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　　 教育没有什么惊天动地的大事，只要把每件小事做好，就能享受到教育的幸福。作者张曼凌老师就是这样一位一直在享受教育幸福的教师。她结合自己的经历，从班级管理、课堂教学、个别学生的教育、个性修炼及业余生活等角度人手，告诉各位教师，只要在细节上多用心，培养起学生学习及管理班级的积极性，不仅可以高效率地完成工作，还可以充分享受休闲生活。不把工作带回家其实就是这么简单！

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精彩书评

　　 小曼，一个必要的乌托邦。
　　 那是一个以诗做底子的小曼，那是一个以善良做血肉的小曼，那是一个以梦想做心灵的小曼，更是一个以执著做意志的 小曼。
　　 我在贪婪地品鉴着小曼，品鉴着她的文字，是因为一贯生活在愤怒和绝望中的郑杰，可以通过品鉴而知道除了批判，还有一种建设性的教育生活。
　　 那么多热爱教育的人们都在品鉴她和她的文字，是为了印证人心里期盼已久的安宁和渴望。
　　 细细品鉴，浮现在你眼前的，是那肃杀气氛里难得的一缕幽香。
　　 ——中国知名校长、人们眼中的“另类校长” 郑杰
　　
　　 读小曼这本《魅力女教师修炼记》，我再次坚信：一个教师，是否“优秀”不是重要的，关键的是，是否“幸福”。因为“优秀”与否是别人的评价，“幸福”与否是自己的感觉。小曼享受着学生，享受着工作，享受着每一个平凡而充实的日子，她因此而幸福。
　　 ——著名特级教师、成都武侯实验中学校长 李镇西
　　
　　 精致，源于细致；精致，始于精心，成于精彩！小曼老师和她的教育生活，浪漫而精心，细腻又精致，智慧且精彩！
　　 ——翔宇教育集团总校长、新教育研究院院长 户志文
　　
　　 小曼用心诠释了她对生活、对教育的热爱与敬畏。她的文字很快乐，很细腻，也很热情，读着让人安静，让人温暖。新教育让小曼在平凡的工作中做出了不平凡的成绩，这不仅是一位年轻女教师自我修炼的提升，更反映了一个教育人的自省。
　　 ——吉林市劳动模范 陈久文

目录

preface to first edition
preface
acknowledgments
1 introduction
1.1 computation and science
1.2 the emergence of modem computers
1.3 computer algorithms and languages
exercises

2 approximation of a function
2.1 interpolation
2.2 least-squares approximation
2.3 the millikan experiment
2.4 spline approximation
2.5 random-number generators
exercises

3 numerical calculus
3.1 numerical differentiation
3.2 numerical integration
3.3 roots of an equation
3.4 extremes of a function
3.5 classical scattering
exercises

4 ordinary differential equations
4.1 initial-value problems
4.2 the euler and picard methods
4.3 predictor-corrector methods
4.4 the runge-kutta method
4.5 chaotic dynamics of a driven pendulum
4.6 boundary-value and eigenvalue problems
4.7 the shooting method
4.8 linear equations and the sturm-liouville problem
4.9 the one-dimensional schr6dinger equation
exercises

5 numerical methods for matrices
5.1 matrices in physics
5.2 basic matrix operations
5.3 linear equation systems
5.4 zeros and extremes of multivariable functions
5.5 eigenvalue problems
5.6 the faddeev-leverrier method
5.7 complex zeros of a polynomial
5.8 electronic structures of atoms
5.9 the lanczos algorithm and the many-body problem
5.10 random matrices
exercises

6 spectral analysis
6.1 fourier analysis and orthogonal functions
6.2 discrete fourier transform
6.3 fast fourier transform
6.4 power spectrum of a driven pendulum
6.5 fourier transform in higher dimensions
6.6 wavelet analysis
6.7 discrete wavelet transform
6.8 special functions
6.9 gaussian quadratures
exercises

7 partial differential equations
7.1 partial differential equations in physics
7.2 separation of variables
7.3 discretization of the equation
7.4 the matrix method for difference equations
7.5 the relaxation method
7.6 groundwater dynamics
7.7 initial-value problems
7.8 temperature field of a nuclear waste rod
exercises

8 molecular dynamics simulations
8.1 general behavior of a classical system
8.2 basic methods for many-body systems
8.3 the verlet algorithm
8.4 structure of atomic clusters
8.5 the gear predictor-corrector method
8.6 constant pressure, temperature, and bond length
8.7 structure and dynamics of real materials
8.8 ab initio molecular dynamics
exercises

9 modeling continuous systems
9.1 hydrodynamic equations
9.2 the basic finite element method
9.3 the ritz variational method
9.4 higher-dimensional systems
9.5 the finite element method for nonlinear equations
9.6 the particle-in-cell method
9.7 hydrodynamics and magnetohydrodynamics
9.8 the lattice boltzmann method
exercises

10 monte carlo simulations
10.1 sampling and integration
10.2 the metropolis algorithm
10.3 applications in statistical physics
10.4 critical slowing down and block algorithms
10.5 variational quantum monte carlo simulations
10.6 green's function monte carlo simulations
10.7 two-dimensional electron gas
10.8 path-integral monte carlo simulations
10.9 quantum lattice models
exercises

11 genetic algorithm and programming
11.1 basic elements of a genetic algorithm
11.2 the thomson problem
11.3 continuous genetic algorithm
11.4 other applications
11.5 genetic programming
exercises

12 numerical renormalization
12.1 the scaling concept
12.2 renormalization transform
12.3 critical phenomena: the ising model
12.4 renormalization with monte carlo simulation
12.5 crossover: the kondo problem
12.6 quantum lattice renormalization
12.7 density matrix renormalization
exercises
references
index

精彩书摘

The basic idea behind a genetic algorithm is to follow the biological processof evolution in selecting the path to reach an optimal configuration of a givencomplex system. For exampie, for an interacting many-body system, the equilib-rium is reached by moving the system to the configuration that is at the globalminimum on its potential energy surface. This is single-objective optimization,which can be described mathematically as searching for the global minimum ofa multivariable function. Multiobjective optimization involvesmore than one equation, for example, a search for the minima of gk Both types ofoptimization can involve some constraints.We limit ourselves to single-objective optimization here. For a detailed dis-cussion on multi-objective optimization using the genetic algorithm, see Deb.
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前言/序言

计算物理学导论（第2版） [An Introduction to Computational Physics(Second Edition)] 电子书 下载 mobi epub pdf txt

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《统计学（第六版）》学习指导书

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