β π‘4πΉπ+1
π
π πβπ]
π
π=1 )(22b)
To obtain better results as in section 5. We have another paper on this type of method, but without
generalizing the original formula [17].β
3. NEW PENALTY PARAMETER
βDue to the importance of concomitant gradient methods in this paper, we highlight them in the
derivation of new algorithms. Now, we will derive more coefficients of the penalty function. Although, in this
formula, we will focus on the two new parameters defined in the (19) and (22) respectively, then we will check
and update the derived parameters by relying on the appropriate direction of regression of the CG method:
3.1. Lemma 1
Assume that the sequence of the solution generated by the method (19) with monotone line search,
then for a few positive scalars πΏ1 and πΏ2 satisfying πΏ1 + πΏ2 < 1, we have:
πΉπ+1
π
ππ+1 β€ β(1 β πΏ1 β πΏ2)βπΉπ+1β2
(23)
When:
|πππ‘ β 1| β€ β
2 πΏ2(π¦π
ππ π)
βπ πβ β βπ πβπβ
π
π=1
(24)
π1 =
2 πΏ1βπΉπ+1β2
π(π‘2+1) πππ₯
π=1,..,π
((π¦πβπβ0.5 πππ πβπ)ππΉπ+1)
2;ππ β€ 1 (25)
Proof: if we used (5) and (14) then:
πΉπ+1
π
ππ+1 = ββπΉπ+1β2
+
1
β (π¦πβπ
π π π)
2
π
π=1
((πΉπ+1
π
π¦π)(π¦π
π
π π)(πΉπ+1
π
π π) β
πππ‘
(π‘2+1)
(πΉπ+1
π
π π)(π¦π
π
π π)(πΉπ+1
π
π π)) β
ππ
2π1(π‘2+1)
(πΉπ+1
π
π π)
2
β (π¦πβπ
π π π)
2
π
π=1
(26)
Since ππ β€ 1, implies that:
8. ο² ISSN: 1693-6930
TELKOMNIKA Telecommun Comput El Control, Vol. 21, No. 3, June 2023: 545-555
552
Table 1. Number of initial points
Name of variable Number
π₯1 (1,1,1, . . ,1)π
π₯2 (rand, rand, rand, . . , rand)π
Table 2. Information of test functions [20]-[26]
Name of functions Details Reference
πΉ1 πΉπ(π₯) = 2 π₯π β sin|π₯π| [21]
πΉ2 πΉπ(π₯) = π₯π β sin(π₯π) [21]
πΉ3 πΉπ(π₯) = ππ₯π β 1 [22]
πΉ4 πΉπ(π₯) = βπ(π₯1 β 1), πππ π = 2,3, . . , π β 1
πΉπ(π₯) =
1
4π
β π₯π
2
π
π=1
β
1
4
πππ π = 1 β 10β5
[22]
πΉ5 πΉπ(π₯) = ln( |π₯π| + 1) β
π₯π
π
[20]
πΉ6 πΉπ(π₯) = min(min(|π₯π|, π₯π
2
) , max(|π₯π|,π₯π
3
)) [23]
πΉ7
πΉ1(π₯) = π₯1 β π
cos(π₯1+π₯2)
π+1
πΉπ(π₯) = π₯π β π
cos(π₯π+1+π₯π+π₯πβ1)
π+1 , πππ π = 2,3, . . , π β 1
πΉπ(π₯) = π₯π β π
cos(π₯πβ1+π₯π)
π+1
[24]
πΉ8
πΉπ(π₯) =
π
π
ππ₯π β 1
[21]
πΉ9 πΉ1(π₯) = ππ₯1 β 1, πΉπ(π₯) = ππ₯π β π₯πβ1 β 1 [20]
πΉ10
πΉπ(π₯) = β|π₯π|π
π
π=1
[25]
πΉ11
πΉπ(π₯) = β|π₯π|
π
π=1
[25]
πΉ12 πΉπ(π₯) = max
π=1,..,π
|π₯π| [25]
πΉ13
πΉπ(π₯) = β|π₯π|
π
π=1
πβ β sin(π₯π
2
)
π
π=1
[25]
πΉ14
πΉπ(π₯) = β|π₯π sin(π₯π) + 0.1 (π₯π)|
π
π=1
[26]
πΉ15
πΉπ(π₯) = β|π₯π|π+1
π
π=1
[26]
The π-dimensional versions of these techniques are implemented here (1000, 2000, 5000, 7000,
12000). The stopping criterion is βπΉ(π₯π)β < 10β8
. All algorithms of this kind may be distinguished from
one another based on their performance in terms of (Iter): the number of iterations, (Eval-F): the number of
evaluations of functions, (Time): in seconds measured by the CPU, and (Norm): the approximate solution
norm. In Table 2, we mention the details of the test problems πΉ(π₯) = (π1, π2, π3, β¦ , ππ)π
used with the
references from which they were taken, the points π₯ = (π₯1, π₯2, π₯3, β¦ , π₯π)π
, and πΊ = β+
π
.
(a) (b)
Figure 1. Performance of the seven algorithms with respect to (Iter): (a) about π₯1 and (b) about π₯2
9. TELKOMNIKA Telecommun Comput El Control ο²
A generalized Dai-Liao type CG-method with a new monotone line search for β¦ (Rana Z. Al-Kawaz)
553
Using style for figures as in [19], the following three figures are for comparison between the old DL
and MDL algorithms with the new algorithm when switching the value of the generalization i.e., from 1 to 5 to
get 5 new algorithms. New algorithms are considered generalizations and can generalize to more than 5.
To accurately know the impact of algorithms, we drew the following figures.
Figure 1 shows the effect of the new algorithms when taking the iterations tool as a measure of the effect,
and the figure was divided into two parts, i.e., Figure 1(a) when calculating the point π₯1, and Figure 1(b) when
calculating the point π₯2. As for Figure 2, it shows the effect of the new algorithms when taking the function
number calculation tool as a measure of the effect, and the figure was divided into two parts, i.e., Figure 2(a) when
calculating the point π₯1, and Figure 2(b) when calculating the point π₯2. Finally, Figure 3 shows the effect of the
new algorithms when taking the time spent in calculations tool as a measure of impact, i.e., Figure 3(a) when
calculating the point π₯1, and Figure 3(b) when calculating the point π₯2.
(a) (b)
Figure 2. Performance of the seven algorithms with respect to (Eval-F): (a) about π₯1 and (b) about π₯2
(a) (b)
Figure 3. Performing the seven algorithms with respect to (Time): (a) about π₯1 and (b) about π₯2
Through the use of the three figures, we can conclude that the new algorithms are superior to the
two algorithms with which we compared our work. Furthermore, we have discovered that the random starting
point with which we began our work brought to light the significance of the new algorithm when
generalizing π = 2 when calculating the number of iterations and was the best of the new algorithms when
calculating the number of times, the function is calculated. The algorithm with the value of π equal to one
performed the best and was superior to the others. In conclusion, when considering the amount of effort
invested in developing these algorithms, the two algorithms with π equal to 5 performed the best.
10. ο² ISSN: 1693-6930
TELKOMNIKA Telecommun Comput El Control, Vol. 21, No. 3, June 2023: 545-555
554
6. CONCLUSION
Our numerical results in the previous section, represented by three numbers, show the efficiency of
the generalized algorithm GDDL when compared with the previous two algorithms. Its efficiency is better
when we take the randomly generated point as a starting point and this will increase the efficiency when the
dimensions used in the variables increase, which shows greater stability and makes the new generalized
algorithm more suitable than other previous algorithms for the existence of the limit containing the penalty
parameter in the new generalized algorithm. Theoretical proofs of the new algorithm give the proposed
algorithms more power than the previous algorithms. This is why these algorithms are considered successful
in both theoretical and numerical aspects.
ACKNOWLEDGEMENTS
The University of Mosulβs Department of Computer Science and Mathematics and the University of
Telaferβs Department of Elementary and Secondary Education are providing funding for this study. The authors
state that they have no financial or personal stake in the outcome of this study.
REFERENCES
[1] J. E. Dennis Jr and R. B. Schnabel, βNumerical methods for unconstrained optimization and nonlinear equations,β Classics in
Applied Mathematics, vol. 16, 1996, doi: 10.1137/1.9781611971200.
[2] J. Barzilai and J. M. Borwein, βTwo-point step size gradient methods,β IMA Journal of Numerical Analysis, vol. 8, pp. 141-148,
1988. [Online]. Available: https://pages.cs.wisc.edu/~swright/726/handouts/barzilai-borwein.pdf
[3] Y. Narushima, H. Yabe, and J. A. Ford, βA three-term conjugate gradient method with sufficient descent property for
unconstrained optimization,β SIAM Journal on Optimization, vol. 21, no. 1, pp. 212-230, 2011, doi: 10.1137/080743573.
[4] E. T. Hamed, R. Z. Al-Kawaz, and A. Y. Al-Bayati, βNew Investigation for the Liu-Story Scaled Conjugate Gradient Method for
Nonlinear Optimization,β Journal of Mathematics, vol. 2020, 2020, doi: 10.1155/2020/3615208.
[5] A. B. Abubakar, P. Kumam, M. Malik, P. Chaipunya, and A. H. Ibrahim, βA hybrid FR-DY conjugate gradient algorithm for
unconstrained optimization with application in portfolio selection,β AIMS Mathematics, vol. 6, no. 6, pp. 6506-6527, 2021,
doi: 10.3934/math.2021383.
[6] J. M. Ortega and W. C. Rheinboldt, βIterative solution of nonlinear equations in several variables,β SIAM, vol. 30, 2020,
doi: 10.1137/1.9780898719468.
[7] M. V. Solodov and B. F. Svaiter, βA globally convergent inexact Newton method for systems of monotone equations,β In
Reformulation: Nonsmooth, Piecewise Smooth, Semismooth, and Smoothing Methods, Boston, MA: Springer, 1998, vol. 22,
pp. 355-369, doi: 10.1007/978-1-4757-6388-1_18.
[8] W. Zhou and D. Shen, βAn inexact PRP conjugate gradient method for symmetric nonlinear equations,β Numerical Functional
Analysis and Optimization, vol. 35, no. 3, pp. 370-388, 2014, doi: 10.1080/01630563.2013.871290.
[9] J. Liu and S. Li, βSpectral DY-type projection method for nonlinear monotone systems of equations,β Journal of Computational
Mathematics, vol. 33, pp. 341β355, 2015, doi: 10.4208/jcm.1412-m4494.
[10] S. -Y. Liu, Y. -Y. Huang, and H. -W. Jiao, βSufficient descent conjugate gradient methods for solving convex constrained
nonlinear monotone equations,β Abstract and Applied Analysis, vol. 2014, 2014, doi: 10.1155/2014/305643.
[11] H. Mohammad and A. B. Abubakar, βA positive spectral gradient-like method for large-scale nonlinear monotone equations,β
Bulletin of Computational Applied Mathematics, vol. 5, no. 1, pp. 97β113, 2017. [Online]. Available:
https://www.researchgate.net/publication/317932625_A_positive_spectral_gradient-like_method_for_large-
scale_nonlinear_monotone_equations
[12] A. B. Abubakar, P. Kumam, H. Mohammad, A. M. Awwal, and K. Sitthithakerngkiet, βA modified FletcherβReeves conjugate
gradient method for monotone nonlinear equations with some applications,β Mathematics, vol. 7, no. 8, 2019,
doi: 10.3390/math7080745.
[13] A. B. Abubakar and P. Kumam, βA descent Dai-Liao conjugate gradient method for nonlinear equations,β Numerical Algorithms,
vol. 81, pp. 197-210, 2019, doi: 10.1007/s11075-018-0541-z.
[14] R. Z. Al-Kawaz and A. Y. Al-Bayati, βAn effective new iterative CG-method to solve unconstrained non-linear optimization
issues,β TELKOMNIKA (Telecommunication, Computing, Electronics and Control), vol. 19, no. 6, pp. 1847-1856, 2021,
doi: 10.12928/telkomnika.v19i6.19791.
[15] R. Z. Al-Kawaz and A. Y. Al-Bayati, βAn adaptive search direction algorithm for the modified projection minimization
optimization,β IOP Conference Series: Materials Science and Engineering, 2021, vol. 1152, doi: 10.1088/1757-
899X/1152/1/012019.
[16] Y. -H. Dai and L. -Z. Liao, βNew conjugacy conditions and related nonlinear conjugate gradient methods,β Applied Mathematics
and Optimization, vol. 43, pp. 87-101, 2001, doi: 10.1007/s002450010019.
[17] A. B. Abubakar, P. Kumam, and A. M. Awwal, βA descent dai-Liao projection method for convex constrained nonlinear
monotone equations with applications,β Thai Journal of Mathematics, pp. 128-152, 2018. [Online]. Available:
http://thaijmath.in.cmu.ac.th/index.php/thaijmath/article/view/3372
[18] M. Fatemi, βA new conjugate gradient method with an efficient memory structure,β Computational and Applied Mathematics,
vol. 38, pp. 59, 2019, doi: 10.1007/s40314-019-0834-4.
[19] W. La Cruz, J. M. MartΓnez, and M. Raydan, βSpectral residual method without gradient information for solving large-scale
nonlinear systems of equations,β Mathematics of computation, vol. 75, no. 255, pp. 1429-1448, 2006. [Online]. Available:
https://www.ams.org/journals/mcom/2006-75-255/S0025-5718-06-01840-0/S0025-5718-06-01840-0.pdf
[20] W. Zhou and D. Li, βLimited memory BFGS method for nonlinear monotone equations,β Journal of Computational Mathematics,
vol. 25, no. 1, pp. 89-96, 2007. [Online]. Available: https://www.global-sci.org/v1/jcm/volumes/v25n1/pdf/251-89.pdf
[21] C. Wang, Y. Wang, and C. Xu, βA projection method for a system of nonlinear monotone equations with convex constraints,β
Mathematical Methods of Operations Research, vol. 66, pp. 33-46, 2007, doi: 10.1007/s00186-006-0140-y.
11. TELKOMNIKA Telecommun Comput El Control ο²
A generalized Dai-Liao type CG-method with a new monotone line search for β¦ (Rana Z. Al-Kawaz)
555
[22] R. Z. Al-Kawaz and A. Y. Al-Bayati, βShow off the efficiency of the dai-Liao method in merging technology for monotonous
non-linear problems,β Indonesian Journal of Electrical Engineering and Computer Sciences, vol. 21, no. 1, pp. 505-515, 2021,
doi: 10.11591/ijeecs.v21.i1.pp505-515.
[23] W. La Cruz, βA spectral algorithm for large-scale systems of nonlinear monotone equations,β Numerical Algorithms, vol. 76,
pp. 1109-1130, 2017, doi: 10.1007/s11075-017-0299-8.
[24] X. -S. Yang, βTest Problems in Optimization,β arXiv, 2010, doi: 10.48550/arXiv.1008.0549.
[25] M. Jamil and X. -S. Yang, βA literature survey of benchmark functions for global optimization problems,β International Journal
of Mathematical Modelling and Numerical Optimisation, vol. 4, no. 2, pp. 150-194, 2013, doi: 10.48550/arXiv.1308.4008.
[26] E. D. Dolan and J. J. MorΒ΄e, βBenchmarking optimization software with performance profiles,β Mathematical Programming, vol. 91,
pp. 201-213, 2002, doi: 10.1007/s101070100263.
BIOGRAPHIES OF AUTHORS
Rana Z. Al-Kawaz received the PhD. degree (Philosophy of Science) in
computational numerical optimization from the Mosul University in Iraq. She is a Lecturer of
Mathematical Sciences in the Department of Mathematics, College of Basic Education,
University of Telafer, Mosul, Iraq. Additionally, she is working as the Head of Mathematics in
College of Basic Education, University of Telafer, Mosul, Iraq. She has published over 16
journal papers, 1 authored book and 2 papers in conference proceedings. His research interests
are in Numerical Optimization, Metahuristic Optimization Algorithm, Large-Scale Methods,
Computational Numerical Analysis, Unconstrained Optimization, Conjugate Gradient Method,
Nonlinear Equations, Operational Researches. She can be contacted at email:
rana.alkawaz@yahoo.com and ranazidan@uotelafer.edu.iq.
Abbas Y. Al-Bayati received the PhD. degree (Philosophy of Science) in
computational numerical optimization from the University of Leeds in U.K. He is a Professor
of Mathematical Sciences in the Department of Mathematics, College of Basic Education,
University of Telafer, Mosul, Iraq. In addition, he is currently the Director of the Computer
Department at University of Telafer, Iraq. He has published over 235 journal papers, 1
authored book and 32 papers in conference proceedings. His research interests are in
Numerical Analysis, Complex Analysis, Large-Scale Methods, Computational Numerical
Optimization, Computer Oriented Algorithms, Parallel Computing, Operational Researches.
He can be contacted at email: profabbasalbayati@yahoo.com.