Smoothing Levenberg–Marquardt method for general nonlinear complementarity problems under local error bound

Smoothing Levenberg–Marquardt method for general nonlinear complementarity problems under local error bound

Applied Mathematical Modelling 35 (2011) 1337–1348 Contents lists available at ScienceDirect Applied Mathematical Modelling journal homepage: www.el...

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Applied Mathematical Modelling 35 (2011) 1337–1348

Contents lists available at ScienceDirect

Applied Mathematical Modelling journal homepage: www.elsevier.com/locate/apm

Smoothing Levenberg–Marquardt method for general nonlinear complementarity problems under local error bound q Haodong Yu ⇑, Dingguo Pu Department of Mathematics, Tongji University, Shanghai 200092, PR China

a r t i c l e

i n f o

a b s t r a c t

Article history: Received 21 July 2009 Received in revised form 30 August 2010 Accepted 8 September 2010 Available online 24 September 2010 Keywords: Nonlinear complementarity problems Smoothing methods Levenberg–Marquardt method Superliner convergence Local error bound

By using the F–B function and smoothing technique to convert the nonlinear complementarity problems to smoothing nonlinear systems, and introducing perturbation parameter lk into the smoothing Newton equation, we present a new smoothing Levenberg– Marquardt method for general nonlinear complementarity problems. For general mapping F, not necessarily a P0 function, the algorithm has global convergence. Each accumulation point of the iterative sequence is at least a stationary point of the problem. Under the local error bound condition, which is much weaker than nonsingularity assumption or the strictly complementarity condition, we get the local superlinear convergence. Under some proper condition, quadratic convergence is also obtained. Ó 2010 Elsevier Inc. All rights reserved.

1. Introduction Consider the following nonlinear complementarity problem (denoted by NCP(F)):

FðxÞ P 0 x P 0 and xT FðxÞ ¼ 0; n

n

ð1Þ

n

where x 2 R , and F: R ? R are continuously differentiable functions. The nonlinear complementarity problem has a large number of important applications and has attracted strong interests in numerical methods for solving this problem. A function /: R2 ? R is called an NCP function if the following property is satisfied:

/ða; bÞ ¼ 0 () a P 0; b P 0 and ab ¼ 0: In this paper, we focus on the well-known Fischer–Burmeister function /: R2 ? R

/ða; bÞ ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 a2 þ b  a  b:

By using the F–B function, the NCP(F) can be equivalently reformulated as the following nonsmooth equations:

0

1 /ðx1 ; F 1 ðxÞÞ B C .. C ¼ 0: UðxÞ :¼ B . @ A /ðxn ; F n ðxÞÞ The merit function of (2) used in this paper is WðxÞ :¼ 12 UðxÞT UðxÞ ¼ 12 kUðxÞk2 . q

Research Supported by National Natural Science Foundation of China Grant No. 10771162.

⇑ Corresponding author.

E-mail address: [email protected] (H. Yu). 0307-904X/$ - see front matter Ó 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.apm.2010.09.012

ð2Þ

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H. Yu, D. Pu / Applied Mathematical Modelling 35 (2011) 1337–1348

Since the standard Newton method cannot be used on this equation directly, smoothing method turns to become an important approach for solving nonsmooth problems, and numerous algorithms of this class have been developed (see [1–8]). However, most current smoothing methods cannot solve general nonlinear complementarity problems. They have to assume the function F to be P0 functions to guarantee the nonsingularity of the Jacobian of the smoothing Newton equation. The assumption of P0 function restricts the use of smoothing methods for general NCP(F). Kanzow and Pieper [1] proposed a smoothing algorithm for general NCP(F). Their method is called Jacobian smoothing method, which solves a mixed Newton equation at each iteration. The key idea of their method is to introduce a gradient step as a substituted search direction when the mixed Newton step fails to find a proper decent direction. This is an simple idea. However, the introduction of the gradient step may reduce the convergence efficiency of the algorithm. On the other hand, the gradient step is for another merit function, i.e. W. Thus, their method has to minimize two different merit function, the convergence analysis turns to be very complicated. There also exists some papers which try to reform the NCP(F) to a constrained problem and use Newton-type to solve it, see [9–12]! for reference. Levenberg–Marquardt method, which is a special class of Newton method, is also an important method for solving nonlinear equation. The main idea of this method is to introduce an perturbation item lk to guarantee the Newton equation to be solvable. Namely, for the following nonlinear equation:

f ðxÞ ¼ 0; n

ð3Þ n

where f: R ? R , the algorithm solves the following system

ðf 0 ðxk ÞT f 0 ðxk Þ þ lk IÞd ¼ f 0 ðxk ÞT f ðxk Þ

ð4Þ

at each iterate point xk. However, to prove the local superlinearly convergence of the algorithm, the parameter lk must be designed carefully. Yamashita and Fukushima [13] and Dan et al. [14] presented a new update rule for lk. Their method sets lk = kf(xk)kd. The advantage of this designation is that they can prove the algorithm to be superlinearly convergent under local error bound condition. This condition is weaker than the nonsingularity assumption, which is essential for most Levenberg–Marquardt method. As to complementarity problems, their method was applied to solve LCP firstly. Then, Yu et al. [15] proposed a smoothing Levenberg–Marquardt method for the extended linear complementarity problems. On the other hand, Zhang and Zhang [16] presented a smoothing L–M method for nonlinear complementarity problems. Their algorithm introduces an smoothing parameter e and views e as an independent variable. Based on the technique proposed by Yamashita and Fukushima [13] and the local error condition, they analyze the local convergence of the algorithm under two special cases, namely, strictly complementarity condition or the nonsingularity assumption. Like most smoothing algorithm, they still have to assume the function F to be P0 function. In this paper, we present a smoothing L–M method for general NCP(F). The method uses the smoothing parameter of which the update rule is similar with that in [8]. The algorithm solve a Levenberg–Marquardt equation at each iteration, and the perturbation parameter is set by lk = kU(xk)kd (0 < d 6 2). The merits of this method are: as a special Newton-type method, the L–M algorithm retains high iteration efficiency; for general nonlinear complementarity problems, all accumulation points of the iterative sequence are stationary points of W; under the assumption of local error bound without the nonsingularity or strict complementarity condition, we get the local superlinear convergence of the algorithm. The organization of this paper is as follows: In the next section, some definitions and preliminary results are given. The smoothing L–M algorithm for general NCP(F) is presented in section 3. The global convergence is also established in this section. The local superlinear convergence under local error bound assumption will be stated in detail in the fourth section. At last, some numerical results are reported. 2. Preliminaries In this paper, we use the Kanzow’s [3] smoothing function to approximate the F–B function:

/e ða; bÞ ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 a2 þ b þ 2e  a  b:

At each iteration, the algorithm deals with the following equation:

0

B Ue ðxÞ :¼ B @

1 /e ðx1 ; F 1 ðxÞÞ C .. C: . A /e ðxn ; F n ðxÞÞ

ð5Þ

Similar with the definition of W(), we define We ðxÞ ¼ 12 Ue ðxÞT Ue ðxÞ ¼ 12 kUe ðxÞk2 . Let mapping G: Rn ? Rn be locally Lipschitzian. Denote G0 (x) be the Jacobian of G at a point x 2 Rn, and rG(x) be the transposed Jacobian of G. In the sense of Clark [17], the generalized Jacobian of G at x can be defined as follows:

  @GðxÞ ¼ conv H 2 Rn  Rn jH ¼ lim G0 ðxk Þ; xk 2 DG ; xk !x

where DG denotes the set of differentiable point of G. To estimate the @G(x), Qi [18] defined the C-subdifferential of G at x:

@ C GðxÞT :¼ @G1 ðxÞ  @G2 ðxÞ     @Gn ðxÞ:

H. Yu, D. Pu / Applied Mathematical Modelling 35 (2011) 1337–1348

1339

Lemma 2.1 ([1]). For all x 2 Rn and all e1, e2 P 0, we have

kUe1 ðxÞ  Ue2 ðxÞk 6 jj where

pffiffiffiffiffi

pffiffiffiffiffi

e1  e2 j;

ð6Þ

pffiffiffiffiffiffi

j :¼ 2n. In particular, for all x 2 Rn and all e P 0, we have pffiffiffi kUe ðxÞ  UðxÞk 6 j e:

ð7Þ

Lemma 2.2 ([1]). Let {xk}, {dk} # Rn and {tk} # R be sequences with xk+1 :¼ xk + tkdk such that {xk} ? x*, {dk} ? d*, and {tk} ; 0 for certain vectors x*, d* 2 Rn. Furthermore, let {ek} # R be a sequence with {ek} ; 0. Then

lim

Wek ðxk þ t k dk Þ  Wek ðxk Þ tk

k!1



¼ rWðx ÞT d :

Lemma 2.3 ([2]). Let x 2 Rn be arbitrary but fixed. Assume that x is not a solution of NCP(F). Let us define the constants

cðxÞ :¼ maxfkxi ei þ F i ðxÞrF i ðxÞkg P 0 iRbðxÞ

and

aðxÞ :¼ maxfx2i þ F i ðxÞ2 g > 0; iRbðxÞ

where b(x) :¼ {ijxi = Fi(x) = 0}. Let d > 0 be given, and define

eðx; dÞ :¼

8 < 1; : aðxÞ2  2

if d2 ; ncðxÞ2 d2 aðxÞ

ncðxÞ2 d2

 aðxÞ 6 0;

otherwise:

Then

distðU0e ðxÞ; @ C UðxÞÞ 6 d for all e such that 0 < e 6 eðx; dÞ. In order to analyze the local superlinear convergence of the algorithm, we need the local error bound assumption. See [19,20] for more details about the error bound theory and its application. Assumption 2.4. Let X be the solution set of (1), there exists a neighbor N(X, r1) = {xjdist(x, X) 6 r1, r1 > 0} and a constant c > 0 such that when x 2 N(X, r1), we have

distðx; XÞ 6 ckUðxÞk:

ð8Þ

Proposition 2.5 ([2]). Assume that {xk} # Rn is a convergent sequence with a limit point x* 2 Rn. Then the following statements hold. (i) The function U is semismooth, which implies that for any Vk 2 @ CU(xk),

kUðxk Þ  Uðx Þ  V k ðxk  x Þk ¼ oðkxk  x kÞ: (ii) If F0 is Lipschitz continuous, then the function U is strongly semismooth, which implies that for any Vk 2 @ CU(xk),

kUðxk Þ  Uðx Þ  V k ðxk  x Þk ¼ Oðkxk  x k2 Þ: It is well known that the function /() and U() are locally Lipschitz continuous. Hence, it is easy to deduce the following result. Proposition 2.6. If the solution set X is bounded, then there exists a closed neighbor N(X, b) = {xjdist(x, X) 6 b, b > 0}, and a constant L1 > 0 such that

kUðx1 Þ  Uðx2 Þk 6 L1 kx1  x2 k;

8x1 ; x2 2 NðX; bÞ:

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3. Algorithm and analysis In the first part of this section, we propose a smoothing L–M method for general NCP(F). In the second part, we show that the algorithm is well defined and prove its global convergence. 3.1. Algorithm We now state the algorithm formally. Algorithm 3.1 Step 0. Initialization:   Given x0 2 Rn,pffiffiffiffiffiffi choose k; g; 2 ð0; 1Þ; a 2 ð0; 0:8Þ; r 2 0; 14 a ; d 2 ð0; 2; c > 0, and the toleration s. Set b0 :¼  a 2 2 0 kUðx Þk; j :¼ 2n; e0 :¼ 2j b0 and k :¼ 0, l :¼ 1. Step 1. If krW(xk)k 6 s, then terminates. Step 2. Compute a solution dk 2 Rn of the linear system

ðrUek ðxk ÞU0ek ðxk Þ þ lk IÞd ¼ rUek ðxk ÞUek ðxk Þ; where lk = kU(xk)kd.  Step 3. Set rk ¼ min r;

1 4

1ð12Þ

ð9Þ

 k d k U ðx Þk . Search the smallest sk in {0, 1, 2, . . .} which satisfies that l

k

k

Wek ðx þ k d Þ 6 Wek ðxk Þ  rk ksk kd k2 : k

sk

sk

k+1

Set tk :¼ k and x Step 4. If

k

ð10Þ

k

:¼ x + tkd .

  1 kUðxkþ1 Þk 6 max gbk ; kUðxkþ1 Þ  Uek ðxkþ1 Þk

a

ð11Þ

then reset l = 1 and set

bkþ1 :¼ kUðxkþ1 Þk and choose ek+1 such that

0 < ekþ1 6 min





a 2 2 3 b ; ek ; eðxkþ1 ; cbkþ1 Þ : 4 2j kþ1

ð12Þ

If the condition is not satisfied, find ek+1 such that

0 < ekþ1 6 min



2 3 a kUðxkþ1 Þk2 ; ek 4 2j

 ð13Þ

and set bk+1 :¼ bk, l :¼ l + 1. Step 5. Set k :¼ k + 1, and return to step 1. Remark 1 (1) To guarantee the smoothing parameter ek to go to 0, we reduce it at each iteration by at least a factor of 3/4. (2) The Armijo parameter rk of the line search in step 3 seems complicated. It plays important rules in the proof of global convergence. Without loss of generality, we assume implicitly that the iteration does not terminate in finite steps. In other words, the smoothing parameter e will converge to zero, and for all k P 1, we have lk > 0. For convenience of the analysis, we denote the index set

K :¼ f0g

[

k 2 NjkUðxk Þk 6 max gbk1 ; a1 kUðxk Þ  Uek1 ðxk Þk :

ð14Þ

Firstly we have to prove the algorithm is well defined. Hence we need the following results: Lemma 3.1. The following two statements holds: (a) We have

kUðxk Þ  Uek ðxk Þk 6 akUðxk Þk2 for all k P 0.

ð15Þ

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H. Yu, D. Pu / Applied Mathematical Modelling 35 (2011) 1337–1348

(b) We have

dist F ðU0ek ðxk Þ; @ C Uðxk ÞÞ 6 ckUðxk Þk

ð16Þ

for all k 2 K with k P 1. Proof. The first statement of this Lemma is obtained immediately from Lemma 2.1, (12) and (13). The second statement is directly cited from [1]. h Proposition 3.2. Algorithm A is well defined. Proof. Since the function F is continuously differentiable, and rWe ðxÞ ¼ ðU0e ðxÞÞT Ue ðxÞ, we obtain k

k

k

Wek ðxk þ kd Þ  Wek ðxk Þ ¼ krWek ðxk ÞT d þ oðkÞ ¼ kUek ðxk ÞT U0ek ðxk Þd þ oðkÞ: It is easy to deduce from the expression of rk that k

rk 6 12 lk . Thus, we can get

k

k

k

k

Wek ðxk þ kd Þ  Wek ðxk Þ ¼ kðd ÞT ðrUek ðxk ÞU0ek ðxk Þ þ lk IÞd þ oðkÞ 6 klk kd k2 þ oðkÞ 6 2krk kd k2 þ oðkÞ: Notice that the rk is always positive, the line search condition will be satisfied for k small enough. Thus, the algorithm is well defined. h 3.2. Global convergence In this subsection, we prove the smoothing Levenberg–Marquardt algorithm is globally convergent. We will show that every accumulation point of {xk} is a stationary point of NCP(F). We begin with the following obvious result. Lemma 3.3. The following statement holds for all k 2 N

kUek ðxkþ1 Þk < kUek ðxk Þk: Lemma 3.4 ([1]). Let {xk} # Rn be a sequence generated by Algorithm 3.1 . Assume that {xk} has an accumulation point x*, which is a solution of NCP(F). Then the index set K is infinite and {ek} ? 0. Lemma 3.5. The following statement holds for all l 2 N

pffiffiffiffi pffiffiffiffiffiffiffiffi kUelþ1 ðxlþ1 Þk þ j elþ1 6 kUel ðxl Þk þ j el : Proof. By Lemmas 2.1 and 3.3,

pffiffiffiffi pffiffiffiffiffiffiffiffi kUelþ1 ðxlþ1 Þk 6 kUelþ1 ðxlþ1 Þ  Uel ðxlþ1 Þk þ kUel ðxlþ1 Þk 6 j el  elþ1 þ kUel ðxl Þk: This leads to

pffiffiffiffi pffiffiffiffiffiffiffiffi kUelþ1 ðxlþ1 Þk þ j elþ1 6 kUel ðxl Þk þ j el

ð17Þ

which is just we want to prove. h From this lemma, and using the same technique as [1], we can prove the next important propositions. Note that, since the update rules of ek of our algorithm is different from that in [1], the level set L0 is also slightly different. Proposition 3.6. The sequence {xk} generated by Algorithm 3.1 remains in the level set

L0 :¼ fx 2 Rn jWðxÞ 6 ð1 þ b0 aÞ2 Wðx0 Þg:

ð18Þ

The index set K is critical in the analysis of the algorithm, because we have the following result. Proposition 3.7. Let {xk} be the sequence generated by Algorithm 3.1 and have at least one accumulation point, then the index set K is infinite if and only if each accumulation point of {xk} is a solution of NCP(F). Proof. If K is infinite, by Proposition 5.6 in [1], any accumulation point x* is a solution of NCP(F). Conversely, if one accumulation point is a solution of NCP(F), then Proposition Lemma 5.1 in [1] shows that K is infinite. h The proof of this proposition immediately shows the following two results:

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Lemma 3.8. If any one accumulation point of the iteration sequence {xk} is a solution of NCP(F), then each accumulation point of {xk} is a solution of NCP(F). Lemma 3.9. If K is finite, then none of the accumulation points of {xk} is a solution of NCP(F). Now we prove the main result of this section. Theorem 3.10. Let {xk} be a sequence generated by Algorithm 3.1 and let {xk}L be a subsequence converging to an accumulation point x* 2 Rn, then x* is a stationary point of the function W. Proof. If K is infinite, by Proposition 3.7, x* is a solution of NCP(F), which is surely a stationary point of W. ~ be the largest element in K. Hence we assume the set K is finite. With out loss of generality, we assume K \ L = ;. Let k ~ From Step 4 of Algorithm 3.1, we get that for all k > k, ~

bk ¼ bk~ ¼ kUðxk Þk;

ð19Þ

~

kUðxk Þk > gkUðxk Þk > 0:

ð20Þ

~ This means, for all k > k, ~

Wðxk Þ > g2 Wðxk Þ > 0:

ð21Þ

Assume by contradiction that rW(x*) – 0. We first show that lim infk2Ltk = 0. If limk2Linf tk = t* > 0, the line search rules (10) in step 3 shows that k

Wek ðxkþ1 Þ  Wek ðxk Þ 6 rk t k kd k2

ð22Þ

for all k 2 L. It follows from the L–M Eq. (9) that k

d ¼ ½rUek ðxk ÞU0ek ðxk Þ þ lk I1 rUek ðxk ÞUek ðxk Þ:

ð23Þ

Thus, we obtain k

kd k2 ¼ rWek ðxk ÞT ½rUek ðxk ÞU0ek ðxk Þ þ lk I2 rWek ðxk Þ: 0

ð24Þ k

Denote the singular value decomposition (SVD) of Uek ðx Þ by

2

3

rk;1

6 6 U0ek ðxk Þ ¼ U k Rk V Tk ¼ U k 6 6 4

7 7 T 7V ; 7 k 5

rk;2 ..

.

ð25Þ

rk;n nn

where Uk, Vk 2 R

are orthogonal matrices and rk,1 P rk,2 P    P rk,n P 0. Hence, (23) and (24) can be rewritten as

k

d ¼ V k Kk V Tk rWek ðxk Þ

ð26Þ

and k

kd k2 ¼ rWek ðxk ÞT V k K2k V Tk rWek ðxk Þ;

ð27Þ

where

2 6 6 6 6 Kk ¼ 6 6 6 4

3

1

r2k;1 þlk 1

r2k;2 þlk

..

. 1

7 7 7 7 7: 7 7 5

ð28Þ

r2k;n þlk

Since {x}L ? x* and {ek} ? 0, there exists some m > 0 such that for all k 2 L and all i = 1, . . . , n,

jr2k;i j < m: Notice that rW(x*) – 0, we have {lk}L ? kU(x*)kd > 0. Hence, there exists a scalar M > 0 that for all k 2 L sufficiently large, k

kd k2 P

1 ðm þ lk Þ2

krWek ðxk Þk2 P MkrWek ðxk Þk2 :

ð29Þ

H. Yu, D. Pu / Applied Mathematical Modelling 35 (2011) 1337–1348

1343

So, we obtain from the line search rule in step 3 that

Wek ðxkþ1 Þ  Wek ðxk Þ 6 rk t k MkrWek ðxk Þk2

ð30Þ

for all k 2 L large enough. On the other hand, since K is finite, we have

frk gL ! r ¼ min





1 4

r; kUðx Þkd > 0:

ð31Þ

Let c :¼ r*t*MkrW(x*)k2 > 0, and remember that {ek} ? 0, we obtain

Wðxkþ1 Þ  Wðxk Þ 6 

c 2

ð32Þ

for all k 2 L sufficiently large. Let L = {l0, l1, l2, . . .}. Then, it is easy to deduce from Lemmas 2.1 and 3.5 that

pffiffiffiffiffiffiffiffiffi kUðxljþ1 Þk 6 kUðxlj þ1 Þk þ 2j elj þ1 : This implies that for all lj sufficiently large

1 2

c 4

Wðxljþ1 Þ ¼ kUðxljþ1 Þk2 6 Wðxlj þ1 Þ þ :

ð33Þ

Together with (32) and (33), we have

Wðxljþ1 Þ  Wðxlj Þ ¼ ðWðxljþ1 Þ  Wðxlj þ1 ÞÞ þ ðWðxlj þ1 Þ  Wðxlj ÞÞ 6

c  c c þ  6 4 2 4

for all lj sufficiently large. This contradicts the nonnegativity of W(x). Hence, we obtain lim infk2Ltk = 0. Subsequencing if necessary, we assume limk2Ltk = 0. Hence, we have k

Wek ðxk þ ksk 1 d Þ  Wek ðxk Þ ksk 1

k

> rkd k2

ð34Þ

for all k 2 L large enough. This implies

rWðx ÞT d P r kd k2 ;

ð35Þ

k

where d* is the limit of {d }L and r* is the limit of {rk}L. d 1 1 1 * Without loss of generality, suppose fV k gL ! V  ; fKk gL ! K ¼ diagðr2 þ l ; r22 þl ; . . . ; r2n þl Þ, where l = kU(x*)k > 0. So, the 1 inequality (35) is equivalent with

rWðx ÞT V  K V T rWðx Þ P r rWðx ÞT V  K2 V T rWðx Þ: Recall that from (31),

ð36Þ

r 6 l . Hence, (36) implies that 1 4

V T rWðx Þ ¼ 0

ð37Þ

which implies that rW(x*) = 0. This completes our proof. h 4. Local convergence under local error bound In this section, we study the local superlinear convergence of the algorithm. Firstly, we need the following assumptions. Assumption 4.1. Assume that the sequence {xk} generated by Algorithm 3.1 has at least one accumulation point x* which is a solution of the NCP(F). Assumption 4.2. Assume that the solution set X is bounded and F0 is locally Lipschitz continuous on a neighbor N(X, r2) = {xjdist(x, X) 6 r2, r2 > 0}. From this assumption, we get the strongly semismooth of the function U(x) on a neighbor of X. Thus, by Proposition 2.5, we have the next lemma. Lemma 4.3. For any x1, x2 2 N(X, r2), there exists L2 > 0 such that for any V 2 @ CU(x1)

kUðx1 Þ  Uðx2 Þ  Vðx1  x2 Þk 6 L2 kx1  x2 k2 : For each point xk, denote  xk be a point such that

kxk  xk k ¼ distðxk ; XÞ;

xk 2 X:

To study the superlinear convergence of the algorithm, we firstly establish the following lemma.

ð38Þ

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Lemma 4.4. Denote r = min{r1, r2, b}, where r1, r2, b are defined in Assumption 2.4, Lemma 4.3 and Proposition 2.6, respectively. If k 2 K, and xk 2 N(X, r), there exists m1, m2 > 0 such that k

kd k2 6 m1 distðxk ; XÞ2 ;

ð39Þ

k

kUek ðxk Þ þ U0ek ðxk Þd k2 6 m2 distðxk ; XÞ2þd :

ð40Þ

Proof. Since dk satisfies the L–M equation (9), dk must be the solution of the following problem:

min pk ðdÞ ¼ n d2R

1 1 kUek ðxk Þ þ U0ek ðxk Þdk2 þ lk kdk2 : 2 2

ð41Þ

Then, we obtain that for any Vk 2 @ CU(xk) k

kd k2 6 6

2

lk

pk ðxk  xk Þ 6

1 h

lk

1

lk

kUek ðxk Þ þ U0ek ðxk Þðxk  xk Þk2 þ kxk  xk k2

kUðxk Þ  Uðxk Þ  V k ðxk  xk Þk þ kUek ðxk Þ  Uðxk Þk þ kU0ek ðxk Þ  V k k  kxk  xk k

i2

þ kxk  xk k2 :

Since k xk  xk k ¼ distðxk ; XÞ 6 r, from Lemma 4.3, we have

kUðxk Þ  Uðxk Þ  V k ðxk  xk Þk 6 L2 kxk  xk k2 :

ð42Þ

From Lemma 2.1, together with Step 4 of the algorithm and Proposition 2.6, we get

pffiffiffiffiffi a a kUek ðxk Þ  Uðxk Þk 6 j ek 6 kUðxk Þk2 6 L21 kxk  xk k2 : 2 2

ð43Þ

Also, from Lemma 3.1(b), there exists some V k 2 @ C Uðxk Þ such that

kU0ek ðxk Þ  V k k  kxk  xk k 6 ckUðxk Þk  kxk  xk k 6 cL1 kxk  xk k2 :

ð44Þ

Notice from the local error bound Assumption 2.4 that

lk ¼ kUðxk Þkd ¼ kUðxk  xk Þkd P

1 k kx  xk kd : cd

ð45Þ

On the other hand, by Proposition 2.6

lk ¼ kUðxk Þ  Uðxk Þkd 6 Ld1 kxk  xk kd :

ð46Þ

Hence, if we set

h a i2 m1 ¼ r 2d cd cL1 þ L2 þ L21 þ 1: 2

ð47Þ

By (42)–(45) we get k

kd k2 6 m1 kxk  xk k2

ð48Þ

which prove the first statement of the lemma. Similarly, (41) shows that k k kUek ðxk Þ þ U0ek ðxk Þd k2 6 2pk ðd Þ 6 kUek ðxk Þ þ U0ek ðxk Þðxk  xk Þk2 þ lk kxk  xk k2 h h a i2 a i2 6 cL1 þ L2 þ L21 kxk  xk k4 þ lk kxk  xk k2 6 cL1 þ L2 þ L21 kxk  xk k4 þ Ld1 kxk  xk k2þd 2 2

h i2 So, if we set m2 ¼ r2d cL1 þ L2 þ a2 L21 þ Ld1 , then the second statement of the lemma is proved. h Lemma 4.5. If xk, xk + dk 2 N(X, r) and k 2 K, then there exists m3 > 0 such that k

d

distðxk þ d ; XÞ 6 m3 distðxk ; XÞ1þ2 : Proof. Since xk, xk + dk 2 N(X, r), by Assumption 2.4 and Lemma 4.3, we obtain that for any Vk 2 @ CU(xk)

1 k k k k distðxk þ d ; XÞ 6 kUðxk þ d Þk 6 kUðxk Þ þ V k d k þ L2 kd k2 c pffiffiffiffiffi k k k 6 kUek ðxk Þ þ U0ek ðxk Þd k þ j ek þ kU0ek ðxk Þ  V k k  kd k þ L2 kd k2 :

ð49Þ

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H. Yu, D. Pu / Applied Mathematical Modelling 35 (2011) 1337–1348

By Lemma 3.1 and Proposition 2.6, there exists an Vk 2 @ CU(xk) such that k k k kU0ek ðxk Þ  V k k  kd k 6 ckUðxk Þk  kd k 6 cL1 kxk  xk k  kd k:

Thus, according to Lemma 4.4 and the update rules of ek, we can deduce from the previous inequality that 1 d 1 a k k k distðxk þ d ; XÞ 6 m22 distðxk ; XÞ1þ2 þ L21 kxk  xk k2 þ cL1 kxk  xk k  kd k þ L2 kd k2 c 2  1 1 d a  6 m22 distðxk ; XÞ1þ2 þ cL1 m21 þ L2 m1 þ L21 distðxk ; XÞ2 : 2

h 1 i 1 d Let m3 ¼ c m22 þ ðcL1 m21 þ L2 m1 þ a2 L21 Þr12 , then we have shown that m3 satisfies d

k

distðxk þ d ; XÞ 6 m3 distðxk ; XÞ1þ2

ð50Þ

which is just we want to prove. h Furthermore, we have to discuss that in what situation, the condition xk, xk + dk 2 N(X, r) in this lemma will be satisfied. Lemma 4.6. Suppose K0 is the index set defined as K0 = {kjxk 2 N(X, r), k 2 K}, then for k 2 K0 sufficiently large, xk + dk 2 N(X, r). Proof. Since xk 2 N(X, r), by Lemma 4.4, we have 1

k

1

distðxk þ d ; XÞ 6 ð1 þ m21 Þdistðxk ; XÞ 6 ð1 þ m21 ÞckUðxk Þk: Notice that k 2 K, which implies that {kU(xk)k}K ? 0, hence for k 2 K0 large enough, dist(xk + dk, X) 6 r. h Lemma 4.7. For the subsequence {xk}, k 2 K0, we have k

kUðxk þ d Þk ¼ oðkUðxk ÞkÞ:

ð51Þ

Proof. Denote ~ xk be a point such that k k k~xk  ðxk þ d Þk ¼ distðxk þ d ; XÞ;

~xk 2 X:

Since for k 2 K0 large enough, xk, xk + dk 2 N(X, r). According to Proposition 2.6, we have k

k

k

k

d

kUðxk þ d Þk ¼ kUðxk þ d Þ  Uð~xk Þk 6 L1 k~xk  ðxk þ d Þk ¼ L1 distðxk þ d ; XÞ 6 L1 m3 distðxk ; XÞ1þ2 d

6 L1 m3 c1þ2 kUðxk Þk1þ2 : d

Hence k

kUðxk þ d Þk ¼ oðkUðxk ÞkÞ:



In order to establish the superlinear convergence of the algorithm, we need to prove the following lemma, which is slightly different with Lemma 3.2 in [8]. Lemma 4.8. Let k be a constant which satisfies

"

1 ð1  a  4rÞ2 1  k2 ; 2 2 2ð2 þ aÞ2

#

and

WðyÞ  Wðxk Þ 6 2kWðxk Þ n

ð52Þ k

for some k 2 K and y 2 R . If kU(x )k < 1, then

Wek ðyÞ  Wek ðxk Þ 6 2rWek ðxk Þ: Proof. Since k 2 K and kU(xk)k < 1, it follows from (7) that

pffiffiffiffiffi

ek 6

a 2j

which implies

kUðxk Þk2 <

a 2j

kUðxk Þk

ð53Þ

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H. Yu, D. Pu / Applied Mathematical Modelling 35 (2011) 1337–1348

pffiffiffiffiffi a kUek ðxk Þk P kUðxk Þk  j ek P ð1  ÞkUðxk Þk 2 and

pffiffiffiffiffi a kUek ðyÞk 6 kUðyÞk þ j ek 6 kUðyÞk þ kUðxk Þk 2 for all y 2 Rn, k 2 K. Therefore, by (52)

2 1  pffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 a a2 kUðxk Þk2 6 WðyÞ þ a 1  2kWðxk Þ  ð1  aÞWðxk Þ kUðyÞk þ kUðxk Þk  1 2 2 2 2  pffiffiffiffiffiffiffiffiffiffiffiffiffiffi 6 ð2k  a 1 þ 1  2k ÞWðxk Þ:

Wek ðyÞ  Wek ðxk Þ 6

ð54Þ

Denote



pffiffiffiffiffiffiffiffiffiffiffiffiffiffi

uðkÞ ¼ 2k  a 1 þ 1  2k : a4rÞ2 Set ~ k ¼ 12  ð1 , then 2ð2þaÞ2

uð~kÞ ¼ 1 

ð1  a  4rÞ2 ð2 þ aÞ2

1  a  4r 2ð1  a  4rÞ 1  a  4r P1 ¼ 4r : a 1þ a 1þ 2þa 2þa 2þa

Notice that the function u is monotonically increasing in ½0; 12 and that a 2 (0, 0.8), we get

a

Wek ðyÞ  Wek ðxk Þ 6 4rWðxk Þ 6 2rð1 þ Þ2 Wðxk Þ 6 2rWek ðxk Þ:

ð55Þ

2

This complete the proof. h Finally, we establish the main result of this section. Theorem 4.9. Let {xk} be a sequence generated by Algorithm 3.1 and suppose that Assumptions 2.4, 4.1 and 4.2 hold, then {dist(xk, X)} converges to 0 superlinearly. Furthermore, if d = 2, then the convergence rate is quadratical.

Proof. It follows from Assumption 4.1 that the index set K0 is an infinite set. From Lemmas 4.5 and 4.6, we have that k

distðxk þ d ; XÞ ¼ oðdistðxk ; XÞÞ; Let k ¼ max

n

1 2

 k

k

ð1a4rÞ2 2ð2þaÞ2

2 ; 12g

o

k 2 K 0 ; k ! 1:

ð56Þ

, we obtain from Lemma 4.7 that for any k 2 K0 large enough

k

Wðx þ d Þ  Wðx Þ 6 2kWðxk Þ k

ð57Þ k

and kU(x )k < 1. Thus, Lemma 4.8 implies that the stepsize t = 1, i.e. x easy to deduce from (57) that

k+1

k

k

= x + d . Furthermore, from the definition of k, it is

kUðxkþ1 Þk 6 gkUðxk Þk ¼ gbk : Hence, we get k + 1 2 K. Finally, by Lemma 4.6, k + 1 2 K0. We have proven that for any k 2 N large enough, k 2 K0. Therefore, by (56), we establish the superlinear convergence of the algorithm. If d = 2, the quadratic convergence follows immediately from Lemma 4.5. This completes the whole proof. h

5. Numerical experiments In this part, we give several examples to illustrate the efficiency of the proposed method. In the following examples, we set the toleration be s = 1.0e6 and g = 0.8, a = 0.7, k = 0.5, c = 10, d = 2, r = 0.15. Problem 1. Let F(x) = Mx + q, where

2

4 1 0 6 2 4 1 6 6 6 0 2 4 M¼6 6    6 6 4 0 0 0 0 0 0 and q = (1, 1, . . . ,  1)T.



0

3

0 7 7 7  0 7 7  7 7 7  1 5  4 

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H. Yu, D. Pu / Applied Mathematical Modelling 35 (2011) 1337–1348

Problem 2. Let F(x) = Mx + q, where

2

1 2 2  2

3

1 2  27 7 7 1  27 7 .. .7 . .. 5

6 6 6 M¼6 6 6 4

1 T

and q = (1, 1, . . . , 1) . Problem 3. Let F(x) = (f1(x), f2(x), f3(x), f4(x))T, where

f1 ðxÞ ¼ 3x21 þ 2x1 x2 þ 2x22 þ x3 þ 3x4  6; f2 ðxÞ ¼ 2x21 þ x1 þ x22 þ 10x3 þ 2x4  2; f3 ðxÞ ¼ 3x21 þ x1 x2 þ 2x22 þ 2x3 þ 9x4  9; f4 ðxÞ ¼ x21 þ 3x22 þ 2x3 þ 3x4  3: This example is Kojima–Shindo problem which comes from [12]. It has two solutions: x1 ¼ The former is a degenerate solution and the latter is a nondegenerate solution.

pffiffi

T

6 ; 0; 0; 0:5 2

and x2 = (1, 0, 3, 0)T.

Problem 4. Let F(x) = (f1(x), f2(x), f3(x))T, where

f1 ðxÞ ¼ x1  2; f2 ðxÞ ¼ x2  x3 þ x32 þ 3; f3 ðxÞ ¼ x2 þ x3 þ 2x33  3: This problem has the solution (2, 0, 1)T. Problem 5. This problem is constructed in the way proposed by Gomes–Ruggiero [21]. Let f(x): Rn ? Rn be continuously differentiable functions. Denote f(x) = (f1(x), f2(x), . . . , fn(x))T. Let x* = (0, 1, 0, 1, . . .)T 2 Rn. Set

F i ðxÞ ¼



fi ðxÞ  fi ðx Þ þ 1; if i is odd; fi ðxÞ  fi ðx Þ;

Otherwise:

for i = 1, . . . , n. Obviously, x* is a nondegenerate solution of NCP(F). In this example, function f is defined as follows, which is Problem 3 in [22]

8 n P > > > < fi ðxÞ ¼ ðn þ 1Þ þ xi þ xj ;

i ¼ 1; . . . ; n  1;

i¼1

n Q > > > : fn ðxÞ ¼ 1 þ xj : j¼1

The results of this example are listed in Table 1. Dim denotes the dimension of the variables in the problem, Iter denotes the number of iterations, NF means the number of evaluations of function F. Table 1 Numerical results for Algorithm 3.1. Problem

Dim

Start point

Iter

NF

W(x)

krW(x)k

Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro

4 4 8 4 8 4 4 4 4 3 3 3 10 20 100

(1, 1, 1, 1) (1, 2, 3, 4) (0, 0, . . . , 0) (0, 0, 0, 0) (1, 1, . . . , 1) (5, 5, 5, 5) (1, 1, 1, 2) (2, 2, 2, 2) (1, 2, 3, 4) (1, 1, 1) (1, 1, 1) (1, 2, 3) (1, 1, . . . , 1) (1, 1, . . . , 1) (0, 0, . . . , 0)

6 11 5 6 9 19 7 9 11 6 8 9 10 13 16

6 11 5 6 9 19 7 9 11 6 8 9 11 14 18

6.2e17 1.1e26 7.8e15 4.0e16 4.0e14 1.4e14 3.4e15 3.2e16 1.1e26 1.2e15 6.8e18 1.7e21 4.9e13 1.6e13 3.1e19

3.2e08 4.3e13 2.2e09 1.9e08 2.0e07 3.6e07 2.3e07 7.2e08 4.3e13 3.8e08 2.6e09 5.0e11 7.2e07 4.2e07 4.0e10

1 1 1 2 2 2 3 3 3 4 4 4 5 5 5

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H. Yu, D. Pu / Applied Mathematical Modelling 35 (2011) 1337–1348

6. Conclusions In this paper, we present a new smoothing L–M method for the solution of the general nonlinear complementarity problems. The smoothing parameter ek borrowed from Chen et al. [8] and the perturbation parameter lk are crucial for the algorithm to get desirable convergence properties. For general NCP(F), each accumulation point of the algorithm is a stationary point of the problem. As to local convergence, under the local error bound assumption, which is a quite mild condition, the algorithm has local superlinear convergence. Numerical experiments shows the algorithm is efficient. We believe that it is not very difficult to extend this method for mixed complementarity problems or variational inequality problems. On the other hand, it would also be interesting to see how to introduce the perturbation item lk into the Jacobian smoothing method. We will study these problems in further study. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22]

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