A modified nonmonotone BFGS algorithm for unconstrained optimization
 Xiangrong Li^{1}Email author,
 Bopeng Wang^{1} and
 Wujie Hu^{1}
https://doi.org/10.1186/s1366001714535
© The Author(s) 2017
Received: 20 March 2017
Accepted: 14 July 2017
Published: 9 August 2017
Abstract
In this paper, a modified BFGS algorithm is proposed for unconstrained optimization. The proposed algorithm has the following properties: (i) a nonmonotone line search technique is used to obtain the step size \(\alpha_{k}\) to improve the effectiveness of the algorithm; (ii) the algorithm possesses not only global convergence but also superlinear convergence for generally convex functions; (iii) the algorithm produces better numerical results than those of the normal BFGS method.
Keywords
MSC
1 Introduction
Formula 1
[37]
Formula 2
[38]
Some scholars have conducted further research to obtain a better approximation of the Hessian matrix of the objective function.
Formula 3
[39]
Formula 4
[41]
Formula 5
[42]
 (i)WWP line search technique. \(\alpha_{k}\) is determined bywhere \(0<\delta <\sigma <1\). Recently, a modified WWP line search technique was proposed by Yuan, Wei, and Lu [46] to ensure that the BFGS and the PRP methods have global convergence for nonconvex functions; these two open problems have been solved. However, monotonicity may generate a series of extremely small steps if the contours of the objective functions are a family of curves with large curvature [47]. Nonmonotonic line search to solve unconstrained optimization was proposed by Grippo et al. in [47–49] and was further studied by [50]. Grippo, Lamparillo, and Lucidi [47] proposed the following nonmonotone line search and called it GLL line search.$$ f(x_{k}+\alpha_{k}d_{k})\leq f(x_{k})+\delta \alpha_{k}g_{k}^{T}d_{k},\qquad g(x_{k}+\alpha_{k}d_{k})^{T}d_{k} \geq \sigma g_{k}^{T}d_{k}, $$(1.11)
 (ii)GLL nonmonotone line search. \(\alpha_{k}\) is determined by$$\begin{aligned}& f(x_{k+1}) \leq \max_{0\leq j \leq M_{0}}f(x_{kj})+ \epsilon_{1}\alpha _{k}g_{k}^{T}d_{k}, \end{aligned}$$(1.12)where \(p\in (\infty,1)\), \(k=0, 1, 2, \ldots\) , \(\varepsilon_{1} \in (0,1)\), \(\varepsilon_{2} \in (0,\frac{1}{2})\), \(M_{0}\) is a nonnegative integer. By combining this line search with the normal BFGS formula, Han and Liu [51] established the global convergence of the convex objective function; its superlinear convergence was established by Yuan and Wei [52]. Although these nonmonotone techniques perform well in many cases, the numerical performance is dependent on the choice of \(M_{0}\) to some extent (see [47, 53, 54] in detail). Zhang and Hager [55] presented another nonmonotone line search technique.$$\begin{aligned}& g(x_{k+1})^{T}d_{k} \geq \max \bigl\{ \epsilon_{2}, 1\bigl(\alpha_{k}\Vert d_{k} \Vert \bigr)^{p} \bigr\} g_{k}^{T}d_{k}, \end{aligned}$$(1.13)
 (iii)Zhang and Hager nonmonotone line search technique [55]. In this technique \(\alpha_{k}\) is found bywhere \(\eta_{k}\in [\eta_{\min },\eta_{\max }]\), \(0\leq \eta_{\min } \leq \eta_{\max }\leq 1\), \(C_{0}=f(x_{0})\) and \(Q_{0}=1\). It is easy to conclude that \(C_{k+1}\) is a convex combination of \(C_{k}\) and \(f(x_{k+1})\). The numerical results show that this technique is more competitive than the nonmonotone method of [47], but it requires strong assumption conditions for convergence analysis.$$ Q_{k+1}=\eta_{k}Q_{k}+1,\qquad C_{k+1}= \frac{\eta_{k}Q_{k}C_{k}+f(x_{k+1})}{Q_{k+1}}, $$(1.14)

The GLL line search technique is used in the algorithm to ensure good convergence.

The major contribution of the new algorithm is an extension of the modified BFGS update from [43] and [42].

Another contribution is the proof of global convergence for generally convex functions.

The major aim of the proposed method is to establish the superlinear convergence and the global convergence for generally convex functions.

The experimental problems, including both normal unconstrained optimization and engineering problems (benchmark problems), indicate that the proposed algorithm is competitive with the normal method.
This paper is organized as follows. In the next section, we present the algorithm. The global convergence and superlinear convergence are established in Section 3 and Section 4, respectively. Numerical results are reported in Section 5. In the final section, we present a conclusion. Throughout this paper, \(\Vert \cdot \Vert \) denotes the Euclidean norm of a vector or matrix.
2 Algorithm
Algorithm 1
ModnonBFGSA
 Step 0::

Given a symmetric and positive definite matrix \(B_{0}^{*}\) and an integer \(M_{0}>0\), choose an initial point \(x_{0} \in \Re^{n}\), \(0<\varepsilon <1\), \(0<\epsilon_{1}<\epsilon_{2}<1\), \(p\in (\infty,1)\); Set \(k:=0\).
 Step 1::

\(\Vert g_{k}\Vert \leq \varepsilon\), stop; Otherwise, go to the next step.
 Step 2::

Solveto obtain \(d_{k}\).$$ B_{k}^{*}d_{k}+g_{k}=0 $$(2.3)
 Step 4::

Let \(x_{k+1}=x_{k}+\alpha_{k}d_{k}\).
 Step 5::

Generate \(B_{k+1}^{*}\) from (2.1) and set \(k=k+1\); Go to Step 1.
3 Global convergence
The following assumptions are required to obtain the global convergence of Algorithm 1.
Assumption A
 (i)
The level set \(\L_{0}=\{x \mid f(x) \le f(x _{0}) \}\) is bounded.
 (ii)The objective function f is continuously differentiable and convex on \(L_{0}\). Moreover, there exists a constant \(L\ge 0\) satisfying$$ \bigl\Vert g(x)g(y)\bigr\Vert \le L\Vert xy \Vert ,\quad \forall x, y \in L_{0}. $$(3.1)
Lemma 3.1
The proof is similar to [41], so it is not presented here.
Lemma 3.2
Lemma 3.3
Proof
For \(k=0\), by the positive definiteness of \(B_{0}\), we have \(s_{0}^{T}y_{0}^{*}>0\). Then \(B_{1}\) is generated by (2.1), and \(B_{1}\) is positive definite. Assume that \(B_{k}\) is positive definite; for all \(k\geq 1\), we prove that \(s_{k}^{T}y_{k}^{*}>0\) holds by the following three cases.
Case 3: \(\bar{A}_{k}>0\) . The proof can be found in [41]
Similar to the proof of Theorem 3.1 in [51], we can establish the global convergence theorem of Algorithm 1. Here, we state the theorem but omit the proof. □
Theorem 3.1
4 Superlinear convergence analysis
Based on Theorem 3.1, we suppose that \(x^{*}\) is the limit of the sequence \(\{x_{k}\}\). To establish the superlinear convergence of Algorithm 1, the following additional assumption is needed.
Assumption B
In a way similar to [41], we can obtain the superlinear convergence of Algorithm 1, which we state as follows but we omit its proof.
5 Numerical results
This section reports the numerical results of Algorithm 1. All code was written in MATLAB 7.0 and run on a PC with a 2.60 GHz CPU processor, 256 MB memory and the Windows XP operating system. The parameters are chosen as \(\delta =0.1\), \(\sigma =0.9\), \(\varepsilon =10^{5}\), \(\epsilon_{1}=0.1\), \(\epsilon_{2}=0.01\), \(p=5\), \(M_{0}=8\), and the initial matrix \(B_{0}=I\) is the unit matrix. Since the line search cannot ensure the descent condition \(d_{k}^{T}g_{k}<0\), an uphill search direction may occur in the numerical experiments. In this case, the line search rule may fail. To avoid this case, the step size \(\alpha_{k}\) is accepted if the search number is greater than 25 in the line search. The following is the Himmeblau stop rule: If \(\vert f(x_{k})\vert > e_{1}\), let \(\mathit{stop}1=\frac{\vert f(x_{k})f(x_{k+1})\vert }{\vert f(x_{k})\vert }\); otherwise, let \(\mathit{stop}1=\vert f(x_{k})f(x_{k+1})\vert \). In the experiment, if \(\Vert g(x)\Vert < \varepsilon \) or \(\mathit{stop} 1 < e_{2}\) satisfies \(e_{1}=e _{2}=10^{5}\), we end the program.
5.1 [57] problems
 Problem::

the name of the test problem;
 Dim::

the dimensions of the problem;
 NI::

the total number of iterations;
 Time::

the cpu time in seconds;
 NFG::

\(NFG=NF+5NG\), where NF and NG are the total number of function and gradient evaluations, respectively (see [47]).
Numerical results
Problem  Dim  BFGSWP NI/NFG/Time  BFGSWPZhang NI/NFG/Time  BFGSNon NI/NFG/Time  BFGSMNon NI/NFG/Time 

ROSE  2  35/590/4.506480e−002  31/611/4.882020e−002  2/19/6.259000e−003  2/19/6.259000e−003 
FROTH  2  9/116/1.376980e−002  7/90/1.001440e−002  2/19/6.259000e−003  2/19/7.510800e−003 
BADSCP  2  43/706/5.507920e−002  43/706/5.507920e−002  8/264/2.753960e−002  8/264/2.753960e−002 
BADSCB  2  3/60/1.126620e−002  3/60/1.001440e−002  3/32/7.510800e−003  3/32/6.259000e−003 
BEALE  2  15/220/2.128060e−002  16/226/2.002880e−002  2/19/6.259000e−003  2/19/6.259000e−003 
JENSAM  2  2/42/1.126620e−002  2/42/1.001440e−002  2/19/6.259000e−003  2/19/8.762600e−003 
HELIX  3  34/483/4.381300e−002  23/325/3.004320e−002  169/2,191/2.090506e−001  87/1,163/1.114102e−001 
BARD  3  16/229/3.004320e−002  14/182/2.503600e−002  72/930/1.226764e−001  72/930/1.226764e−001 
GAUSS  3  2/19/6.259000e−003  2/19/6.259000e−003  2/19/7.510800e−003  2/19/7.510800e−003 
MEYER  3  2/42/1.376980e−002  2/42/1.251800e−002  2/32/1.126620e−002  2/32/1.251800e−002 
GULF  3  2/42/1.502160e−002  2/42/1.502160e−002  2/19/3.755400e−003  2/19/1.001440e−002 
BOX  3  2/42/1.251800e−002  2/42/1.126620e−002  2/19/7.510800e−003  2/19/8.762600e−003 
SING  4  20/280/2.503600e−002  18/269/2.503600e−002  2/19/6.259000e−003  2/19/7.510800e−003 
WOOD  4  19/271/2.628780e−002  20/289/2.753960e−002  2/19/6.259000e−003  2/19/6.259000e−003 
KOWOSB  4  21/295/3.505040e−002  23/324/3.630220e−002  83/1,077/1.314390e−001  104/1,345/1.664894e−001 
BD  4  17/244/3.505040e−002  19/276/3.880580e−002  2/19/7.510800e−003  2/19/1.001440e−002 
OSB1  5  2/42/2.128060e−002  2/42/1.877700e−002  2/19/7.510800e−003  2/19/1.001440e−002 
BIGGS  6  25/322/4.506480e−002  7/108/2.253240e−002  15/330/4.381300e−002  21/287/4.130940e−002 
OSB2  11  3/56/6.259000e−002  3/56/6.259000e−002  3/33/1.877700e−002  3/33/2.002880e−002 
WATSON  20  31/457/3.880580e−001  29/412/3.555112e−001  2/19/2.002880e−002  2/19/2.253240e−002 
ROSEX  100  229/3,704/1.268073e+000  276/4,359/1.512174e+000  2/19/1.126620e−002  2/19/1.251800e−002 
SINGX  400  65/922/1.174939e+001  155/2,375/2.844465e+001  2/19/2.065470e−001  2/19/2.115542e−001 
PEN1  400  2/47/7.247922e−001  2/47/7.310512e−001  2/19/1.940290e−001  2/19/1.927772e−001 
PEN2  200  2/25/6.884900e−002  2/25/6.634540e−002  2/19/6.008640e−002  2/19/6.384180e−002 
VARDIM  100  2/47/2.879140e−002  2/47/2.879140e−002  2/19/1.001440e−002  2/19/8.762600e−003 
TRIG  500  9/138/1.627340e+002  9/144/1.671604e+002  8/146/1.700345e+002  50/876/1.039274e+003 
BV  500  2/19/3.492522e−001  2/19/3.492522e−001  2/19/3.480004e−001  2/19/3.517558e−001 
IE  500  6/71/7.711088e+000  6/71/7.706081e+000  6/71/7.722354e+000  6/71/7.772426e+000 
TRID  500  53/760/1.622333e+001  50/727/1.501159e+001  564/7,325/1.690631e+002  564/7,325/1.692333e+002 
BAND  500  12/275/5.551733e+000  12/238/4.696754e+000  2/19/4.781876e−001  2/19/4.431372e−001 
LIN  500  2/19/4.719286e−001  2/19/4.744322e−001  2/19/4.806912e−001  2/19/4.719286e−001 
LIN1  500  3/32/9.363464e−001  3/32/9.388500e−001  3/31/9.050514e−001  3/31/9.025478e−001 
LIN0  500  3/32/1.165426e+000  3/32/1.161670e+000  3/31/1.119109e+000  3/31/1.130375e+000 
In Table 1, ‘BFGSWP’, ‘BFGSNon’, ‘BFGSWPZhang’, and ‘BFGSMNon’ stand for the normal BFGS formula with WWP rule, the normal BFGS formula with GLL rule, the modified BFGS equation (1.10) with WWP rule, and MNBFGSA, respectively. The numerical results in Table 1 indicate that the proposed method is competitive with the other three similar methods.
Figure 1 shows that BFGSMNon and BFGSNon outperform BFGSWP and BFGSWPZhang on approximately 9% and 6% of the problems, respectively. The BFGSWPZhang and BFGSWP methods can successfully solve 94% and 91% of the test problems, respectively.
Figure 2 shows that BFGSMNon and BFGSNon are superior to BFGSWP and BFGSWPZhang on approximately 12% and 9% of these problems, respectively. The BFGSMNon and BFGSNon methods solve 100% of the test problems at \(t\approx 10\). The BFGSWPZhang and the BFGSWP methods solve the test problems with probabilities of 91% and 88%, respectively.
Figure 3 shows that the success rates when using the BFGSMNon and BFGSNon methods to address the test problems are higher than the success rates when using BFGSWP and BFGSWPZhang by approximately 6% and 9%, respectively. Additionally, the BFGSMNon and BFGSNon algorithms can address almost all the test problems. Moreover, BFGSWPZhang has better results than BFGSWP.
5.2 Benchmark problems
Definition of the benchmark problems and their features
Function  Definition  Multimodal?  Separable?  Regular? 

Sphere  \(f_{Sph}(x)=\sum_{i=1}^{p}x_{i}^{2}\)  no  yes  n/a 
\(x_{i}\in [5.12,5.12]\), \(x^{*}=(0,0,\ldots,0)\), \(f_{Sph}(x^{*})=0\).  
Schwefel’s  \(f_{SchDS}(x)=\sum_{i=1}^{p}(\sum_{j=1}^{i}x_{j})^{2}\)  no  no  n/a 
\(x_{i}\in [65.536,65.536]\), \(x^{*}=(0,0,\ldots,0)\), \(f_{SchDS}(x^{*})=0\).  
Griewank  \(f_{Gri}(x)=1+\sum_{i=1}^{p}\frac{x_{i}^{2}}{4{,}000}\prod_{i=1}^{p}\cos \frac{x_{i}}{i}\)  yes  no  yes 
\(x_{i}\in [600,600]\), \(x^{*}=(0,0,\ldots,0)\), \(f_{Gri}(x^{*})=0\).  
Rosenbrock  \(f_{Ros}(x)=\sum_{i=1}^{p}[100(x_{i+1}x_{i}^{2})^{2}+(x_{i}1)^{2}]\)  no  no  n/a 
\(x_{i}\in [2.048,2.048]\), \(x^{*}=(1,1,\ldots,1)\), \(f_{Ros}(x^{*})=0\).  
Ackley  \(f_{Ack}(x)=20+e20 e^{0.2\sqrt{\frac{1}{p}\sum _{i=1}^{p}x_{i}^{2}}}e^{\frac{1}{p}\sum _{i=1}^{p}\cos (2\pi x_{i})}\)  yes  no  yes 
\(x_{i}\in [30,30]\), \(x^{*}=(0,0,\ldots,0)\), \(f_{Ack}(x^{*})=0\). 
Numerical results of the benchmark problems
Problem/ \(\boldsymbol{x_{0}}\)  Dim  BFGSWP NI/NFG/Time  BFGSWPZhang NI/NFG/Time  BFGSNon NI/NFG/Time  BFGSMNon NI/NFG/Time 

Sphere/\(x_{Sph10}\)  30  2/19/1.562500e−001  2/19/1.562500e−002  2/19/4.687500e−002  2/19/4.687500e−002 
500  2/19/2.031250e−001  2/19/3.125000e−001  2/19/2.656250e−001  2/19/2.187500e−001  
1,000  2/19/1.015625e+000  2/19/1.093750e+000  2/19/1.062500e+000  2/19/1.046875e+000  
Sphere/\(x_{Sph20}\)  30  2/19/0  2/19/0  2/19/0  2/19/0 
500  2/19/1.875000e−001  2/19/2.500000e−001  2/19/2.187500e−001  2/19/1.875000e−001  
1,000  2/19/9.531250e−001  2/19/1.046875e+000  2/19/1.031250e+000  2/19/1.218750e+000  
Sphere/\(x_{Sph30}\)  30  2/19/0  2/19/0  2/19/0  2/19/0 
500  2/19/2.031250e−001  2/19/2.812500e−001  2/19/2.343750e−001  2/19/1.718750e−001  
1,000  2/19/1.015625e+000  2/19/9.687500e−001  2/19/9.531250e−001  2/19/9.843750e−001  
Sphere/\(x_{Sph40}\)  30  2/19/0  2/19/0  2/19/0  2/19/0 
500  2/19/1.718750e−001  2/19/2.343750e−001  2/19/2.187500e−001  2/19/1.250000e−001  
1,000  2/19/9.218750e−001  2/19/1  2/19/1  2/19/1.015625e+000  
Schwefel’s/\(x_{SchDs10}\)  30  3/32/0  3/32/6.250000e−002  3/32/6.250000e−002  3/32/0 
50  3/32/0  3/32/0  3/32/6.250000e−002  3/32/6.250000e−002  
100  4/45/1.562500e−001  4/45/2.500000e−001  6/70/3.750000e−001  6/70/4.062500e−001  
Schwefel’s/\(x_{SchDs20}\)  30  2/19/6.250000e−002  2/19/0  2/19/0  2/19/0 
50  2/19/0  2/19/6.250000e−002  2/19/0  2/19/0  
100  3/32/1.875000e−001  3/32/1.250000e−001  3/32/1.875000e−001  3/32/1.718750e−001  
Schwefel’s/\(x_{SchDs30}\)  30  3/32/0  3/32/6.250000e−002  3/32/0  3/32/0 
50  3/32/6.250000e−002  3/32/0  3/32/0  3/32/6.250000e−002  
100  3/32/1.875000e−001  3/32/1.250000e−001  3/32/1.875000e−001  3/32/1.250000e−001  
Schwefel’s/\(x_{SchDs40}\)  30  2/19/0  2/19/0  2/19/0  2/19/0 
50  2/19/0  2/19/6.250000e−002  2/19/0  2/19/0  
100  2/19/6.250000e−002  2/19/6.250000e−002  2/19/1.250000e−001  2/19/6.250000e−002  
Griewank/\(x_{Gri10}\)  30  3/37/0  3/37/0  11/258/6.250000e−002  9/130/6.250000e−002 
500  2/24/5.781250e−001  2/24/5.312500e−001  2/24/5.781250e−001  2/24/6.406250e−001  
1,000  2/24/1.984375e+000  2/24/1.656250e+000  2/24/1.671875e+000  2/24/1.625000e+000  
Griewank/\(x_{Gri20}\)  30  4/75/0  4/75/4.687500e−002  4/59/0  4/58/0 
500  2/24/6.718750e−001  2/24/3.437500e−001  2/24/4.062500e−001  2/24/6.562500e−001  
1,000  2/24/1.765625e+000  2/24/1.796875e+000  2/24/1.859375e+000  2/24/1.640625e+000  
Griewank/\(x_{Gri30}\)  30  3/38/0  3/37/4.687500e−002  11/394/1.250000e−001  9/178/0 
500  2/24/5.625000e−001  2/24/5.468750e−001  2/24/5.625000e−001  2/24/5.781250e−001  
1,000  2/24/2.046875e+000  2/24/1.531250e+000  2/24/1.468750e+000  2/24/1.421875e+000  
Griewank/\(x_{Gri40}\)  30  15/200/6.250000e−002  19/249/6.250000e−002  9/502/6.250000e−002  18/446/1.250000e−001 
500  2/24/6.093750e−001  2/24/2.968750e−001  2/24/5.468750e−001  2/24/5.468750e−001  
1,000  2/24/1.843750e+000  2/24/1.468750e+000  2/24/1.828125e+000  2/24/1.781250e+000  
Rosenbrock/\(x_{Ros10}\)  30  34/483/1.406250e−001  5/116/0  2/19/0  2/19/0 
500  30/419/3.431250e+001  5/116/2.031250e+000  2/19/2.187500e−001  2/19/1.875000e−001  
1,000  28/393/2.136875e+002  6/152/2.207813e+001  2/19/1.078125e+000  2/19/9.375000e−001  
Rosenbrock/\(x_{Ros20}\)  30  30/467/9.375000e−002  5/121/0  2/19/0  2/19/0 
500  16/268/1.650000e+001  3/38/6.250000e−001  2/19/1.875000e−001  2/19/2.187500e−001  
1,000  17/286/1.181094e+002  3/38/3.453125e+000  2/19/1.062500e+000  2/19/9.062500e−001  
Rosenbrock/\(x_{Ros30}\)  30  8/134/0  7/141/0  2/19/0  2/19/0 
500  9/154/6.828125e+000  6/110/3.546875e+000  2/19/2.031250e−001  2/19/2.187500e−001  
1,000  7/115/3.090625e+001  5/92/1.373438e+001  2/19/1.125000e+000  2/19/1.156250e+000  
Rosenbrock/\(x_{Ros40}\)  30  8/140/0  5/102/0  2/19/6.250000e−002  2/19/0 
500  12/186/1.185938e+001  6/105/5.203125e+000  2/19/2.343750e−001  2/19/2.031250e−001  
1,000  15/226/101  6/105/2.275000e+001  2/19/1.062500e+000  2/19/1.015625e+000  
Ackley/\(x_{Ack10}\)  30  5/68/6.250000e−002  6/80/0  6/83/0  6/80/0 
500  5/67/2.343750e+000  5/64/1.937500e+000  5/67/2.046875e+000  5/68/2.171875e+000  
1,000  5/66/1.407813e+001  6/79/2.229688e+001  5/66/1.410938e+001  6/79/2.278125e+001  
Ackley/\(x_{Ack20}\)  30  2/42/0  2/42/0  7/99/6.250000e−002  7/97/6.250000e−002 
500  6/79/3.250000e+000  6/77/3.640625e+000  6/79/3.671875e+000  6/77/3.593750e+000  
1,000  5/66/1.354688e+001  5/63/1.443750e+001  5/65/1.423438e+001  5/66/1.429688e+001  
Ackley/\(x_{Ack30}\)  30  9/126/0  5/67/0  9/126/6.250000e−002  6/83/0 
500  6/88/3.500000e+000  4/50/1.187500e+000  6/88/3.437500e+000  6/78/2.828125e+000  
1,000  4/53/7.531250e+000  4/51/7.671875e+000  7/95/3.085938e+001  6/77/2.229688e+001  
Ackley/\(x_{Ack40}\)  30  4/56/6.250000e−002  4/57/6.250000e−002  8/108/0  7/92/4.687500e−002 
500  4/55/1.343750e+000  4/54/1.015625e+000  7/98/4.062500e+000  7/92/4.562500e+000  
1,000  6/84/2.232813e+001  6/79/2.256250e+001  6/84/2.310938e+001  6/77/2.254688e+001  
Total CPU Time  516.1562  161.5781  115.0938  115.0156 
However, the effectiveness of one algorithm compared another algorithm cannot be determined based on the number of problems that it solves better. The ‘no free lunch’ theorem (see [61]) states that provided we compare two searching algorithms with all possible functions, the performance of any two algorithms will be, on average, the same. As a result, attempting to find a perfect test set where all the functions are present to determine whether an algorithm is better than another algorithm for every function is a fruitless task. Therefore, when an algorithm is evaluated, we identify the types of problems where its performance is good to characterize the types of problems for which the algorithm is suitable. The authors previously studied functions to be optimized to construct a test set with a better selection of fewer functions (see [62, 63]). This enables us to draw conclusions about the performance of the algorithm depending on the type of function.
Figure 4 indicates that BFGSWP can solve approximately 93% of the test problems and that the other three methods can solve all the problems. The proposed algorithm solves the problems in the shortest amount of time.
The performance in Figure 5 is similar to that in Figure 4. BFGSWP can solve approximately 95% of the test problems, while the other methods can solve all the problems.
According to these two figures, the proposed algorithm has the best performance among these four methods, and the BFGSWP performs the worst. In summary, based on the numerical results of the [57] and benchmark problems, the GLL nonmonotone line search with quasiNewton update is more effective than the normal WWP line search with quasiNewton update, which is consistent with the results of [47, 51]. Moreover, these numerical results indicate that the modified BFGS equation (1.10) is better than the normal BFGS update, which is consistent with the results of [42]. Furthermore, the proposed algorithm is competitive with the related methods.
6 Conclusion
 (i)
This paper conducts a further study of the modified BFGS update formula in [43]. The main contribution is the global convergence and superlinear convergence for generally convex functions. The numerical results show that the proposed method is competitive with other quasiNewton methods for the test problems.
 (ii)
In contrast to [42] and [43], this paper achieves both superlinear and global convergence. Moreover, the convergence is obtained for generally convex functions, whereas the other two papers only obtained convergence for uniformly convex functions. The conditions of this paper are weaker than those of the previous research.
 (iii)
For further research, we should study the performance of the new algorithm under different stop rules and in different testing environments (such as [66]). Moreover, more numerical experiments for large practical problems should be performed in the future.
Declarations
Acknowledgements
The authors thank the referees for their valuable comments, which greatly improved their paper.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
References
 Fu, Z, Wu, X, Guan, C, et al.: Toward efficient multikeyword fuzzy search over encrypted outsourced data with accuracy improvement. IEEE Trans. Inf. Forensics Secur. 11(12), 27062716 (2016) View ArticleGoogle Scholar
 Gu, B, Sheng, VS, Tay, KY, et al.: Incremental support vector learning for ordinal regression. IEEE Trans. Neural Netw. Learn. Syst. 26(7), 14031416 (2015) MathSciNetView ArticleGoogle Scholar
 Gu, B, Sun, X, Sheng, VS: Structural minimax probability machine. IEEE Trans. Neural Netw. Learn. Syst. 99, 111 (2016) Google Scholar
 Li, J, Li, X, Yang, B, et al.: Segmentationbased image copymove forgery detection scheme. IEEE Trans. Inf. Forensics Secur. 10(3), 507518 (2015) View ArticleGoogle Scholar
 Pan, Z, Zhang, Y, Kwong, S: Efficient motion and disparity estimation optimization for low complexity multiview video coding. IEEE Trans. Broadcast. 61(2), 166176 (2015) View ArticleGoogle Scholar
 Pan, Z, Lei, J, Zhang, Y, et al.: Fast motion estimation based on content property for lowcomplexity H.265/HEVC Encoder. IEEE Trans. Broadcast. 99, 110 (2016) Google Scholar
 Yuan, G, Lu, S, Wei, Z: A new trustregion method with line search for solving symmetric nonlinear equations. Int. J. Comput. Math. 88(10), 21092123 (2011) MathSciNetView ArticleMATHGoogle Scholar
 Yuan, G, Meng, Z, Li, Y: A modified Hestenes and Stiefel conjugate gradient algorithm for largescale nonsmooth minimizations and nonlinear equations. J. Optim. Theory Appl. 168(1), 129152 (2016) MathSciNetView ArticleMATHGoogle Scholar
 Yuan, G, Wei, Z: The Barzilai and Borwein gradient method with nonmonotone line search for nonsmooth convex optimization problems. Math. Model. Anal. 17(2), 203216 (2012) MathSciNetView ArticleMATHGoogle Scholar
 Yuan, G, Wei, Z, Li, G: A modified PolakRibièrePolyak conjugate gradient algorithm for nonsmooth convex programs. J. Comput. Appl. Math. 255, 8696 (2014) MathSciNetView ArticleMATHGoogle Scholar
 Yuan, G, Wei, Z, Lu, S: Limited memory BFGS method with backtracking for symmetric nonlinear equations. Math. Comput. Model. 54(12), 367377 (2011) MathSciNetView ArticleMATHGoogle Scholar
 Yuan, G, Wei, Z, Lu, X: A BFGS trustregion method for nonlinear equations. Computing 92(4), 317333 (2011) MathSciNetView ArticleMATHGoogle Scholar
 Yuan, G, Wei, Z, Wang, Z: Gradient trust region algorithm with limited memory BFGS update for nonsmooth convex minimization. Comput. Optim. Appl. 54(1), 4564 (2013) MathSciNetView ArticleMATHGoogle Scholar
 Yuan, G, Yao, S: A BFGS algorithm for solving symmetric nonlinear equations. Optimization 62(1), 8599 (2013) MathSciNetView ArticleMATHGoogle Scholar
 Yuan, G, Zhang, M: A threeterms PolakRibièrePolyak conjugate gradient algorithm for largescale nonlinear equations. J. Comput. Appl. Math. 286, 186195 (2015) MathSciNetView ArticleMATHGoogle Scholar
 Yuan, G, Zhang, M: A modified HestenesStiefel conjugate gradient algorithm for largescale optimization. Numer. Funct. Anal. Optim. 34(8), 914937 (2013) MathSciNetView ArticleMATHGoogle Scholar
 Schropp, J: A note on minimization problems and multistep methods. Numer. Math. 78(1), 87101 (1997) MathSciNetView ArticleMATHGoogle Scholar
 Schropp, J: Onestep and multistep procedures for constrained minimization problems. IMA J. Numer. Anal. 20(1), 135152 (2000) MathSciNetView ArticleMATHGoogle Scholar
 Wyk, DV: Differential optimization techniques. Appl. Math. Model. 8(6), 419424 (1984) MathSciNetView ArticleMATHGoogle Scholar
 Vrahatis, MN, Androulakis, GS, Lambrinos, JN, et al.: A class of gradient unconstrained minimization algorithms with adaptive stepsize. J. Comput. Appl. Math. 114(2), 367386 (2000) MathSciNetView ArticleMATHGoogle Scholar
 Yuan, G: Modified nonlinear conjugate gradient methods with sufficient descent property for largescale optimization problems. Optim. Lett. 3(1), 1121 (2009) MathSciNetView ArticleMATHGoogle Scholar
 Yuan, G, Duan, X, Liu, W, et al.: Two new PRP conjugate gradient algorithms for minimization optimization models. PLoS ONE 10(10), e0140071 (2015) View ArticleGoogle Scholar
 Yuan, G, Wei, Z: New line search methods for unconstrained optimization. J. Korean Stat. Soc. 38(1), 2939 (2009) MathSciNetView ArticleMATHGoogle Scholar
 Yuan, G, Wei, Z: A trust region algorithm with conjugate gradient technique for optimization problems. Numer. Funct. Anal. Optim. 32(2), 212232 (2011) MathSciNetView ArticleMATHGoogle Scholar
 Yuan, G, Wei, Z, Zhao, Q: A modified PolakRibièrePolyak conjugate gradient algorithm for largescale optimization problems. IIE Trans. 46(4), 397413 (2014) View ArticleGoogle Scholar
 Broyden, C: The convergence of a class of double rank minimization algorithms. J. Inst. Math. Appl. 6(1), 222231 (1970) MathSciNetView ArticleMATHGoogle Scholar
 Fletcher, R: A new approach to variable metric algorithms. Comput. J. 13(2), 317322 (1970) View ArticleMATHGoogle Scholar
 Goldfarb, A: A family of variable metric methods derived by variational means. Math. Comput. 24(109), 2326 (1970) MathSciNetView ArticleMATHGoogle Scholar
 Schanno, J: Conditions of quasiNewton methods for function minimization. Math. Comput. 24(4), 647650 (1970) View ArticleGoogle Scholar
 Broyden, CG, Dennis, JE, Moré, JJ: On the local and superlinear convergence of quasiNewton methods. J. Inst. Math. Appl. 12(3), 223245 (1973) MathSciNetView ArticleMATHGoogle Scholar
 Byrd, RH, Nocedal, J: A tool for the analysis of quasiNewton methods with application to unconstrained minimization. SIAM J. Sci. Comput. 26(3), 727739 (1989) MathSciNetMATHGoogle Scholar
 Byrd, RH: Global convergence of a cass of quasiNewton methods on convex problems. SIAM J. Numer. Anal. 24(5), 11711190 (1987) MathSciNetView ArticleMATHGoogle Scholar
 Dennis, JE: QuasiNewton methods, motivation and theory. SIAM Rev. 19(1), 4689 (1977) MathSciNetView ArticleMATHGoogle Scholar
 Dennis, JE: A characterization of superlinear convergence and its application to quasiNewton methods. Math. Comput. 28(126), 549560 (1974) MathSciNetView ArticleMATHGoogle Scholar
 Dai, YH: Convergence properties of the BFGS algoritm. SIAM J. Optim. 13(3), 693701 (2002) MathSciNetView ArticleMATHGoogle Scholar
 Mascarenhas, WF: The BFGS method with exact line searches fails for nonconvex objective functions. Math. Program. 99(1), 4961 (2004) MathSciNetView ArticleMATHGoogle Scholar
 Li, DH, Fukushima, M: A modified BFGS method and its global convergence in nonconvex minimization. J. Comput. Appl. Math. 129(12), 1535 (2001) MathSciNetView ArticleMATHGoogle Scholar
 Li, DH, Fukushima, M: On the global convergence of BFGS method for nonconvex unconstrained optimization problems. SIAM J. Optim. 11(4), 10541064 (1999) MathSciNetView ArticleMATHGoogle Scholar
 Wei, Z, Yu, G, Yuan, G, et al.: The superlinear convergence of a modified BFGStype method for unconstrained optimization. Comput. Optim. Appl. 29(3), 315332 (2004) MathSciNetView ArticleMATHGoogle Scholar
 Wei, Z, Li, G, Qi, L: New quasiNewton methods for unconstrained optimization problems. Appl. Math. Comput. 175(2), 11561188 (2006) MathSciNetMATHGoogle Scholar
 Yuan, G, Wei, Z: Convergence analysis of a modified BFGS method on convex minimizations. Comput. Optim. Appl. 47(2), 237255 (2010) MathSciNetView ArticleMATHGoogle Scholar
 Zhang, JZ, Deng, NY, Chen, LH: New quasiNewton equation and related methods for unconstrained optimization. J. Optim. Theory Appl. 102(1), 147167 (1999) MathSciNetView ArticleMATHGoogle Scholar
 Yuan, G, Wei, Z, Wu, Y: Modified limited memory BFGS method with nonmonotone line search for unconstrained optimization. J. Korean Math. Soc. 47(4), 767788 (2010) MathSciNetView ArticleMATHGoogle Scholar
 Davidon, WC: Variable metric method for minimization. SIAM J. Optim. 1(1), 117 (1991) MathSciNetView ArticleMATHGoogle Scholar
 Powell, MJD: A new algorithm for unconstrained optimization. In: Nonlinear Programming, pp. 3165. Academic Press, New York (1970) View ArticleGoogle Scholar
 Yuan, G, Wei, Z, Lu, X: Global convergence of BFGS and PRP methods under a modified weak WolfePowell line search. Appl. Math. Model. 47, 811825 (2017) MathSciNetView ArticleGoogle Scholar
 Grippo, L, Lampariello, F, Lucidi, S: A nonmonotone line search technique for Newton’s method. SIAM J. Sci. Comput. 23(4), 707716 (1986) MathSciNetMATHGoogle Scholar
 Grippo, L, Lampariello, F, Lucidi, S: A truncated Newton method with nonmonotone line search for unconstrained optimization. J. Optim. Theory Appl. 60(3), 401419 (1989) MathSciNetView ArticleMATHGoogle Scholar
 Grippo, L, Lampariello, F, Lucidi, S: A class of nonmonotone stabilization methods in unconstrained optimization. Numer. Math. 59(1), 779805 (1991) MathSciNetView ArticleMATHGoogle Scholar
 Liu, G, Han, J, Sun, D: Global convergence of the BFGS algorithm with nonmonotone linesearch. Optimization 34(2), 147159 (1995) MathSciNetView ArticleMATHGoogle Scholar
 Han, J, Liu, G: Global convergence analysis of a new nonmonotone BFGS algorithm on convex objective functions. Comput. Optim. Appl. 7(3), 277289 (1997) MathSciNetView ArticleMATHGoogle Scholar
 Yuan, GL, Wei, ZX: The superlinear convergence analysis of a nonmonotone BFGS algorithm on convex objective functions. Acta Math. Sin. Engl. Ser. 24(1), 3542 (2008) MathSciNetView ArticleMATHGoogle Scholar
 Raydan, M: The Barzilai and Borwein gradient method for the large scale unconstrained minimization problem. SIAM J. Sci. Comput. 7(1), 2633 (1997) MathSciNetMATHGoogle Scholar
 Toint, PL: An assessment of nonmonotone linesearch techniques for unconstrained optimization. SIAM J. Sci. Comput. 17(3), 725739 (2012) MathSciNetView ArticleMATHGoogle Scholar
 Zhang, H, Hager, WW: A nonmonotone line search technique and its application to unconstrained optimization. SIAM J. Optim. 14(4), 10431056 (2006) MathSciNetView ArticleMATHGoogle Scholar
 Powell, MJD: Some properties of the variable metric algorithm. In: Numerical Methods for Nonlinear Optimization, pp. 117. Academic Press, London (1972) Google Scholar
 Moré, JJ, Garbow, BS, Hillstrom, KE: Testing unconstrained optimization software. ACM Trans. Math. Softw. 7(1), 1741 (1981) MathSciNetView ArticleMATHGoogle Scholar
 Dolan, ED, Moré, JJ: Benchmarking optimization software with performance profiles. Math. Program. 91(2), 201213 (2002) MathSciNetView ArticleMATHGoogle Scholar
 Hadley, G: Nonlinear and Dynamics Programming. AddisonWesley, New Jersey (1964) MATHGoogle Scholar
 Friedman, JH: An overview of predictive learning and function approximation. In: Cherkassky, V, Friedman, JH, Wechsler, H (eds.) From Statistics to Neural Networks, Theory and Pattern Recognition Applications. NATO ASI Series F, vol. 136, pp. 161. Springer, Berlin (1994) Google Scholar
 Wolpert, DH, Macready, WG: No freelunch theorems for search. Technical Report 9502010, Santa Fe Institute (1995) Google Scholar
 Salomon, R: Reevaluating genetic algorithm performance under coordinate rotation of benchmark functions. Biosystems 39(3), 263278 (1996) View ArticleGoogle Scholar
 Whitley, D, Mathias, K, Rana, S, Dzubera, J: Building better test functions. In: Eshelman, L (ed.) Sixth International Conference on Genetic Algorithms, pp. 239246. Kaufmann, California (1995) Google Scholar
 Yuan, G, Lu, X, Wei, Z: A conjugate gradient method with descent direction for unconstrained optimization. J. Comput. Appl. Math. 233(2), 519530 (2009) MathSciNetView ArticleMATHGoogle Scholar
 Yuan, G, Lu, X, Wei, Z: BFGS trustregion method for symmetric nonlinear equations. Biosystems 230(1), 4458 (2009) MathSciNetMATHGoogle Scholar
 Gould, NIM, Orban, D, Toint, PL: CUTEr and SifDec: a constrained and unconstrained testing environment, revisited. ACM Trans. Math. Softw. 29(4), 373394 (2003) View ArticleMATHGoogle Scholar