老婆我爱你

老婆我爱你(篇一)
《老婆我爱你》

老婆我爱你，我真的是爱你的，可有时候我却是不知道该怎么和你说，我能容忍你的小任性！

我只想好好的和你走下去，生活中难免有争吵有分歧，这个我都有心理准备的，因为我们的 性格不同，生活习惯都不同 ，我们走到一起真的是有着莫大的机缘和 幸运，所以我很珍惜你！更珍惜我们的爱情！我很爱你！真的 不 知道我怎么会如此的爱你？但是爱情笨来 就很是奇怪，所以我也不会去把它搞清楚，也不想去。。。。。。只想好好的爱你!疼你！让你不受委屈，我也尽力不去惹你生气！你就好好的做我 的老婆吧！我会很努力的去爱你，为了我们的爱情我会很努力的工作的！我要风风光光的去娶你！喜欢你的笑，你生气的样子，说着不理我的话，就是不知道你是不是故意的？老婆，你说，我们会爱到白头么？我 想我们会的，因为我们经历了那么多的考验和折磨，所以我很坚定的 相信我们会爱到白头的，最后我们会很幸福的看着彼此的 眼睛，笑到最后的！老婆，我爱你！我们结婚吧！

二〇一一年十一月五日星期

六

老婆我爱你(篇二)
《老婆我爱你》

老婆我爱你(篇三)
《老婆我爱你》

老婆我爱你(篇四)
《老婆 我爱你》

老婆 我爱你

老婆情人节快乐，老婆我们节日快乐。

老婆，到这一刻我们恋爱整整三十八个

星期了。也就

是266天。也

就是6389个

小时。也就是

383340分钟。

也就是23000

400秒。和你在

一起的每一秒我

都好开心，和你在一起的每一个画面都时

常出现在我的脑子里，每次回想自己都感

到自己好幸福， 好幸福，好幸福

老婆，你还记得我们刚 开始认识的时候每天聊天

聊到一两点的快乐么？你说小猪我说恩。我说糖葫芦，你说哎的

时光老婆，你还记得我们第一次见面那时美好的开始么？老婆，

你还记得我们第一次独处一直到第二天才会宿舍还被阿姨拦

住记名我们都写自写的幸福么？老婆，你还记得我们那

次打电话一打就个半小时的奇迹么？老婆，你还

记得你第一次表白的场景么？老婆，你还

记得我答应你的瞬间我们的快乐么？

老婆，你还记得我们第一约会么？

你还记得我们第一次接吻么？

你还记得我们第一

次出去玩么？

记得玄

武湖你吃相？记得你夫子庙我们吃臭豆腐么？记得我们、、、、、、

老婆，还记得我帮你过生日我们一起快乐？还记得生日我给你的

最重要的礼物么？一份最重要的礼物——我这个猪头。老婆，

还记得平安夜你送我苹果，我送你的“特制糖葫芦”么？还有

我放在你书包里的情书么？还有我们一起放孔明灯的那个夜晚

么？老婆，还记得放假你送我离开时我们的眼泪么？还记得

放假每天想念彼此的情形么？老婆，还记得我们过得第一

个情人节么。你成为了我的妻子。老婆，还记得我们七

个月纪念日，我送给你的三封情书？老婆，还记得我

将戒指套进你的手指的瞬间么？老婆，还记得我们

一起过那段吃棒棒糖大布丁的时光么？虽然苦但

是真的好幸福。。。 老婆，当 然还有暑假中

我们在芜湖，我们在方特的快乐的时光了。

还有你来巢湖就知道吃哈你懂得啊

老婆，那段把你弄哭的录音礼物。。。那只是礼物的一部分，

那天把录音送给你，今天我将原稿送给你，老婆这就是我送给你

的情人节的礼物。。。老婆节日快乐

99种我爱你

老婆又一年的亲人节来了，回想我们起过去的我们的整

整三十八个星期的恋爱过程，我感觉我自己好幸福，好幸福。

能有你是我这辈子最大的幸福。过去的266天里，我们一起

逛玄武湖，一起放风筝，一起钓鱼虽然一条也没钓到，一起

早起跑操，一起逛学校虽然常常是我骑车带你这个懒猪，一

起放孔明灯，一起吃一起笑一起疯狂一起度过美好的时

光、、、、、、太多的美好的回忆，太多的幸福，太多的快乐，

在这时我不知道送什么给你，和你说些什么好，想了好久最

终决定用99种我爱你，表达我对你的爱。。。愿我们天长地久，

白头偕老

1 英语：I love you

2 犹太语 --> Ani ohev otach (啊你 偶

和夫 偶踏西)

3 法语：je t'aime (也带嘛)

4 德语：ich liebe dich (衣西里拔弟兮)

5 希腊语：σε αγαπ? se agapo (萨哈泼)

6 匈牙利语：szertlek (赛来特可来)

7 爱尔兰语：taim i'ngra leat (踢蚂蚁恩rua丽t)

8 爱沙尼亚语：mina armadtansind (米那 阿马斯叹赛)

9 芬兰语：mina rakastan sinua (明那 饿拉卡司谈 洗奴娃)

10 比利时弗拉芒语：ik zie (一客 也有狼鸡)

11 意大利语：Ti Amo (提阿么)

12 蒙古语 --> bi chamd hairtal (比掐

木弹还日抬)

13 拉脱维亚语：estevi milu(一司特喂 米卢)

14 荷兰语：ik hou van jou (阿荣吼范丸)

15 丹麦语：jeg elsker dig (接个 爱死 替个)

16 葡萄牙语：eu amo-te (哎呜啊木腿)

17 波斯语 --> Tora dost daram (土司

特大轮)

18 北印度语 --> main tumse pyar karta hoon (慢色爹革了地后)

19 马其顿语：te sakam (特飒侃)

20乌兹别克族：Sizni Sewaman

21 孟加拉语：ami to may halobashi阿米 托买 科 波哈罗巴

适

22 阿拉伯语 --> Ana Ahebak (女生对

男生：不黑不开)

23 罗马尼亚语：te tu be besc (有背4克)

24 捷克语：milujite (米卢急特)

25 马耳他语：inhobbok (音红博客)

老婆我爱你(篇五)
《老婆我爱你》

牵 手 的 那 一 刻 ， 心 便 跳 在 同 一 节 奏倚 在 一 起 ， 看 日 出 日 落像两片云，融在一起走 直到永久………在这深情的季节里，我好想送你一束盛开的玫 瑰和数不尽的祝福！但愿这玫瑰的清香能淡淡地散 发出对你的柔柔关怀和思念的气息，让生活的点点 滴滴伴随我们一起走过人生百年。情人节快乐！

老婆我爱你(篇六)
《老婆我爱你》

老婆我爱你！论文很重要可是心情更重要，要控制住自己的情绪，要对自己有自信，老公会一直爱你支持你！嫁给我宝贝！

AbstractOptimization algorithms based on convex sepa-rable approximations for optimal structural design often use

reciprocal-like approximations in a dual setting; CONLIN

and the method of moving asymptotes (MMA) are well-known examples of such sequential convex programming

(SCP) algorithms. We have previously demonstrated that

replacement of these nonlinear (reciprocal) approximations

by their own second order Taylor series expansion provides

a powerful new algorithmic option within the SCP class of

algorithms. This note shows that the quadratic treatment

of the original nonlinear approximations also enables the

restatement of the SCP as a series of Lagrange-Newton

QP subproblems. This results in a diagonal trust-region

SQP type of algorithm, in which the second order diag-onal terms are estimated from the nonlinear (reciprocal)

intervening variables, rather than from historic information

using an exact or a quasi-Newton Hessian approach. The QP

Loosely based on the paper ‘On diagonal QP subproblems for

sequential approximate optimization’, presented at the 8-th World

Congress on Structural and Multidisciplinary Optimization, 1–5 June,

2009, Lisbon, Portugal, paper 1065.

L. F. P. Etman (B) · J. E. Rooda

Department of Mechanical Engineering, Eindhoven University

of Technology, Eindhoven, The Netherlands

e-mail: l.f.p.etman@tue.nl

J. E. Rooda

e-mail: j.e.rooda@tue.nl

A. A. Groenwold

Department of Mechanical Engineering, University of Stellenbosch,

Stellenbosch, South Africa

e-mail: albertg@sun.ac.za

formulation seems particularly attractive for problems with

far more constraints than variables (when pure dual meth-ods are at a disadvantage), or when both the number of

design variables and the number of (active) constraints is

very large.

KeywordsDiagonal quadratic approximation·

Sequential approximate optimization (SAO)·

Sequential quadratic programming (SQP)·

Sequential convex programming (SCP)·

Reciprocal intervening variables·Trust region method

1 Introduction

Gradient-based optimization algorithms that rely on non-linear but convex separable approximation functions have

proven to be very effective for large-scale structural opti-mization. Well-known examples are the convex lineariza-tion (CONLIN) algorithm (Fleury and Braibant 1986)

and it’s generalization, the method of moving asymptotes

(MMA) (Svanberg 1987, 2002). These algorithms—and

some related variants, e.g. see Borrval and Petersson (2001),

Bruyneel et al. (2002) and Zillober et al. (2004)—are also

known as sequential convex programming (SCP) methods

(Fleury1993; Zillober et al.2004; Duysinx et al.2009).

The aforementioned SCP algorithms are all based on

reciprocal or reciprocal-like approximations. They generate

a series of convex separable nonlinear programming (NLP)

subproblems. The derivation of the reciprocal-like approx-imations typically starts from the substitution of recipro-cal intervening variables into a first-order (linear) Taylor

series expansion, which is subsequently convexified, and

第二页

possibly enhanced using historic information. The resulting

approximations provide for reasonably accurate subprob-lem approximations, while separability and convexity of

the objective and constraint function approximations allows

for the development of efficient solution approaches for

the approximate subproblems. In many of the cited refer-ences, the dual formulation of Falk is used (Falk 1967;

Fleury1979), but other efficient subproblem solvers have

also been proposed, see e.g. Zillober (2001) and Zillober

et al. (2004).

In the spirit of the early efforts by Schmit and Farshi

(1974), the last decades have seen the development of a

variety of separable and non-separable local approxima-tions based on intervening variables for use in sequen-tial approximate optimization (SAO), e.g. see Haftka and

Gürdal (1991), Barthelemy and Haftka (1993), Vanderplaats

(1993), Groenwold et al. (2007) and Kim and Choi (2008).

The intervening variables yield approximations that are non-linear and possibly non-convex in terms of the original or

direct variables. The resulting subproblems are typically

solved in their primal form by means of an appropriate

mathematical programming algorithm. Even though the

subproblem may be separable, either the non-convexity or

the inability to arrive at an analytical primal-dual relation

may hinder the utilization of the dual formulation. This

partly explains why these often highly accurate nonlinear

approximations are not as widely used in large-scale struc-tural optimization as the reciprocal type of approximations.

Recently, we have reported that SAO based on the

replacement of approximations using specific nonlin-ear intervening variables by their own convex diagonal

quadratic Taylor series expansions, may perform equally

well, or sometimes even better, than the original (con-vex) nonlinear approximations. In Groenwold and Etman

(2010b), we have demonstrated that the diagonal quadratic

approximation to the reciprocal and exponential approxi-mate objective functions can successfully be used in topol-ogy optimization. We have generalized this observation

even further in Groenwold et al. (2010), where diagonal

quadratic approximations toarbitrarynonlinear approxi-mate objective and constraint functions were constructed;

the resulting subproblems are convexified (when necessary),

and cast into the dual statement. This gives an SCP type of

algorithm which uses diagonal quadratic instead of recipro-cal type of approximations. However, these truly quadratic

approximations behave reciprocal-like. In addition, the form

of the dual subproblem does not depend on which non-linear approximations or intervening variables are selected

for quadratic treatment: all the approximated approxima-tions can be used simultaneously in a single dual statement.

In Groenwold et al. (2010), diagonal quadratic replace-ments for the reciprocal, exponential, CONLIN, MMA,

and TANA (Xu and Grandhi1998) approximations were

presented. We have described this approach with the term

“approximated-approximations”.

In this note, we convey the observation that the

approximated-approximations approach also allows for the

development of an SCP method that consists of series of of

diagonal QP subproblems, in the spirit of the well-known

SQP algorithms.

SQP methods construct Hessian matrices using second-order derivative information of the Lagrangian function,

often through an approximate quasi-Newton updating

scheme such as BFGS or SR1, e.g. see Nocedal and Wright

(2006), and many others. The storage and update of the

Hessian matrix may become burdensome for large-scale

structural optimization problems with very many design

variables, such as those encountered in topology opti-mization. (Note that BFGS updates, etc. result in dense

matrices, even if the original problem is sparse. While

the dense matrices need not be stored in limited memory

implementations, the computational effort required for the

matrix-vector multiplications is significant.) For this reason,

Fleury (1989) proposed an SQP method that uses diagonal

Hessian information only. He developed methods to effi-ciently calculate the diagonal second order derivatives in a

finite element environment.

Our approximated-approximations approach also allows

for the estimation of diagonal Hessian information with-out using historic information or exact Hessian information.

The diagonal curvature estimates follow directly from the

selected intervening variables. For reciprocal intervening

variables, this means that at every given iteration point,

only function value and gradient information evaluated at

the currentiterate is required, similar to existing gradient-based SCP methods. Various (two- or multipoint) extensions

to this are of course possible, but not discussed herein

for the sake of brevity. A related but different approach

was presented by Fleury (2009), who used an inner loop

of QP subproblems generated using second order Taylor

series to solve each nonlinear intervening-variables based

approximate subproblem generated by the outer loop.

This note is arranged as follows. Section2 presents

the optimization problem statement, and Section 3sum-marizes selected aspects of importance for SQP methods

in mathematical programming. Subsequently, we present

the basic concepts of SAO methods commonly used in

structural optimization in Section4, with a focus on SCP

algorithms. In Section5, we develop a first-order SCP algo-rithm based on diagonal QP subproblems. Numerical results

are offered in Section6, followed by selected conclusions in

Section7.

第三页

2 Optimization problem statement

We consider the nonlinear inequality constrained (struc-tural) optimization problem

min

x

f0(x),

subject to f

j(x)≤0, j =1,...,m,

x∈C⊆Rn

,

withC={x|ˇxi ≤xi ≤ˆxi, i =1,...,n}, (1)

wheref0(x)is a real valued scalar objective function, and

the f

j(x)areminequality constraint functions. ˇ xi and ˆ xi

respectively indicate lower and upper bounds of continuous

real variable xi. The functions f

j(x),j =0,1,...,mare

assumed to be (at least) once continuously differentiable. Of

particular importance here is that we assume that the eval-uation of (part of) the f

j(x),j =0,1,...,m, requires an

expensive numerical analysis, for instance a finite element

structural analysis. We furthermore assume that gradients

∂f

j/∂xi can be efficiently and accurately be calculated, see

e.g. van Keulen et al. (2005).

Herein, we are in particular interested in solving the

large scale variant of problem (1), with a large number

of design variables and constraints. We consider algo-rithms based on gradient-based approximations for which

the approximation functions

˜

f

{k}

j

developed at iteratex

{k}

satisfy (at least) the first-order conditions

˜

f

{k}

j

(x

{k}

) =

f

j(x

{k}

),and∂ ˜

f

{k}

j

/∂xi(x

{k}

) =∂f

老婆我爱你(篇七)
《老婆我爱你(建议用office2007版本以上打开或者wps2012以上的版本打开)》