Two efficient modifications of AZPRP conjugate gradient method with sufficient descent property

Abstract

The conjugate gradient method can be applied in many fields, such as neural networks, image restoration, machine learning, deep learning, and many others. Polak–Ribiere–Polyak and Hestenses–Stiefel conjugate gradient methods are considered as the most efficient methods to solve nonlinear optimization problems. However, both methods cannot satisfy the descent property or global convergence property for general nonlinear functions. In this paper, we present two new modifications of the PRP method with restart conditions. The proposed conjugate gradient methods satisfy the global convergence property and descent property for general nonlinear functions. The numerical results show that the new modifications are more efficient than recent CG methods in terms of number of iterations, number of function evaluations, number of gradient evaluations, and CPU time.

Introduction

We consider the following form for the unconstrained optimization problem:

$$ \min \bigl\{ f(x) |x \in R^{n}\bigr\} , $$

(1.1)

where $f:R^{n} \to R$ is a continuously differentiable function and its gradient is denoted by $g(x) = \nabla f(x)$. To solve (1.1) using the CG method, we use the following iterative method starting from the initial point $x_{0} \in R^{n}$. Then

$$ x_{k + 1} = x_{k} + \alpha _{k}d_{k}, \quad k = 0,1,2,\ldots, $$

(1.2)

where $\alpha _{k} > 0$ is the step size obtained by some line search. The search direction $d_{k}$ is defined by

$$ d_{k} = \textstyle\begin{cases} - g_{k},& k = 0, \\ - g_{k} + \beta _{k}d_{k - 1},&k \ge 1, \end{cases} $$

(1.3)

where $g_{k} = g(x_{k})$ and $\beta _{k}$ is known as the conjugate gradient method. To obtain the step-length $\alpha _{k}$, we have the following two line searches:

• 1. Exact line search

  $$ f(x_{k} + \alpha _{k}d_{k}) = \min f(x_{k} + \alpha d_{k}),\quad \alpha \ge 0. $$

  (1.4)

  However, (1.4) is computationally expensive if the function has many local minima.

• 2. Inexact line search

  To overcome the cost of using exact line search and obtain steps that are neither too long nor too short, we usually use inexact line search, in particular weak Wolfe–Powell (WWP) line search [1, 2] given as follows:

  $$\begin{aligned}& f(x_{k} + \alpha _{k}d_{k}) \le f(x_{k}) + \delta \alpha _{k}g_{k}^{T}d_{k}, \end{aligned}$$

  (1.5)
  $$\begin{aligned}& g(x_{k} + \alpha _{k}d_{k})^{T}d_{k} \ge \sigma g_{k}^{T}d_{k}. \end{aligned}$$

  (1.6)

Another, strong, version of Wolfe–Powell (SWP) line search is given by (1.5) and

$$ \bigl\vert g(x_{k} + \alpha _{k}d_{k})^{T}d_{k} \bigr\vert \le \sigma \bigl\vert g_{k}^{T}d_{k} \bigr\vert , $$

(1.7)

where $0 < \delta < \sigma < 1$.

The descent condition (downhill condition) plays an important role in the CG method, where the equation of the descent condition is given as follows:

$$ g_{k}^{T}d_{k} < 0. $$

(1.8)

Albaali [3] extended (1.8) to the following form:

$$ g_{k}^{T}d_{k} \le - c \Vert g_{k} \Vert ^{2},\quad k \ge 0\text{ and }c > 0, $$

(1.9)

called the sufficient descent condition.

The steepest descent method is the simplest of the gradient methods for optimization functions in n variables. From a current trial point $x_{1}$, for a function $f(x)$, one expects to find a vector close to a minimum by moving away from $x_{1}$ along the direction which causes $f(x)$ to decrease rapidly, i.e., $f(x_{1}) > f(x_{2}) > f(x_{3}) > \cdots$ . This direction of steepest descent is given by the negative gradient, $- g_{k}$. Using contour lines, the minimum point of a function is obtained with two variables. For example, Fig. 1 shows contour lines for Booth function in two dimensions.

Figure 1

Contour lines for Booth function

As we see in Fig. 2, the gradient $f'(x)$ is orthogonal with the contour lines, and for every x, the gradient point in the direction of the steepest increases $f(x)$. In Fig. 2, the gradient, contours, and Booth function are plotted, which clearly portrays the function’s minimum using the function or contour line graph. Despite the steepest descent method robustness, it is not efficient due to CPU time for large-dimensional functions. Thus, using the CG method will avoid the orthogonality between the ∇f and the search direction. Figure 3 shows the angle between the ∇f and $d_{k}$ using the CG method.

$$ \cos (\theta _{k}) = \biggl( - \frac{d_{k}^{T}g_{k}}{ \Vert d_{k} \Vert \Vert g_{k} \Vert } \biggr). $$

The most famous classical formulas of CG methods are Hestenses–Stiefel (HS) [3], Polak–Ribiere–Polyak (PRP) [4], Liu and Storey (LS) [5], Fletcher–Reeves (FR) [6], Fletcher (CD) [7], Dai and Yuan (DY) [8], given as follows:

$$\begin{aligned}& \beta _{k}^{\mathrm{HS}} = \frac{g_{k}^{T}y_{k - 1}}{d_{k - 1}^{T}y_{k - 1}}, \qquad \beta _{k}^{\mathrm{PRP}} = \frac{g_{k}^{T}y_{k - 1}}{ \Vert g_{k - 1} \Vert ^{2}}, \qquad \beta _{k}^{\mathrm{LS}} = - \frac{g_{k}^{T}y_{k - 1}}{d_{k - 1}^{T}g_{k - 1}}, \\& \beta _{k}^{\mathrm{FR}} = \frac{ \Vert g_{k} \Vert ^{2}}{ \Vert g_{k - 1} \Vert ^{2}}, \qquad \beta _{k}^{\mathrm{CD}} = - \frac{ \Vert g_{k} \Vert ^{2}}{d_{k - 1}^{T}g_{k - 1}}, \qquad \beta _{k}^{\mathrm{DY}} = \frac{ \Vert g_{k} \Vert ^{2}}{d_{k - 1}^{T}g_{k - 1}}, \end{aligned}$$

where $y_{k - 1} = g_{k} - g_{k - 1}$.

Figure 2

The graph of Booth function with contour lines and its gradients

Figure 3

The angle between the negative gradient and the search direction

These methods are similar if we use exact line search and a function satisfying quadratic line search condition since $g_{k}^{T}d_{k - 1} = 0$, which implies $g_{k}^{T}d_{k} = - \Vert g_{k} \Vert ^{2}$ using (1.3). In addition, if the function is quadratic, then $g_{k}^{T}g_{k - 1} = 0$.

The global convergence properties were studied by Zoutendijk [9] and Al-Baali [10]. The global convergence of the PRP method for a convex objective function under exact line search was proved by Polak and Ribere in [4]. Later, Powell [11] gave a counterexample showing a nonconvex function, in which PRP and HS can cycle infinitely without getting a solution. Powell emphasized the importance to achieve the global convergence of PRP and HS method, which should not be negative. Moreover, Gilbert and Nocedal [12] proved that nonnegative PRP, i.e., with $\beta _{k} = \max \{ \beta _{k}^{\mathrm{PRP}}, 0 \}$, is globally convergent under complicated line searches.

Since the function is quadratic, i.e., the step size is obtained by exact line search (1.4), the CG method satisfies the conjugacy condition, i.e., $d_{i}^{T}Hd_{j}^{T} = 0$, $\forall i \ne j$. Using the mean value theorem and exact line search with equation (1.3), we can obtain $\beta _{k}^{\mathrm{HS}}$. From the quasi-Newton method, BFGS method, the limited memory (LBFGS) method, and equation (1.3), Dai and Liao [13] proposed the following conjugacy condition:

$$ d_{k}^{T}y_{k - 1} = - tg_{k}^{T}s_{k - 1}, $$

(1.10)

where $s_{k - 1} = x_{k} - x_{k - 1}$, and $t \ge 0$. In the case of $t = 0$, equation (1.10) becomes the classical conjugacy condition. By using (1.3) and (1.10), [13] proposed the following CG formula:

$$ \beta _{k}^{\mathrm{DL}} = \frac{g_{k}^{T}y_{k - 1}}{d_{k - 1}^{T}y_{k - 1}} - t \frac{g_{k}^{T}s_{k - 1}}{d_{k - 1}^{T}y_{k - 1}}. $$

(1.11)

However, $\beta _{k}^{\mathrm{DL}}$ faces the same problem as $\beta _{k}^{\mathrm{PRP}}$ and $\beta _{k}^{\mathrm{HS}}$, i.e., $\beta _{k}^{\mathrm{DL}}$ is not nonnegative in general. Thus, [13] replaced equation (1.11) by

$$ \beta _{k}^{\mathrm{DL} +} = \max \bigl\{ \beta _{k}^{\mathrm{HS}},0\bigr\} - t\frac{g_{k}^{T}s_{k - 1}}{d_{k - 1}^{T}y_{k - 1}}. $$

(1.12)

Moreover, Hager and Zhang [14, 15] presented a modified CG parameter that satisfies the descent property for any inexact line search with $g_{k}^{T}d_{k} \le - (7/8) \Vert g_{k} \Vert ^{2}$. This new version of the CG method is globally convergent whenever the line search satisfies the (WP) line search requirement. This formula is given as follows:

$$ \beta _{k}^{\mathrm{HZ}} = \max \bigl\{ \beta _{k}^{N}, \eta _{k}\bigr\} , $$

(1.13)

where $\beta _{k}^{N} = \frac{1}{d_{k}^{T}y_{k}}(y_{k} - 2d_{k}\frac{ \Vert y_{k} \Vert ^{2}}{d_{k}^{T}y_{k}})^{T}g_{k}$, $\eta _{k} = - \frac{1}{ \Vert d_{k} \Vert \ \min \{ \eta , \Vert g_{k} \Vert \}}$, and $\eta > 0$ is a constant.

Note that if $t = 2\frac{ \Vert y_{k} \Vert ^{2}}{s_{k}^{T}y_{k}}$, then $\beta _{k}^{N} = \beta _{k}^{\mathrm{DY}}$.

In 2006, Wei et al. [16] gave a new positive CG method, which is quite similar to the original PRP method, which has global convergence under exact and inexact line search, that is,

$$ \beta _{k}^{\mathrm{WYL}} = \frac{g_{k}^{T}(g_{k} - \frac{ \Vert g_{k} \Vert }{ \Vert g_{k - 1} \Vert }g_{k - 1})}{ \Vert g_{k - 1} \Vert ^{2}}, $$

where $y_{k - 1} = g_{k} - g_{k - 1}$. From the WYL method, many modifications appeared, such as the following [17]:

$$\beta _{k}^{\mathrm{DPRP}} = \frac{ \Vert g_{k} \Vert ^{2} - \frac{ \Vert g_{k} \Vert }{ \Vert g_{k - 1} \Vert } \vert g_{k}g_{k - 1} \vert }{m \vert g_{k}^{T}d_{k - 1} \vert + \Vert g_{k - 1} \Vert ^{2}},\quad m \ge 1 \text{ [11]} $$

and

$$ \beta _{k}^{\mathrm{DHS}} = \frac{ \Vert g_{k} \Vert ^{2} - \frac{ \Vert g_{k} \Vert }{ \Vert g_{k - 1} \Vert }g_{k}^{T}g_{k - 1}}{m \vert g_{k}^{T}d_{k - 1} \vert + d_{k - 1}^{T}y_{k - 1}},\quad \text{where } m > 1. $$

Alhawarat et al. [18] constructed the following CG method with a new restart criterion as follows:

$$ \beta _{k}^{\mathrm{AZPRP}} = \textstyle\begin{cases} \frac{ \Vert g_{k} \Vert ^{2} - \mu _{k}. \vert g_{k}^{T}g_{k - 1} \vert }{ \Vert g_{k - 1} \Vert ^{2}},& \Vert g_{k} \Vert ^{2} > \mu _{k}. \vert g_{k}^{T}g_{k - 1} \vert , \\ 0, &\text{otherwise}, \end{cases} $$

where $\mu _{k} = \frac{ \Vert s_{k} \Vert }{ \Vert y_{k} \Vert }$, $s_{k} = x_{k} - x_{k - 1}$, $y_{k} = g_{k} - g_{k - 1}$, and $\Vert \cdot \Vert$ denotes the Euclidean norm.

Besides, Kaelo et al. [19] proposed the following CG formula:

$$ \beta _{k}^{\mathrm{PKT}} = \textstyle\begin{cases} \frac{ \Vert g_{k} \Vert ^{2} - g_{k}^{T}g_{k - 1}}{\max \{ d_{k - 1}^{T}y_{k - 1}, - g_{k - 1}^{T}d_{k - 1}\}},& \text{if } 0 < g_{k}^{T}g_{k - 1} < \Vert g_{k} \Vert ^{2}, \\ \frac{ \Vert g_{k} \Vert ^{2}}{\max \{ d_{k - 1}^{T}y_{k - 1}, - g_{k - 1}^{T}d_{k - 1}\}}, &\text{otherwise}. \end{cases} $$

Motivation and the new restarted formula

To improve the efficiency of $\beta _{k}^{\mathrm{AZPRP}}$ in terms of function evaluation, gradient evaluation, number of iterations, and CPU time, we construct two new CG methods based on $\beta _{k}^{\mathrm{AZPRP}}$, $\beta _{k}^{\mathrm{DPRP}}$, and $\beta _{k}^{\mathrm{DHS}}$ as follows:

$$ \beta _{k}^{A1} = \textstyle\begin{cases} \frac{ \Vert g_{k} \Vert ^{2} - \mu _{k} \vert g_{k}^{T}g_{k - 1} \vert }{m \vert g_{k}^{T}d_{k - 1} \vert + \Vert g_{k - 1} \Vert ^{2}},& \text{if } \Vert g_{k} \Vert ^{2} > \mu _{k} \vert g_{k}^{T}g_{k - 1} \vert , \\ - \mu _{k}\frac{g_{k}^{T}s_{k - 1}}{d_{k - 1}^{T}y_{k - 1}},& \text{otherwise}, \end{cases} $$

(2.1)

where

$$ \mu _{k} = \frac{ \Vert s_{k - 1} \Vert }{ \Vert y_{k - 1} \Vert }. $$

(2.2)

The second modification is given as follows:

$$ \beta _{k}^{A2} = \textstyle\begin{cases} \frac{ \Vert g_{k} \Vert ^{2} - \mu _{k} \vert g_{k}^{T}g_{k - 1} \vert }{m \vert g_{k}^{T}d_{k - 1} \vert + d_{k - 1}^{T}y_{k - 1}}, &\text{if } \Vert g_{k} \Vert ^{2} > \mu _{k} \vert g_{k}^{T}g_{k - 1} \vert , \\ - \mu _{k}\frac{g_{k}^{T}s_{k - 1}}{d_{k - 1}^{T}y_{k - 1}},& \text{otherwise}. \end{cases} $$

(2.3)

Algorithm 2.1

The global convergence properties

Assumption 1

• I. $f(x)$ is bounded from below on the level set $\Omega = \{ x \in R^{n}:f(x) \le f(x_{1})\}$, where $x_{1}$ is the starting point.

• II. In some neighborhood N of Ω, f is continuous and differentiable, and its gradient is Lipchitz continuous. That is, for any $x,y \in N$, there exists a constant $L > 0$ such that

  $$ \bigl\Vert g(x) - g(y) \bigr\Vert \le L \Vert x - y \Vert . $$

The following is considered one of the most important lemmas used to prove the global convergence properties. For more details, the reader can refer to [9].

Lemma 3.1

Suppose Assumption 1holds. Considering the CG method of the form (1.3), where the search direction satisfies the sufficient descent condition and $\alpha _{k}$ exists by standard WWP line search, we have

$$ \sum_{k = 0}^{\infty } \frac{(g_{k}^{T}d_{k})^{2}}{ \Vert d_{k} \Vert ^{2}} < \infty , $$

(3.1)

where (3.1) is known as the Zoutendijk condition. Inequality (3.1) also holds for the exact line search, the Armijo-Goldstein line search, and the SWP line search.

Substituting (1.9) into (3.1) yields

$$ \sum_{k = 0}^{\infty } \frac{ \Vert g_{k} \Vert ^{4}}{ \Vert d_{k} \Vert ^{2}} < \infty . $$

(3.2)

Gilbert and Nocedal [11] presented an important theorem to find the global convergence of nonnegative PRP and nonnegative methods summarized by Theorem 3.3. Furthermore, they presented a nice property, called Property*, as follows:

Property*

Consider a method of the form (1.1) and (1.2), and suppose $0 < \gamma \le \Vert g_{k} \Vert \le \bar{\gamma }$. We say that the method possesses Property* if there exist constant $b > 1$ and $\lambda > 0$ such that for all $k \ge 1$, we get $\vert \beta _{k} \vert \le b$, and if $\Vert x_{k} - x_{k - 1} \Vert \le \lambda$, then

$$ \vert \beta _{k} \vert \le \frac{1}{2b}. $$

The following theorem plays a crucial role in the CG method given in [11].

Theorem 3.1

Considering any CG method of the form (1.2) and (1.3), suppose the following conditions hold:

• I. $\beta _{k} > 0$.

• II. The sufficient descent condition is satisfied.

• III. The Zoutendijk condition holds.

• IV. Property* is true.

• V. Assumption 1is satisfied.

Then, the iterates are globally convergent, i.e., $\lim_{k \to \infty } \Vert g_{k} \Vert = 0$.

The global convergence properties of $\beta _{k}^{A1}$

Theorem 3.2

Suppose that Assumption 1holds. Then, by considering the CG method of the form (1.2), (1.3), and (2.1), where $\alpha _{k}$ is computed by (1.5) and (1.6) and the sufficient descent condition holds, we multiply (1.2) by $g_{k}^{T}$, which yields

$$\begin{aligned} g_{k}d_{k} &= - \Vert g_{k} \Vert ^{2} + \frac{ \Vert g_{k} \Vert ^{2} - \mu _{k} \vert g_{k}^{T}g_{k - 1} \vert }{m \vert g_{k}^{T}d_{k - 1} \vert + \Vert g_{k - 1} \Vert ^{2}} {g}_{k}^{T}d_{k - 1} \\ &\le - \Vert g_{k} \Vert ^{2} + \frac{ \Vert g_{k} \Vert ^{2}}{m \vert g_{k}^{T}d_{k - 1} \vert + \Vert g_{k - 1} \Vert ^{2}} \bigl\vert {g}_{k}^{T}d_{k - 1} \bigr\vert \\ &\le - \Vert g_{k} \Vert ^{2} + \frac{ \Vert g_{k} \Vert ^{2}}{m \vert g_{k}^{T}d_{k - 1} \vert } \bigl\vert {g}_{k}^{T}d_{k - 1} \bigr\vert \\ &\le - \Vert g_{k} \Vert ^{2} + \frac{ \Vert g_{k} \Vert ^{2}}{m \vert g_{k}^{T}d_{k - 1} \vert } \bigl\vert {g}_{k}^{T}d_{k - 1} \bigr\vert \\ &\le \Vert g_{k} \Vert ^{2} \biggl( - 1 + \frac{1}{m} \biggr). \end{aligned}$$

Theorem 3.3

Suppose that Assumption 1holds. Consider the CG method of the form (1.2), (1.3), and (2.3), where $\alpha _{k}$ is computed by (1.5) and (1.6), then $\beta _{k}^{A1}$ satisfies Property*.

Proof

Let $\lambda = \frac{\gamma ^{2}}{2L(L + 1)\lambda \bar{\gamma } b}$ and

$$\beta _{k}^{A1} = \frac{ \Vert g_{k} \Vert ^{2} - \mu _{k} \vert g_{k}^{T}g_{k - 1} \vert }{m \vert g_{k}^{T}d_{k - 1} \vert + \Vert g_{k - 1} \Vert ^{2}} \le \frac{ \Vert g_{k} \Vert ^{2} + \vert g_{k}^{T}g_{k - 1} \vert }{ \Vert g_{k - 1} \Vert ^{2}} \le \frac{ \Vert g_{k} \Vert ( \Vert g_{k} \Vert + \Vert g_{k - 1} \Vert )}{ \Vert g_{k - 1} \Vert ^{2}} \le \frac{2\bar{\gamma }^{2}}{\gamma ^{2}} = b > 1. $$

To show that $\beta _{k}^{A1} \le \frac{1}{2b}$, we have the following two cases:

Case 1: $\mu _{k} > 1$

$$\begin{aligned} \beta _{k}^{A1} &= \frac{ \Vert g_{k} \Vert ^{2} - \mu _{k} \vert g_{k}^{T}g_{k - 1} \vert }{m \vert g_{k}^{T}d_{k - 1} \vert + \Vert g_{k - 1} \Vert ^{2}} \le \frac{ \Vert g_{k} \Vert ^{2} - \vert g_{k}^{T}g_{k - 1} \vert }{ \Vert g_{k - 1} \Vert ^{2}} \\ &\le \frac{ \Vert g_{k} \Vert \Vert g_{k} - g_{k - 1} \Vert }{ \Vert g_{k - 1} \Vert ^{2}} \le \frac{L\lambda \bar{\gamma }}{\gamma ^{2}}. \end{aligned}$$

Case 2: $\mu _{k} < 1$

To satisfy Property* for $\beta _{k}^{A1}$ with $\mu _{k} < 1$, we need the following inequality:

$$ \Vert w_{k} \Vert + \Vert v_{k} \Vert \le L \Vert w_{k} + v_{k} \Vert , $$

(3.3)

where $w_{k} = g_{k} - \frac{1}{L}g_{k - 1}$, and $v_{k} = \frac{1}{L}g_{k} - g_{k - 1}$, which yields

$$\begin{aligned} \bigl\vert \beta _{k}^{A1} \bigr\vert &\le \biggl\vert \frac{ \Vert g_{k} \Vert ^{2} - \mu _{k} \vert g_{k}^{T}g_{k - 1} \vert }{m \vert g_{k}^{T}d_{k - 1} \vert + \Vert g_{k - 1} \Vert ^{2}} \biggr\vert \le \biggl\vert \frac{ \Vert g_{k} \Vert ^{2} - \frac{1}{L} \vert g_{k}^{T}g_{k - 1} \vert }{ \Vert g_{k - 1} \Vert ^{2}} \biggr\vert \\ & \le \frac{ \Vert g_{k} \Vert \Vert g_{k} - \frac{1}{L}g_{k - 1} \Vert }{ \Vert g_{k - 1} \Vert ^{2}}. \end{aligned}$$

Using (3.3), we obtain

$$\begin{aligned}& \biggl\Vert g_{k} - \frac{1}{L}g_{k - 1} \biggr\Vert \le L \biggl\Vert g_{k} - \frac{1}{L}g_{k - 1} + \frac{1}{L}g_{k} - g_{k - 1} \biggr\Vert \le ({L} + 1) \Vert g_{k} - g_{k - 1} \Vert , \\& \bigl\vert \beta _{k}^{A1} \bigr\vert \le \frac{(L + 1) \Vert g_{k} \Vert \Vert g_{k} - g_{k - 1} \Vert }{ \Vert g_{k - 1} \Vert ^{2}}\le L\frac{(L + 1)\lambda \bar{\gamma }}{\gamma ^{2}}. \end{aligned}$$

Thus, in all cases

$$\bigl\vert \beta _{k}^{A1} \bigr\vert \le \frac{L\lambda \bar{\gamma }}{\gamma ^{2}} \le \frac{L(L + 1)\lambda \bar{\gamma }}{\gamma ^{2}} \le \frac{1}{2b}. $$

The proof is completed. □

Theorem 3.4

Suppose that Assumption 1holds. Consider the CG method of the form (1.2), (1.3), and (2.3), where $\alpha _{k}$ is computed by (1.5) and (1.6), then $\lim_{k \to \infty } \Vert g_{k} \Vert = 0$.

Proof

We will apply Theorem 3.1. Note that the following properties hold for $\beta _{k}^{A1}$:

• i. $\beta _{k}^{A1} > 0$.

• ii. $\beta _{k}^{A1}$ satisfies Property* using Theorem 3.3.

• iii. $\beta _{k}^{A1}$ satisfies the descent property using Theorem 3.2.

• iv. Assumption 1 holds.

Thus, all properties in Theorem 3.1 are satisfied, which leads to $\lim_{k \to \infty } \Vert g_{k} \Vert = 0$. □

The global convergence properties of $\beta _{k}^{A2}$

Theorem 3.5

Suppose Assumption 1holds. Consider the CG method of the form (1.2), (1.3), and (2.3), where $\alpha _{k}$ is computed by (1.5) and (1.6), and where the sufficient descent condition holds for $\beta _{k}^{A2}$. Since $d_{k - 1}^{T}y_{k - 1} \ge 0$, we obtain

$$\begin{aligned} g_{k}d_{k}& = - \Vert g_{k} \Vert ^{2} + \frac{ \Vert g_{k} \Vert ^{2} - \mu _{k} \vert g_{k}^{T}g_{k - 1} \vert }{m \vert g_{k}^{T}d_{k - 1} \vert + d_{k - 1}^{T}y_{k - 1}} {g}_{k}^{T}d_{k - 1}\\ &\le - \Vert g_{k} \Vert ^{2} + \frac{ \Vert g_{k} \Vert ^{2}}{m \vert g_{k}^{T}d_{k - 1} \vert } \bigl\vert {g}_{k}^{T}d_{k - 1} \bigr\vert \\ &\le - \Vert g_{k} \Vert ^{2} + \frac{ \Vert g_{k} \Vert ^{2}}{m \vert g_{k}^{T}d_{k - 1} \vert } \bigl\vert {g}_{k}^{T}d_{k - 1} \bigr\vert \\ &\le \Vert g_{k} \Vert ^{2} \biggl( - 1 + \frac{1}{m} \biggr). \end{aligned}$$

Theorem 3.6

Suppose that Assumption 1holds. Consider the CG method of the form (1.2), (1.3), and (2.3), where $\alpha _{k}$ is computed by (1.5) and (1.6), then the iterates $\beta _{k}^{A2}$ satisfy Property*.

Proof

Let $\lambda = \frac{(1 - \sigma )c\gamma ^{2}}{2L(L + 1)\bar{\gamma } b}$ and

$$\begin{aligned} \beta _{k}^{A2} &= \frac{ \Vert g_{k} \Vert ^{2} - \mu _{k} \vert g_{k}^{T}g_{k - 1} \vert }{m \vert g_{k}^{T}d_{k - 1} \vert + d_{k - 1}^{T}y_{k - 1}} \le \frac{ \Vert g_{k} \Vert ^{2} - \mu _{k} \vert g_{k}^{T}g_{k - 1} \vert }{d_{k - 1}^{T}y_{k - 1}}\\ & \le \frac{ \Vert g_{k} \Vert ^{2} - \mu _{k} \vert g_{k}^{T}g_{k - 1} \vert }{(1 - \sigma )c \Vert g_{k - 1} \Vert ^{2}}\le \frac{ \Vert g_{k} \Vert ^{2} + \vert g_{k}^{T}g_{k - 1} \vert }{(1 - \sigma )c \Vert g_{k - 1} \Vert ^{2}}\\ & \le \frac{ \Vert g_{k} \Vert ( \Vert g_{k} \Vert + \Vert g_{k - 1} \Vert )}{(1 - \sigma )c \Vert g_{k - 1} \Vert ^{2}} \le \frac{2\bar{\gamma }^{2}}{(1 - \sigma )c\gamma ^{2}} = b > 1. \end{aligned}$$

To show that $\beta _{k}^{A2} \le \frac{1}{2b}$, we have the following two cases:

Case $\mu _{k} > 1$

$$\begin{aligned} \beta _{k}^{A2} &= \frac{ \Vert g_{k} \Vert ^{2} - \mu _{k} \vert g_{k}^{T}g_{k - 1} \vert }{m \vert g_{k}^{T}d_{k - 1} \vert + d_{k - 1}^{T}y_{k - 1}} \le \frac{ \Vert g_{k} \Vert ^{2} - \vert g_{k}^{T}g_{k - 1} \vert }{d_{k - 1}^{T}y_{k - 1}} \\ &\le \frac{ \Vert g_{k} \Vert \Vert g_{k} - g_{k - 1} \Vert }{(1 - \sigma )c \Vert g_{k - 1} \Vert ^{2}} \le \frac{L\lambda \bar{\gamma }}{(1 - \sigma )c\gamma ^{2}}.\vadjust{\goodbreak} \end{aligned}$$

Case $\mu _{k} < 1$

To satisfy Property* for $\beta _{k}^{A1}$ with $\mu _{k} < 1$, we need property (3.3) which gives

$$\begin{aligned} \bigl\vert \beta _{k}^{A2} \bigr\vert & \le \biggl\vert \frac{ \Vert g_{k} \Vert ^{2} - \mu _{k} \vert g_{k}^{T}g_{k - 1} \vert }{m \vert g_{k}^{T}d_{k - 1} \vert + d_{k - 1}^{T}y_{k - 1}} \biggr\vert \\ & \le \biggl\vert \frac{ \Vert g_{k} \Vert ^{2} - \frac{1}{L} \vert g_{k}^{T}g_{k - 1} \vert }{d_{k - 1}^{T}y_{k - 1}} \biggr\vert \\ &\le \biggl\vert \frac{ \Vert g_{k} \Vert \Vert g_{k} - \frac{1}{L}g_{k - 1} \Vert }{(1 - \sigma )c \Vert g_{k - 1} \Vert ^{2}} \biggr\vert . \end{aligned}$$

Using (3.3), we obtain

$$\begin{aligned}& \biggl\Vert g_{k} - \frac{1}{L}g_{k - 1} \biggr\Vert \le L \biggl\Vert g_{k} - \frac{1}{L}g_{k - 1} + \frac{1}{L}g_{k} - g_{k - 1} \biggr\Vert \le ({L} + 1) \Vert g_{k} - g_{k - 1} \Vert , \\& \bigl\vert \beta _{k}^{A2} \bigr\vert \le \frac{(L + 1) \Vert g_{k} \Vert \Vert g_{k} - g_{k - 1} \Vert }{(1 - \sigma )c \Vert g_{k - 1} \Vert ^{2}} \le L\frac{(L + 1)\lambda \bar{\gamma }}{(1 - \sigma )c\gamma ^{2}}. \end{aligned}$$

Thus, in all cases

$$ \bigl\vert \beta _{k}^{A2} \bigr\vert \le \frac{L\lambda \bar{\gamma }}{(1 - \sigma )c\gamma ^{2}} \le L\frac{(L + 1)\lambda \bar{\gamma }}{(1 - \sigma )c\gamma ^{2}} \le \frac{1}{2b}. $$

 □

Theorem 3.7

Suppose that Assumption 1holds. Consider the CG method of the form (1.2), (1.3), and (2.3), i.e., $\beta _{k}^{A2}$, where $\alpha _{k}$ is computed by (1.5) and (1.6), then $\lim_{k \to \infty } \Vert g_{k} \Vert = 0$.

Proof

We will apply Theorem 3.1. Note that the following properties hold for $\beta _{k}^{A2}$:

• i. $\beta _{k}^{A2} > 0$.

• ii. $\beta _{k}^{A2}$ satisfies Property* by using Theorem 3.6.

• iii. $\beta _{k}^{A2}$ satisfies the descent property by using Theorem 3.5.

• iv. Assumption 1 holds.

Thus all properties in Theorem 3.1 are satisfied, which leads to $\lim_{k \to \infty } \Vert g_{k} \Vert = 0$.

If the condition $\Vert g_{k} \Vert ^{2} > \mu _{k} \vert g_{k}^{T}g_{k - 1} \vert$ does not hold for $\beta _{k}^{A1}$ and $\beta _{k}^{A2}$, then the CG method will be restarted using $\beta _{k}^{D - H} = - \mu _{k}\frac{g_{k}^{T}s_{k - 1}}{d_{k - 1}^{T}y_{k - 1}}$. □

The following two theorems show that the CG method with $\beta _{k}^{D - H}$ has the descent and convergence properties.

Theorem 3.8

Let sequences $\{ x_{k}\}$ and $\{ d_{k}\}$ be obtained using Eqs. (1.2) and (1.3), which is computed by SWP line search in Eqs. (1.5) and (1.7), then the descent condition holds for $\{ d_{k}\}$ with $\beta _{k}^{D - H}$.

Proof

By multiplying Eq. (1.3) with $g_{k}^{T}$, and substituting $\beta _{k}^{D - H}$, we obtain

$$\begin{aligned} g_{k}^{T}d_{k} &= - \Vert g_{k} \Vert ^{2} - t\frac{g_{k}^{T}s_{k - 1}}{d_{k}^{T}y_{k - 1}} g_{k}^{T}d_{k - 1} \\ &= - \Vert g_{k} \Vert ^{2} - t\alpha _{k}\frac{ \Vert g_{k}^{T}d_{k - 1}^{T} \Vert ^{2}}{d_{k}^{T}y_{k - 1}} \le - \Vert g_{k} \Vert ^{2}. \end{aligned}$$

Letting $c = 1$, we then obtain

$$ g_{k}^{T}d_{k} \le - c \Vert g_{k} \Vert ^{2}, $$

which completes the proof. □

Theorem 3.9

Assume that Assumption 1holds. Consider the conjugate gradient method in (1.2) and (1.3) with $\beta _{k}^{D - H}$ a descent direction and $\alpha _{k}$ obtained by the strong Wolfe line search. Then, $\lim \inf_{ k \to \infty } \Vert g_{k} \Vert = 0$.

Proof

We will prove this theorem by contradiction. Suppose Theorem 3.4 is not true. Then, a constant $\varepsilon > 0$ exists such that

$$ \Vert g_{k} \Vert \ge \varepsilon , \quad \forall k \ge 1. $$

(3.4)

By squaring both sides of (1.2), we obtain

$$\begin{aligned}& \begin{aligned} \Vert d_{k} \Vert ^{2} &= \Vert g_{k} \Vert ^{2} - 2\beta _{k}g_{k}^{T}d_{k - 1} + \beta _{k}^{2} \Vert d_{k - 1} \Vert ^{2} \\ &\le \Vert g_{k} \Vert ^{2} + 2 \vert \beta _{k} \vert \bigl\vert g_{k}^{T}d_{k - 1} \bigr\vert + \beta _{k}^{2} \Vert d_{k - 1} \Vert ^{2} \\ &\le \Vert g_{k} \Vert ^{2} + \frac{2}{L} \frac{ \Vert g_{k} \Vert \Vert s_{k} \Vert }{(1 - \sigma ) \vert g_{k - 1}^{T}d_{k - 1} \vert } ( \sigma ) \bigl\vert g_{k - 1}^{T}d_{k - 1} \bigr\vert + \frac{1}{L^{2}}\frac{ ( ( \sigma )g_{k - 1}^{T}d_{k - 1} )^{2} \Vert s_{k - 1} \Vert ^{2}}{ ( (1 - \sigma )g_{k - 1}^{T}d_{k - 1} )^{2}}\\ & \le \Vert g_{k} \Vert ^{2} + \frac{2}{L} \frac{ \Vert g_{k} \Vert \Vert s_{k} \Vert }{(1 - \sigma )}\sigma + \frac{1}{L^{2}}\frac{ ( \sigma )^{2} \Vert s_{k - 1} \Vert ^{2}}{(1 - \sigma )^{2}}, \end{aligned} \\& \begin{aligned} \frac{ \Vert d_{k} \Vert ^{2}}{ \Vert g_{k} \Vert ^{4}}& \le \frac{ \Vert g_{k} \Vert ^{2}}{ \Vert g_{k} \Vert ^{4}} + \frac{2}{L}\frac{ \Vert g_{k} \Vert \Vert s_{k} \Vert }{(1 - \sigma ) \Vert g_{k} \Vert ^{4}}\sigma + \frac{1}{L^{2}} \frac{\sigma ^{2} \Vert s_{k - 1} \Vert ^{2}}{(1 - \sigma )^{2} \Vert g_{k} \Vert ^{4}} \\ &\le \frac{1}{ \Vert g_{k} \Vert ^{2}} + \frac{2}{L}\frac{ \Vert g_{k} \Vert \Vert s_{k} \Vert }{(1 - \sigma ) \Vert g_{k} \Vert ^{4}}\sigma + \frac{1}{L^{2}}\frac{\sigma ^{2} \Vert s_{k - 1} \Vert ^{2}}{(1 - \sigma )^{2} \Vert g_{k} \Vert ^{4}} \\ &\le \frac{1}{ \Vert g_{k} \Vert ^{2}} + \frac{2}{L}\frac{ \Vert s_{k} \Vert }{(1 - \sigma ) \Vert g_{k} \Vert ^{3}}\sigma + \frac{1}{L^{2}}\frac{\sigma ^{2} \Vert s_{k - 1} \Vert ^{2}}{(1 - \sigma )^{2} \Vert g_{k} \Vert ^{4}}. \end{aligned} \end{aligned}$$

Let

$$ \Vert g_{k} \Vert ^{q} = \min \bigl\{ \Vert g_{k} \Vert ^{2}, \Vert g_{k} \Vert ^{3}, \Vert g_{k} \Vert ^{4} \bigr\} , \quad q \in N, $$

then

$$ \frac{ \Vert d_{k} \Vert ^{2}}{ \Vert g_{k} \Vert ^{4}} \le \frac{1}{ \Vert g_{k} \Vert ^{q}} \biggl( 1 + \frac{2}{L}\frac{\lambda }{(1 - \sigma )}\sigma + \frac{1}{\lambda ^{2}} \frac{\sigma ^{2}\lambda ^{2}}{(1 - \sigma )^{2}} \biggr). $$

Also, let

$$ R = \biggl( 1 + \frac{2}{L}\frac{\lambda }{(1 - \sigma )}\sigma + \frac{1}{\lambda ^{2}}\frac{\sigma ^{2}\lambda ^{2}}{(1 - \sigma )^{2}} \biggr), $$

then

$$\begin{aligned}& \frac{ \Vert d_{k} \Vert ^{2}}{ \Vert g_{k} \Vert ^{4}} \le \frac{R}{ \Vert g_{k} \Vert ^{q}} \le R\sum _{i = 1}^{k} \frac{1}{ \Vert g_{i} \Vert ^{q}}, \\& \frac{ \Vert g_{k} \Vert ^{4}}{ \Vert d_{k} \Vert ^{2}} \ge \frac{\varepsilon ^{q}}{kR}. \end{aligned}$$

Therefore,

$$ \sum_{k = 0}^{\infty } \frac{ \Vert g_{k} \Vert ^{4}}{ \Vert d_{k} \Vert ^{2}} = \infty . $$

 □

Numerical results and discussions

To analyze the efficiency of the new CG method, several test functions are selected from CUTE [20], as shown in the Appendix. These functions can be obtained from the following website:

• http://ccpforge.cse.rl.ac.uk/gf/project/cutest/wiki/

In the Appendix, the following notations are defined as follows:

• No. iter means the number of iterations.

• No. fun. Eva means the number of function evaluations.

• No. Grad. Eva means the number of gradient evaluations.

The comparison was made with respect to CPU time, the number of function evaluations, the number of iterations, and the number of gradient evaluations. The SWP line search is employed with the following parameters of $\delta = 0.01$ and $\sigma = 0.1$. The modified CG-Descent 6.8 with zero memory is employed to obtain the result for $\beta _{k}^{A1}$, $\beta _{k}^{A2}$. The code can be downloaded from the Hager webpage:

• http://users.clas.ufl.edu/hager/papers/Software/

A minimum time of 0.02 seconds is used for all algorithms. The host computer is an Intel® Dual-Core CPU with 2 GB of DDR2 RAM. The results are shown in Figs. 4, 5, 6, and 7, in which a performance measure introduced by Dolan and Moré [21] was employed.

Figure 4

Performance profile based on the number of iteration

Figure 5

Performance profile based on the CPU time

Figure 6

Performance profile based on the functions evaluation time

Figure 7

Performance profile based on the gradient evaluations

It is clear that based on the left-hand side of Figs. 4, 5, 6, and 7, the CG method A1 is above the other curves. Therefore, it is the most efficient method among related AZPRP methods. However, CG method A2 is not as efficient as A1. Still, it is more efficient than AZPRP with respect to CPU time, the number of function evaluations, gradient evaluations, and the number of iterations. In addition, as an application of the CG method in image restoration, the reader can refer to the following references [22–24].

Conclusion

In this paper, we proposed two efficient conjugate gradient methods related to the AZPRP method. The two methods satisfied global convergence properties and the descent property when SWP line searches were employed. Furthermore, our numerical results showed that the new methods are more efficient than the AZPRP method with respect to the number of iterations, gradient evaluations, function evaluations, and CPU time.

Appendix

Function	Dim	A1 CG method				A2 CG method				AZPRP CG method
		No. Iter	No. fun Eva.	No. Grad Eva	CPU Time	No. Iter	No. fun Eva.	No. Grad Eva	CPU Time	No. Iter	No. fun Eva.	No. Grad Eva	CPU Time
AKIVA	2	8	20	15	0.02	8	20	15	0.02	8	20	15	0.02
ALLINITU	4	9	25	18	0.02	9	25	18	0.02	9	25	18	0.02
ARGLINA	200	1	3	2	0.02	1	3	2	0.02	1	3	2	0.02
ARWHEAD	200	6	16	12	0.02	6	16	12	0.02	6	16	12	0.02
BARD	3	12	32	22	0.02	12	32	22	0.02	12	32	22	0.02
BDQRTIC	5000	161	352	334	0.44	25	76	65	0.13	157	334	315	0.66
BEALE	2	11	33	26	0.02	11	33	26	0.02	11	33	26	0.02
BIGGS6	6	24	64	44	0.02	24	64	44	0.02	24	64	44	0.02
BOX3	3	10	23	14	0.02	10	23	14	0.02	10	23	14	0.02
BOX	10,000	7	25	21	0.14	8	27	23	0.08	7	24	20	0.08
BRKMCC	2	5	11	6	0.02	5	11	6	0.02	5	11	6	0.02
BROWNAL	200	5	15	11	0.02	11	53	46	0.02	10	26	18	0.02
BROWNBS	2	10	24	18	0.02	10	24	18	0.02	10	24	18	0.02
BROWNDEN	4	16	38	31	0.02	16	38	31	0.02	16	38	31	0.02
BROYDN7D	5000	84	157	115	0.44	103	180	143	0.47	11	192	153	0.47
BRYBND	5000	85	193	117	0.27	55	127	82	0.2	85	198	124	0.28
CHAINWOO	4000	318	635	393	0.7	446	934	589	0.91	346	691	418	0.67
CHNROSNB	50	372	779	420	0.02	47	108	69	0.02	358	747	400	0.02
CLIFF	2	10	46	39	0.02	10	46	39	0.02	10	46	39	0.02
COSINE	10,000	13	57	47	0.17	12	54	48	0.19	14	56	49	0.22
CRAGGLVY	5000	177	363	309	0.91	88	179	140	0.39	104	221	176	0.41
CUBE	2	17	48	34	0.02	17	48	34	0.02	17	48	34	0.02
CURLY10	10,000	50,576	70,093	81,672	197	48,772	70,747	75,603	165.28	42,321	61,798	65,202	134
CURLY20	10,000	74,906	97,403	1E+05	446.39	78,246	104,075	130,745	374	67,898	90,440	1E+05	390
CURLY30	10,000	76,869	100,202	1E+05	648	73,218	96,533	123,259	639.63	73,218	96,533	1E+05	582
DECONVU	63	313	637	327	2.00E−02	164	392	245	2.00E−02	223	453	232	2.00E−02
DENSCHNA	2	6	16	12	0.02	6	16	12	0.02	6	16	12	0.02
DENSCHNB	2	6	18	15	0.02	6	18	15	0.02	6	18	15	0.02
DENSCHNC	2	11	36	31	0.02	11	36	31	0.02	11	36	31	0.02
DENSCHND	3	14	46	40	0.02	14	46	40	0.02	14	46	40	0.02
DENSCHNE	3	12	43	38	0.02	12	43	38	0.02	12	43	38	0.02
DENSCHNF	2	9	31	26	0.02	9	31	26	0.02	9	31	26	0.02
DIXMAANA	3000	6	15	11	0.02	6	15	11	0.02	5	13	10	0.02
DIXMAANB	3000	6	16	12	0.02	6	16	12	0.02	6	16	12	0.02
DIXMAANC	3000	6	14	9	0.02	6	14	9	0.02	6	14	9	0.02
DIXMAAND	3000	7	17	12	0.02	7	17	12	0.02	6	15	11	0.02
DIXMAANE	3000	218	245	419	0.23	256	283	495	0.3	218	242	422	0.3
DIXMAANF	3000	125	255	133	0.11	129	263	137	0.12	140	285	148	0.19
DIXMAANG	3000	170	345	178	0.16	169	343	177	0.13	174	353	182	0.14
DIXMAANH	3000	176	358	185	0.16	186	377	194	0.14	173	353	184	0.14
DIXMAANI	3000	2994	3083	5909	3.28	3174	3248	6284	3.7	3264	3359	6443	3.3
DIXMAANJ	3000	363	731	371	0.28	345	695	353	0.31	384	773	392	0.31
DIXMAANK	3000	304	613	312	0.25	398	801	406	0.31	401	806	408	0.34
DIXMAANL	3000	342	691	353	0.27	379	765	390	0.31	430	867	441	0.49
DIXON3DQ	10,000	10,000	10,007	19	0.78	10,000	10,007	19,995	19.12	10,000	10,007	19,995	19.12
DJTL	2	75	1163	1148	0.02	75	1163	1148	0.02	75	1163	1148	0.02
DQDRTIC	5000	5	11	6	0.02	5	11	6	0.02	5	11	6	0.02
DQRTIC	5000	15	32	18	0.03	15	32	18	0.02	15	32	18	0.02
EDENSCH	2000	31	70	54	0.05	34	74	67	0.06	30	67	57	0.08
EG2	1000	3	8	5	0.02	3	8	5	0.02	3	8	5	0.02
EIGENALS	2550	8785	16,438	9953	141	17,061	32,415	18,795	280	11,275	20,477	13,384	194.39
EIGENBLS	2550	19,480	38,968	19,489	284	234	548	335	5.14	34,599	69,207	34,609	589
EIGENCLS	2652	11,704	23,434	11,740	180.05	9800	19,062	10,385	162.48	9838	18,888	10,710	185
ENGVAL1	5000	23	45	40	0.05	23	47	41	0.06	23	45	40	0.06
ENGVAL2	3	26	73	55	0.02	26	73	55	0.02	26	73	55	0.02
ERRINROS	50	102,326	201,278	1E+05	2.78	104	260	178	0.02	82,469	2E+05	86,569	2.08
EXPFIT	2	9	29	22	0.02	9	29	22	0.02	9	29	22	0.02
EXTROSNB	1000	2359	5279	3112	0.8	69	193	145	0.03	2205	4964	2945	0.86
FLETCBV2	5000	1	1	1	0.02	1	1	1	0.02	1	1	1	0.02
FLETCHCR	1000	71	153	85	0.03	29	68	43	0.05	88	178	114	0.03
FMINSRF2	5625	426	875	453	1.31E+00	1803	3546	1849	4.59E+00	459	940	486	1.58E+00
FMINSURF	5625	562	1140	584	1.67	1327	2705	1384	4.08	548	1118	575	2.06
FREUROTH	5000	21	54	47	0.09	34	86	85	0.17	27	63	58	0.19
GENHUMPS	5000	13,718	30,411	16,957	46	467	1482	1090	3.33	11,853	35,531	25,448	68.94
GENROSE	500	1831	3888	2134	0.55	61	199	156	0.03	2057	4360	2378	0.47
GROWTHLS	3	109	431	369	0.02	109	431	369	0.03	109	431	369	0.03
GULF	3	33	95	72	0.02	33	95	72	0.02	33	95	72	0.02
HAIRY	2	17	82	68	0.02	17	82	68	0.02	17	82	68	0.02
HATFLDD	3	17	49	37	0.02	17	49	37	0.02	17	49	37	0.02
HATFLDE	3	13	37	30	0.02	13	37	30	0.02	13	37	30	0.02
HATFLDFL	3	21	68	54	0.02	21	68	54	0.02	21	68	54	0.02
HEART6LS	6	375	1137	876	0.02	375	1137	876	0.02	375	1137	876	0.02
HEART8LS	8	253	657	440	0.02	253	657	440	0.02	253	657	440	0.02
HELIX	3	23	60	42	0.02	23	60	42	0.02	23	60	42	0.02
HIELOW	3	13	30	21	0.03	13	30	21	0.02	13	30	21	0.03
HILBERTA	2	2	5	3	0.02	2	5	3	0.02	2	5	3	0.02
HILBERTB	10	4	9	5	0.02	4	9	5	0.02	4	9	5	0.02
HIMMELBB	2	4	18	18	0.02	4	18	18	0.02	4	18	18	0.02
HIMMELBF	4	23	59	46	0.02	23	59	46	0.02	23	59	46	0.02
HIMMELBG	2	7	22	17	0.02	7	22	17	0.02	7	22	17	0.02
HIMMELBH	2	5	13	9	0.02	5	13	9	0.02	5	13	9	0.02
HUMPS	2	45	223	202	0.02	45	223	202	0.02	45	223	202	0.02
JENSMP	2	12	47	41	0.02	12	47	41	0.02	12	47	41	0.02
JIMACK	35,449	8316	11,134	8318	1165	8719	17,440	8721	1224	7297	14,596	7299	1027
KOWOSB	4	16	46	32	0.02	16	46	32	0.02	16	46	32	0.02
LIARWHD	5000	16	43	31	0.03	16	45	31	0.03	15	40	28	0.03
LOGHAIRY	2	26	196	179	0.02	26	196	179	0.02	26	196	179	0.02
MANCINO	100	11	23	12	0.08	11	23	12	0.06	11	23	12	0.06
MARATOSB	2	589	2885	2585	0.02	589	2885	2585	0.02	589	2885	2585	0.02
MEXHAT	2	14	59	55	0.02	14	59	55	0.02	14	59	55	0.02
MOREBV	5000	161	168	317	0.36	161	168	317	0.39	161	168	317	0.39
MSQRTALS	1024	2760	5529	2771	8.02	2776	5562	2789	8.8	2776	5562	2789	8.3
MSQRTBLS	1024	2252	4512	2262	7.53	2119	4198	2182	6.6	2179	4366	2189	6.77
NCB20B	500	4994	7577	10,892	80.22	99	216	202	1.59	4912	7503	10,461	79.45
NCB20	5010	908	1965	1477	11.94	216	470	371	3.05	1074	2459	1545	13.16
NONCVXU2	5000	6864	13,196	7400	16.33	6580	12,710	7032	15.19	7477	14,009	8428	17.9
NONDIA	5000	7	25	19	0.02	7	25	19	0.03	7	25	19	0.03
NONDQUAR	5000	616	1372	868	0.78	1423	3001	1663	1.75	2562	5235	2743	2.95
OSBORNEA	5	82	230	174	0.02	82	230	174	0.02	82	230	174	0.02
OSBORNEB	11	57	134	84	0.02	57	134	84	0.02	57	134	84	0.02
OSCIPATH	10	295,029	781,729	5E+05	2.16	3E+05	781,729	534,425	2.23	295,029	8E+05	5E+05	2.23
PALMER1C	8	12	27	28	0.02	12	27	28	0.02	12	27	28	0.02
PALMER1D	7	10	24	23	0.02	10	24	23	0.02	10	24	23	0.02
PALMER2C	8	11	21	22	0.02	11	21	22	0.02	11	21	22	0.02
PALMER3C	8	11	21	21	0.02	11	21	21	0.02	11	21	21	0.02
PALMER4C	8	11	21	21	0.02	11	21	21	0.02	11	21	21	0.02
PALMER5C	6	6	13	7	0.02	6	13	7	0.02	6	13	7	0.02
PALMER6C	8	11	24	24	0.02	11	24	24	0.02	11	24	24	0.02
PALMER7C	8	11	20	20	0.02	11	20	20	0.02	11	20	20	0.02
PALMER8C	8	11	19	19	0.02	11	19	19	0.02	11	19	19	0.02
PARKCH	15	740	1513	1404	35.83	15	59	134	2.08	726	1548	1348	35
PENALTY1	1000	15	61	56	0.02	41	164	144	0.02	43	168	146	0.02
PENALTY2	200	215	263	421	0.03	212	247	404	0.05	200	243	386	0.03
PENALTY3	200	99	330	275	2.06	32	105	86	0.64	83	278	236	1.86
POWELLSG	5000	20	49	34	0.01	34	84	58	0.02	28	72	49	0.03
POWER	10,000	456	933	488	0.81	325	890	637	0.89	544	1119	592	1
QUARTC	5000	15	32	18	0.02	15	32	18	0.03	15	32	18	0.02
ROSENBR	2	28	84	65	0.02	28	84	65	0.02	28	84	65	0.02
S308	2	7	21	17	0.02	7	21	17	0.02	7	21	17	0.02
SCHMVETT	5000	41	71	58	0.25	41	71	58	0.22	37	67	50	0.17
SENSORS	100	46	116	75	0.61	35	97	69	0.55	51	131	88	0.78
SINEVAL	2	46	181	153	0.02	46	181	153	0.02	46	181	153	0.02
SINQUAD	5000	13	44	38	0.08	14	45	39	0.08	14	51	44	0.11
SISSER	2	5	19	19	0.02	5	19	19	0.02	5	19	19	0.02
SNAIL	2	61	251	211	0.02	61	251	211	0.02	61	251	211	0.02
SPARSINE	5000	22,466	22,744	44,664	83.92	21,468	21,760	42,654	83	21,700	22,006	43,104	84.5
SPARSQUR	10,000	37	158	148	0.91	52	205	188	0.84	34	143	136	0.84
SPMSRTLS	4999	216	439	229	0.47	252	501	275	0.61	213	435	224	0.47
SROSENBR	5000	9	23	15	0.02	9	23	15	0.02	9	23	15	0.02
STRATEC	10	170	419	283	6.11	170	419	283	6.2	170	419	283	6.17
TESTQUAD	5000	1543	1550	3081	1.25E+00	1515	3025	1.25	1.52E+00	1573	1580	3141	1.34E+00
TOINTGOR	50	118	214	152	0.02	123	220	163	0.02	120	215	155	0.02
TOINTGSS	5000	4	9	5	0.02	4	9	5	0.02	4	9	5	0.02
TOINTPSP	50	143	336	254	0.02	26	101	90	0.02	140	326	245	0.02
TOINTQOR	50	29	36	53	0.02	29	36	53	0.02	29	36	53	0.02
TQUARTIC	5000	12	44	36	0.03	11	37	29	0.03	11	38	30	0.03
TRIDIA	5000	783	790	1561	0.89	782	789	1559	0.91	783	790	1561	0.89
VARDIM		9	23	18	0.02	10	24	17	0.02	10	23	18	0.02
VAREIGVL	50	24	51	29	0.02	24	51	29	0.02	23	49	28	0.02
VIBRBEAM	8	98	255	174	0.02	98	255	174	0.02	98	255	174	0.02
WATSON	12	53	124	78	0.02	49	127	88	0.02	57	130	78	0.02
WOODS	4000	24	60	40	0.05	23	59	40	0.03	22	57	41	0.03
YFITU	3	68	208	167	0.02	68	208	167	0.02	68	208	167	0.02
ZANGWIL2	2	1	3	2	0.02	1	3	2	0.02	1	3	2	0.02