力学和质代写|Mechanics and Matter PHYS10006 University of Bristol Assignment

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Assignment-daixieTM为您提供布里斯托大学University of Bristol Mechanics and Matter PHYS10006力学和质代写代考辅导服务!

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Mechanics is a branch of physics that studies the motion of objects and the forces that cause them. It is concerned with how objects move and interact with each other, and how to describe and predict these behaviors using mathematical models. Mechanics is one of the oldest and most fundamental branches of physics, and has many important applications in engineering, technology, and everyday life.

Matter is anything that has mass and takes up space. It includes all the substances that we can see and touch, as well as those that are invisible to us, such as gases and subatomic particles. Matter is made up of tiny particles called atoms, which are themselves composed of even smaller particles such as protons, neutrons, and electrons.

Mechanics and matter are closely related, since mechanics is concerned with the motion and behavior of objects, and all objects are made of matter. The behavior of matter can be described and predicted using the laws of mechanics, which tell us how objects move and interact with each other under various conditions. For example, mechanics can be used to describe the motion of a ball rolling down a hill, the behavior of a spring as it is compressed and released, or the movement of planets and stars in the universe.

In summary, mechanics and matter are two important concepts in physics that are closely related. Mechanics helps us understand how objects move and interact with each other, while matter is the substance that makes up these objects and is affected by these interactions. Together, these concepts help us to understand the behavior of the physical world around us.

力学和质代写|Mechanics and Matter PHYS10006 University of Bristol Assignment

问题 1.

In a crash test, a truck with mass $2500 \mathrm{~kg}$ traveling at $24 \mathrm{~m} / \mathrm{s}$ smashes head-on into a concrete wall without rebounding. The front end crumples so much that the truck is $0.72 \mathrm{~m}$ shorter than before. (b) About how long does the collision last? (That is, how long is the interval between first contact with the wall and coming to a stop?)

证明 .

We can use the conservation of energy to find the initial kinetic energy of the truck:

$\frac{1}{2} m v^2=\frac{1}{2}(2500 \mathrm{~kg})(24 \mathrm{~m} / \mathrm{s})^2=1.44 \times 10^6 \mathrm{~J}$

During the collision, this kinetic energy is converted into deformation work done on the truck, which we can calculate from the change in length:

$W=\frac{1}{2} k x^2=\frac{1}{2} \frac{F}{\Delta x} x^2$

where $k$ is the spring constant of the deformed truck, $x$ is the amount of deformation, and $F$ is the average force exerted by the wall on the truck. Since the truck doesn’t rebound, we can assume that all of the initial kinetic energy is converted into deformation work, so we can equate these two expressions and solve for $F$:

$1.44 \times 10^6 \mathrm{~J}=\frac{1}{2} \frac{F}{0.72 \mathrm{~m}}(0.72 \mathrm{~m})^2 \Rightarrow F=2.96 \times 10^6 \mathrm{~N}$

问题 2.

(c) What is the magnitude of the average force exerted by the wall on the truck during the collision?

证明 .

We can use the impulse-momentum theorem to find the time interval $\Delta t$ during which the force is exerted:

$F \Delta t=\Delta p=m v_f-m v_i=2500 \mathrm{~kg}(0 \mathrm{~m} / \mathrm{s}-24 \mathrm{~m} / \mathrm{s})=-6.0 \times 10^4 \mathrm{~kg} \mathrm{~m} / \mathrm{s}$

Since the truck comes to a stop, the change in momentum is negative. Solving for $\Delta t$ gives:

$\Delta t=\frac{-6.0 \times 10^4 \mathrm{~kg} \mathrm{~m} / \mathrm{s}}{2.96 \times 10^6 \mathrm{~N}}=0.020 \mathrm{~s}$

So the collision lasts about 0.020 seconds.

问题 3.

It is interesting to compare this force to the weight of the truck. Calculate the ratio of the force of the wall to the gravitational force $m g$ on the truck. This large ratio shows why a collision is so damaging.

证明 .

The weight of the truck is $mg = (2500 \mathrm{~kg})(9.81 \mathrm{~m/s^2}) = 24.5 \times 10^3 \mathrm{~N}$. The ratio of the force of the wall to the gravitational force on the truck is:

$\frac{2.96 \times 10^6 \mathrm{~N}}{24.5 \times 10^3 \mathrm{~N}}=120$

So the force exerted by the wall is 120 times greater than the weight of the truck. This large ratio shows why a collision can be so damaging.

这是一份2023年的布里斯托大学University of Bristol Mechanics and Matter PHYS10006代写的成功案例

概率、统计和计量经济学代写|Probability, Statistics and Econometrics EFIM10024 University of Bristol Assignment

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Assignment-daixieTM为您提供布里斯托大学University of Bristol Probability, Statistics and Econometrics EFIM10024概率、统计和计量经济学代写代考辅导服务!

Instructions:

Probability, statistics, and econometrics are three closely related fields that are fundamental to understanding the behavior of systems that involve uncertainty, such as financial markets, natural phenomena, and social systems.

Probability theory deals with the study of random events and the likelihood of their occurrence. It is concerned with the formulation and manipulation of mathematical models that can be used to quantify the likelihood of different outcomes in a given situation.

Statistics is the science of collecting, analyzing, and interpreting data. It involves the use of mathematical and computational tools to summarize and make inferences from data, and to test hypotheses about the underlying mechanisms that generate the data.

Econometrics is the application of statistical methods to the analysis of economic data. It is concerned with developing models that can be used to understand the relationships between different economic variables and to make predictions about future economic outcomes.

All three fields are essential for making informed decisions in a variety of contexts, such as business, finance, public policy, and scientific research. They provide a rigorous framework for understanding and quantifying uncertainty, and for making predictions based on available data.

概率、统计和计量经济学代写|Probability, Statistics and Econometrics EFIM10024 University of Bristol Assignment

问题 1.

(Multivariate limit theorems) Let $\mathbf{X}=\left(X_1, \ldots, X_m\right)^{\prime}$ and $\mathbf{X}_n=\left(X_{n 1}, \ldots, X_{n m}\right)^{\prime}$ be $m$-dimensional random vectors. Define a norm $\|\mathbf{X}\|=\sqrt{X_1^2+\ldots+X_m^2}$. (a) Show that $E\|\mathbf{X}\|<\infty$ if and only if $E\left|X_i\right|<\infty$ for all $i=1, \ldots, m$.

证明 .

(a) By the Cauchy-Schwarz inequality, we have

$|\mathbf{X}|=\sqrt{X_1^2+\ldots+X_m^2} \leq \sqrt{m \cdot\left(X_1^2+\ldots+X_m^2\right)}=\sqrt{m} \cdot|\mathbf{X}|_2$,

where $|\cdot|_2$ denotes the Euclidean norm. Therefore, if $E|\mathbf{X}|<\infty$, then $E|\mathbf{X}|_2<\infty$. Since $|X|_2 \leq \sqrt{m} \cdot |X|$, we have $E|X|<\infty$. Conversely, if $E\left|X_i\right|<\infty$ for all $i=1,\ldots,m$, then

$E|\mathbf{X}| \leq \sqrt{\sum_{i=1}^m E\left(X_i^2\right)} \leq \sqrt{m \cdot \max _{i=1, \ldots, m} E\left(X_i^2\right)}<\infty$.

问题 2.

(b) Define $\mathbf{X}_n \rightarrow^p \mathbf{X}$ if for any $\varepsilon>0, \lim _{n \rightarrow \infty} P\left\{\left\|\mathbf{X}_n-\mathbf{X}\right\|>\varepsilon\right\}=0$. Show $\mathbf{X}_n \rightarrow^p \mathbf{X}$ if and only if $X_{n i} \rightarrow^p X_i$ for all $i=1, \ldots, m$.

证明 .

(b) Assume $\mathbf{X}_n \rightarrow^p \mathbf{X}$. Let $\epsilon > 0$ and let $i$ be any fixed index between $1$ and $m$. Then

$P\left(\left|X_{n i}-X_i\right|>\epsilon\right) \leq P\left(\left|\mathbf{X}_n-\mathbf{X}\right|>\epsilon\right) \rightarrow 0$

as $n\rightarrow\infty$. Thus, $X_{ni} \rightarrow^p X_i$ for all $i=1,\ldots,m$. Conversely, assume that $X_{ni} \rightarrow^p X_i$ for all $i=1,\ldots,m$. Let $\epsilon > 0$. Then

$P\left(\left|\mathbf{X}n-\mathbf{X}\right|>\epsilon\right)=P\left(\max {i=1, \ldots, m}\left|X_{n i}-X_i\right|>\frac{\epsilon}{\sqrt{m}}\right) \leq \sum_{i=1}^m P\left(\left|X_{n i}-X_i\right|>\frac{\epsilon}{\sqrt{m}}\right) \rightarrow 0$

as $n\rightarrow\infty$. Therefore, $\mathbf{X}_n \rightarrow^p \mathbf{X}$.

问题 3.

证明 .

The multivariate Central Limit Theorem states that if $\mathbf{X}_1,\mathbf{X}_2,\ldots$ are independent random vectors with $\mathbb{E}(\mathbf{X}_n)=\boldsymbol{\mu}$ and $\text{Var}(\mathbf{X}_n)=\boldsymbol{\Sigma}$, and if $\boldsymbol{\Sigma}$ is positive definite, then

$\frac{\mathbf{S}_n-\boldsymbol{\mu}}{\sqrt{n}} \Rightarrow \mathcal{N}(\mathbf{0}, \mathbf{\Sigma})$

where $\mathbf{S}n=\sum{i=1}^n\mathbf{X}_i$ and $\Rightarrow$ denotes convergence in distribution.

To prove this, we can use a version of the one-dimensional Linderberg-Feller’s theorem for multivariate random vectors. Specifically, let $\mathbf{X}_1,\mathbf{X}_2,\ldots$ be independent random vectors with $\mathbb{E}(\mathbf{X}_n)=\boldsymbol{\mu}$ and $\text{Var}(\mathbf{X}_n)=\boldsymbol{\Sigma}$, and let $\boldsymbol{\Sigma}$ be positive definite. Suppose that for all $\epsilon>0$,

$\lim {n \rightarrow \infty} \frac{1}{s_n^2} \sum{i=1}^n \mathbb{E}\left(\left|\mathbf{X}i-\boldsymbol{\mu}\right|^2 \mathbf{1}{\left{\left|\mathbf{X}_i-\boldsymbol{\mu}\right|>\epsilon s_n\right}}\right)=0$,

$\lim {n \rightarrow \infty} \frac{1}{s_n^2} \sum{i=1}^n \mathbb{E}\left(\left|\mathbf{X}i-\boldsymbol{\mu}\right|^2 \mathbf{1}{\left{\left|\mathbf{X}_i-\boldsymbol{\mu}\right|>\epsilon s_n\right}}\right)=0$,

where $s_n^2=\sum_{i=1}^n\text{Var}(|\mathbf{X}_i-\boldsymbol{\mu}|)$. Then,

$\frac{\mathbf{S}_n-\boldsymbol{\mu}}{s_n} \Rightarrow \mathcal{N}(\mathbf{0}, \mathbf{I})$,

where $\mathbf{I}$ is the identity matrix.

To prove this, we can use characteristic functions. Let $\boldsymbol{\lambda}$ be a fixed $m$-dimensional vector with $|\boldsymbol{\lambda}|=1$. Then, \begin{align*} \mathbb{E}(\exp{i\boldsymbol{\lambda}^{\prime}(\mathbf{S}n-\boldsymbol{\mu})/s_n})&=\mathbb{E}(\exp{i\boldsymbol{\lambda}^{\prime}(\sum{i=1}^n(\mathbf{X}i-\boldsymbol{\mu}))/s_n})\ &=\prod{i=1}^n\mathbb{E}(\exp{i\boldsymbol{\lambda}^{\prime}(\mathbf{X}i-\boldsymbol{\mu})/s_n})\ &=\prod{i=1}^n\exp{-\frac{1}{2}|\boldsymbol{\lambda}|^2\text{Var}(|\mathbf{X}_i-\boldsymbol{\mu}|)/s_n^2+o(1/s_n^2)}\ &=\exp{-\frac{1}{2}|\boldsymbol{\lambda}|^2/n+o(1/n)}, \end{align*}

这是一份2023年的布里斯托大学University of Bristol Probability, Statistics and Econometrics EFIM10024代写的成功案例

经济学代写|Economics ECON10001 University of Bristol Assignment

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Assignment-daixieTM为您提供布里斯托大学University of Bristol Economics ECON10001经济学代写代考辅导服务!

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This unit provides an overview of the key concepts and tools of modern economics, covering topics such as market behavior, institutional analysis, and policy formation. The course will begin with a comparison of historical and cross-country economic trends to provide context for the study of economic analysis. The course will then focus on the behavior of economic actors in the goods, labor, and credit markets, exploring how institutions and policy shape economic outcomes.

The course will examine both successful and failed market systems and study key economic variables such as GDP, unemployment, inequality, and inflation. Students will also learn about monetary and fiscal policy and explore how these policies affect the global economy. To analyze these topics, students will use empirical data, graphical and mathematical models, and historical and methodologically informed narrative.

Through their coursework, students will develop the skills to communicate economic concepts and ideas to both specialist and non-specialist audiences. This will include the ability to analyze and interpret economic data and use economic models to explain economic phenomena. Overall, this course provides a foundation in modern economics and prepares students to apply economic analysis to a wide range of issues and questions.

经济学代写|Economics ECON10001 University of Bristol Assignment

问题 1.

Let $X$ and $Y$ be random variables with finite variances. (i) Show that $$ \min _{g(\cdot)} E(Y-g(X))^2=E(Y-E(Y \mid X))^2 $$ where $g(\cdot)$ ranges over all functions.

证明 .

(i) Let $g^(x) = E(Y \mid X=x)$, then we have \begin{align} E(Y – g^(X))^2 &= E((Y – E(Y \mid X)) + (E(Y \mid X) – g^(X)))^2 \ &= E((Y – E(Y \mid X))^2) + E((E(Y \mid X) – g^(X))^2) \ &\qquad + 2E((Y – E(Y \mid X))(E(Y \mid X) – g^(X))) \ &= E((Y – E(Y \mid X))^2) + E((E(Y \mid X) – E(Y \mid X))^2) \ &= E((Y – E(Y \mid X))^2), \end{align*} where we have used the fact that $E(E(Y \mid X)) = E(Y)$ and the law of iterated expectations.

问题 2.

(ii) Assume $m(X)=E(Y \mid X)$ and write $Y=m(X)+e$. Show that $\operatorname{Var}(Y)=$ $\operatorname{Var}(m(X))+\operatorname{Var}(e)$

证明 .

(ii) We have \begin{align*} \operatorname{Var}(Y) &= E(Y – E(Y))^2 \ &= E((m(X) + e – E(m(X) + e))^2) \ &= E((m(X) – E(m(X)))^2) + E(e^2) + 2E((m(X) – E(m(X)))e) \ &= \operatorname{Var}(m(X)) + \operatorname{Var}(e) + 2E((m(X) – E(m(X)))e), \end{align*} where we have used the fact that $E(e) = 0$ and $\operatorname{Var}(m(X)) = E((m(X) – E(m(X)))^2)$.

To show that $E((m(X) – E(m(X)))e) = 0$, note that \begin{align*} E((m(X) – E(m(X)))e) &= E(E((m(X) – E(m(X)))e \mid X)) \ &= E((m(X) – E(m(X)))E(e \mid X)) \ &= 0, \end{align*} where we have used the fact that $E(e \mid X) = 0$.

问题 3.

(iii) If $E(Y \mid X=x)=a+b x$ find $E(Y X)$ as a function of moments of $X$.

证明 .

(iii) We have \begin{align*} E(YX) &= E((a+bX)X) \ &= aE(X) + bE(X^2). \end{align*}

这是一份2023年的布里斯托大学University of Bristol conomics ECON10001代写的成功案例

金融数学代写|Mathematics for Economics EFIM10023 University of Bristol Assignment

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Assignment-daixieTM为您提供布里斯托大学University of Bristol Mathematics for Economics EFIM10023金融数学代写代考辅导服务!

Instructions:

Mathematics is an essential tool for economics, as it allows economists to model economic phenomena and analyze economic data using rigorous and precise methods. Here are some of the key mathematical concepts that are important for economics:

  1. Calculus: Calculus is essential for modeling changes in economic variables over time. It is used to derive optimization problems, which are fundamental to economic analysis. Calculus is also used to analyze the behavior of economic functions, such as demand and supply curves.
  2. Linear Algebra: Linear algebra is used to solve systems of linear equations, which arise frequently in economics. It is also used in matrix algebra, which is important for input-output analysis, among other things.
  3. Probability and Statistics: Probability and statistics are essential for analyzing economic data, making inferences about populations from samples, and testing hypotheses. Probability is used to model the uncertainty that is inherent in economic decisions, while statistics is used to measure the variability in economic data.
  4. Optimization Theory: Optimization theory is used to model economic agents’ behavior, such as firms and consumers, who seek to maximize their objectives subject to constraints. This is the basis of microeconomic theory.
金融数学代写|FINANCIAL MATHEMATICS MATH260 University of Liverpool Assignment

问题 1.

Let $X_1, \ldots, X_n$ be indpendent random variables with $$ X_i \sim P_{\theta_i}, \text { for } i=1, \ldots, n $$ (a). For $P_\theta=N(\theta, 1)$, determine the maximum likelihood estimate of $$ \left(\theta_1, \ldots, \theta_n\right) $$ when there are no restrictions on the $\theta_i$.

证明 .

(a) For $P_{\theta}=N(\theta,1)$, the likelihood function is given by \begin{align*} L(\theta_1,\ldots,\theta_n;x_1,\ldots,x_n) &= \prod_{i=1}^{n} \frac{1}{\sqrt{2\pi}} \exp\left(-\frac{(x_i-\theta_i)^2}{2}\right) \ &= \frac{1}{(2\pi)^{n/2}} \exp\left(-\frac{1}{2}\sum_{i=1}^{n}(x_i-\theta_i)^2\right). \end{align*} Taking the logarithm of the likelihood function, we obtain \begin{align*} \log L(\theta_1,\ldots,\theta_n;x_1,\ldots,x_n) &= -\frac{n}{2}\log(2\pi) – \frac{1}{2}\sum_{i=1}^{n}(x_i-\theta_i)^2. \end{align*} Differentiating the log-likelihood function with respect to $\theta_i$, we obtain \begin{align*} \frac{\partial}{\partial \theta_i} \log L(\theta_1,\ldots,\theta_n;x_1,\ldots,x_n) &= \sum_{i=1}^{n}(x_i-\theta_i). \end{align*} Setting the derivative equal to zero, we obtain the maximum likelihood estimate (MLE) of $\theta_i$ as \begin{align*} \hat{\theta_i} &= \frac{1}{n}\sum_{i=1}^{n} x_i. \end{align*} Therefore, the MLE of $\left(\theta_1,\ldots,\theta_n\right)$ is $\left(\frac{1}{n}\sum_{i=1}^{n} x_i,\ldots,\frac{1}{n}\sum_{i=1}^{n} x_i\right)$.

问题 2.

(b). In (a), for $n=2$ determine the maximum likelihood estimate of when $\left(\theta_1, \theta_2\right)$ is restricted to satisfy $\theta_1 \leq \theta_2$.

证明 .

(b) For $\$ n=2 \$$, we have
$\backslash$ begin{align $}$
$\backslash \log L\left(\backslash\right.$ theta_1,\theta_2;x_1,x_2) $\&=-\backslash \log (2 \backslash p i)-\backslash f r a c\left{\left(x_{-} 1-\backslash \text { theta_1 }\right)^{\wedge} 2\right}{2}-$
$\backslash f r a c\left{\left(x_{-} 2-\backslash \text { theta_2 }\right)^{\wedge} 2\right}{2}$.
\end } { \text { align } { } ^ { * } }
Taking partial derivatives of the log-likelihood function with respect to $\$ \backslash$ theta_1\$ and \$\theta_2\$, we obtain
$\backslash$ begin{align $}$
$\backslash f r a c{$ partial $} \backslash$ partial $\backslash$ theta_2} $\backslash \log L\left(\backslash\right.$ theta_1,\theta_2;x_1,x_2) $8=x_{-} 2$ – $\backslash$ theta_2.
\end{align } }
Setting both derivatives equal to zero, we obtain the MLE of $\$ \backslash$ theta_1\$ and
\$1theta_2\$ as
\begin{align } { } ^ { * } }
\hat{theta_1} $8=x _1, \backslash$
\hat ${$ theta_2} $8=x$. 2 .
$\backslash$ \end } { a \operatorname { l i g n } { } ^ { * } }

问题 3.

(c). Repeat (a) and (b) when $P_\theta$ is the Laplace distribution with density $$ f(x \mid \theta)=\frac{1}{2} \exp \{-|x-\theta|\},-\infty<x<\infty . $$

证明 .

(c) For $P_{\theta}$ with Laplace distribution, the likelihood function is given by \begin{align*} L(\theta_1,\ldots,\theta_n;x_1,\ldots,x_n) &= \prod_{i=1}^{n} \frac{1}{2} \exp\left(-|x_i-\theta_i|\right) \ &= \frac{1}{2^n} \exp\left(-\sum_{i=1}^{n}|x_i-\theta_i|\right). \end{align*} Taking the logarithm of the likelihood function, we obtain \begin{align*} \log L(\theta_1,\ldots,\theta_n;x_1,\ldots,x_n) &= -n\log 2 – \sum_{i=1}^{n}|x_i-\theta_i|. \end{align*} Differentiating the log-likelihood function with respect to $\theta_i$, we obtain \begin{align*} \frac{\partial}{\partial \theta_i} \log L(\theta_1,\ldots,\theta_n;x_1,\ldots,x_n) &= \begin{cases} 1, & x_i > \theta_i \ -1, & x_i < \theta_i \ \text{undefined}, & x_i = \theta_i. \end{cases} \end{align*} Since the derivative is undefined at $x_i=\theta_i$, we need to consider the two cases $x_i < \theta_i$ and $x_i > \theta_i$ separately. For $x_i < \theta_i$, we have $\frac{\partial}{\partial \theta_i} \log L(\theta_1,\ldots,\theta_n;x_1,\ldots,x_n) = -1$. For $x_i > \theta_i$, we have $\frac{\partial}{\partial \theta_i} \log L(\theta_1,\ldots,\theta_n;x_1,\ldots,x_n) = 1$. Therefore, the MLE of $\theta_i$ is the median of ${x_1,\ldots,x_n}$, i.e., $\hat{\theta_i} = \text{median}(x_1,\ldots,x_n)$.

这是一份2023年的布里斯托大学University of Bristol Mathematics for Economics EFIM10023代写的成功案例

金融数学代写|FINANCIAL MATHEMATICS MATH260 University of Liverpool Assignment

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Assignment-daixieTM为您提供利物浦大学University of Liverpool FINANCIAL MATHEMATICS MATH260金融数学代写代考辅导服务!

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Financial Mathematics is an interdisciplinary field that combines mathematics, statistics, and finance to analyze and solve problems related to financial markets and instruments.

Derivative pricing is a central problem in Financial Mathematics, as it involves determining the fair price of a financial instrument whose value is derived from an underlying asset, such as a stock or commodity. Derivatives include options, futures, swaps, and other financial contracts that can be used to manage risk or speculate on market movements.

To price derivatives, mathematical models are used to describe the behavior of the underlying asset over time. One popular model is the Black-Scholes model, which assumes that the price of the underlying asset follows a geometric Brownian motion and that the market is efficient and frictionless. Other models include stochastic volatility models, jump-diffusion models, and local volatility models, which allow for more complex and realistic dynamics.

Once a model is chosen, various mathematical techniques can be used to derive formulas for the price of derivatives and the optimal hedging strategies to minimize risk. These techniques include Monte Carlo simulations, partial differential equations, and Fourier transform methods.

金融数学代写|FINANCIAL MATHEMATICS MATH260 University of Liverpool Assignment

问题 1.

ROA can be defined as: $\mathbf{R O A}=$ Profit margin $x$ Asset Turnover
Calculate Intel’s ROA, profit margin, and asset turnover for 2001 and 2002. For simplicity, ignore interest income and interest expense in your calculations.

证明 .

$$
\begin{aligned}
& \text { ROA }=\mathrm{NI} / \text { (Average Total Assets) } \
& \text { 2001: } \quad 1291 / .5(44395+47945)=2.8 \% \
& \text { 2002: } \quad 3117 / .5(44395+44224)=7 \% \
&
\end{aligned}
$$
Profit Margin = NI/Sales
$$
\begin{array}{llll}
\text { 2001: } & & 1291 / 26539 & =4.9 \% \
\text { 2002: } & 3117 / 26764 & =11.6 \%
\end{array}
$$
Asset Turnover $=$ Sales $/($ Average Total Assets $)$
$$
\begin{array}{ll}
\text { 2001: } & 26539 / .5(44395+47945)=57 \% \
\text { 2002: } & 26764 / .5(44395+44224)=60.4 \%
\end{array}
$$

问题 2.

For the years 2001 and 2002, calculate one ratio each year that is indicative of Intel’s short-term liquidity. Briefly comment on Intel’s liquidity.

证明 .

Current Ratio $=($ Current Assets $) /($ Current Liabilities $)$
$\begin{array}{ll}\text { 2001: } & 17633 / 6570=2.68 \ \text { 2002: } & 18925 / 6595=2.87\end{array}$
Quick Ratio $=($ Cash + Marketable Sec. + Accounts Receivable $) /($ Current Liabilities $)$
2001: $\quad(7970+2607) / 6570=1.61$
$2002: \quad(7404+2574) / 6595=1.51$
Intel has very high liquidity.

问题 3.

For 2002 calculate the Days Inventory held by Intel. What is the cost and/or risk of holding high inventory for Intel?

证明 .

Inventory Turnover $=\mathrm{COGS} /($ Average Inventory $)=8650 / .5(2276+2253)=3.82$
Days Inventory Held $=365 /($ Inventory Turnover $)=365 / 3.82=95.6$ days
The cost or risk associated with holding high inventory is that prices drop quickly, particularly in Intel’s industry. There is also concern for the obsolescence of finished goods.

这是一份2023年的利物浦大学University of Liverpool FINANCIAL MATHEMATICS MATH260代写的成功案例

换元代数代写|COMMUTATIVE ALGEBRA MATH247 University of Liverpool Assignment

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Assignment-daixieTM为您提供利物浦大学University of Liverpool METRIC SPACES AND CALCULUS MATH242度量空间和微积分代写代考辅导服务!

Instructions:

Commutative algebra is a branch of abstract algebra that studies commutative rings, which are algebraic structures that have two binary operations, usually denoted as addition and multiplication. Commutative algebra plays an important role in several areas of mathematics, including number theory, algebraic geometry, and linear algebra.

In commutative algebra, one studies commutative rings, which are rings in which the multiplication operation is commutative. Examples of commutative rings include the ring of integers, polynomial rings, and the field of real numbers. Commutative algebra also studies modules over commutative rings, which are generalizations of vector spaces over a field.

The theory of commutative algebra has many applications to other areas of mathematics. For example, it is used in algebraic geometry to study algebraic varieties, which are geometric objects defined by systems of polynomial equations. Commutative algebra is also used in number theory to study algebraic number fields, which are extensions of the rational numbers that are closed under addition, subtraction, multiplication, and division.

Finally, commutative algebra has applications to linear algebra, especially in the study of linear transformations of vector spaces over fields. For example, if one has a linear transformation of a vector space that is represented by a matrix, then commutative algebra can be used to study the properties of that matrix, such as its eigenvalues and eigenvectors.

换元代数代写|COMMUTATIVE ALGEBRA MATH247 University of Liverpool Assignment

问题 1.

Show that $k\left[\mathbb{A}^2 \backslash{0}\right]=k\left[\mathbb{A}^2\right]$. Conclude that $\mathbb{A}^2 \backslash{0}$ is not affine.
[Hint: Use the covering by two affine open subsets given by $x \neq 0$ and $y \neq 0$, where $x, y$ are coordinates on $\left.\mathbb{A}^2\right]$.

证明 .

Let $U_1$ and $U_2$ be the affine open subsets of $\mathbb{A}^2$ defined by $x\neq0$ and $y\neq0$, respectively. We want to show that $k[U_1\cup U_2] = k[\mathbb{A}^2]$. Since $U_1$ and $U_2$ cover $\mathbb{A}^2\backslash{0}$, it is sufficient to show that $k[U_1\cup U_2] = k[\mathbb{A}^2\backslash{0}]$.

Note that $\mathbb{A}^2\backslash{0} = U_1\cup U_2 \cup (U_1\cap U_2)$. Therefore, we have the following chain of inclusions: \begin{align*} k[\mathbb{A}^2\backslash{0}] &\subseteq k[U_1\cup U_2 \cup (U_1\cap U_2)]\ &\subseteq k[U_1] \cap k[U_2] \cap k[U_1\cap U_2]\ &= k[\mathbb{A}^2]. \end{align*} The first inclusion follows from the fact that $k[\mathbb{A}^2\backslash{0}]$ is the subring of $k(\mathbb{A}^2)$ consisting of rational functions that are regular on $\mathbb{A}^2\backslash{0}$, and every such function is clearly regular on $U_1\cup U_2 \cup (U_1\cap U_2)$. The second inclusion follows from the fact that a rational function that is regular on $U_1\cup U_2 \cup (U_1\cap U_2)$ must be regular on each of these sets individually.

Since we have $k[U_1\cup U_2] = k[\mathbb{A}^2]$, it follows that $\mathbb{A}^2\backslash{0}$ cannot be affine, because an affine variety cannot be covered by two non-empty affine open subsets.

问题 2.

Let $C$ be a curve in $\mathbb{P}^2, x$ be a point in $C$ and $L$ a line passing through $x$. Let $m$ be the multiplicity of $C$ at $x$ and $M$ the multiplicity of intersection of $C$ and $L$ at $x$. Show that $m \leq M$ and that for given $C, x$ the equality $m=M$ holds for all but finitely many lines $L$ as above.

证明 .

Let $f$ be a homogeneous polynomial of degree $d$ defining $C$. We may assume that $x = [1:0:0]$. Up to projective transformation we may also assume that $L$ is given by $y = 0$. Then $C \cap L$ is defined by the ideal $(f, y)$ in $k[x,y,z]$.

Since $f$ vanishes at $x$, we may write $$f(x,y,z) = z^m g(x,y,z)$$ where $g(x,y,z)$ is a homogeneous polynomial of degree $d-m$ and $g(1,0,0) \neq 0$. Then $$(f, y) = (z^m g(x,y,z), y) = (z^m, y) + (g(x,y,z), y).$$ Since $y$ does not divide $z^m$, we have $(z^m, y) = (y)$, and so $$(f, y) = (y) + (g(x,y,z), y).$$ Thus the intersection multiplicity $M$ is equal to the order of vanishing of $g(x,y,z)$ at $[1:0:0]$, which is at least $m$.

To show that $m = M$ for all but finitely many lines $L$, it suffices to show that the intersection multiplicity $M$ is upper semicontinuous in $L$. In other words, we need to show that if $L_t$ is a family of lines converging to $L$ (in the Zariski topology) then $M_t \geq M$ for $t$ sufficiently close to $0$.

We may assume that the family $L_t$ is given by $y = t$. Let $g_t(x,z)$ be the polynomial of degree $d-m$ defining $C \cap L_t$ at $[1:0:0]$. Then $g_t(x,z) = f(x,t,z)$, and so the coefficient of $z^{d-m}$ in $g_t(x,z)$ is given by $$a_t = f_{0,d-m}(x,t,1).$$ Since $f_{0,d-m}(x,t,1)$ is a polynomial in $x$ and $t$, it is upper semicontinuous in $t$. Therefore, there exists a neighborhood $U$ of $0$ such that $a_t > 0$ for all $t \in U$.

问题 3.

Let $Z$ be an irreducible closed subset in an algebraic variety $X$. Show that if $\operatorname{dim}(Z)=\operatorname{dim}(X)$ then $Z$ is a component of $X$.

证明 .

Suppose that $Z$ is not a component of $X$. Then there exists a proper closed subset $Y$ of $X$ such that $Z$ is contained in $Y$. Since $Y$ is a proper subset of $X$, we have $\operatorname{dim}(Y) < \operatorname{dim}(X)$. By the irreducibility of $Z$, we have $Z \not\subseteq Y$. Therefore, there exists a point $p\in Z$ such that $p\notin Y$.

Since $p$ is a point of $Z$, we have $\operatorname{dim}(\mathcal{O}{X,p}) \geq \operatorname{dim}(Z)$, where $\mathcal{O}{X,p}$ is the local ring of $X$ at $p$. On the other hand, since $p\notin Y$, we have $\mathcal{O}{X,p} \subseteq \mathcal{O}{Y,p}$. This implies $\operatorname{dim}(\mathcal{O}{X,p}) \leq \operatorname{dim}(\mathcal{O}{Y,p})$. By the definition of dimension of a local ring, we have $\operatorname{dim}(Y) = \operatorname{dim}(\mathcal{O}_{Y,p})$. Combining these inequalities, we obtain $\operatorname{dim}(Z) \leq \operatorname{dim}(Y)$. This contradicts the assumption that $\operatorname{dim}(Z) = \operatorname{dim}(X)$.

Therefore, our assumption that $Z$ is not a component of $X$ must be false. Thus, $Z$ is a component of $X$.

这是一份2023年的利物浦大学University of Liverpool COMMUTATIVE ALGEBRA MATH247代写的成功案例

统计与概率代写|STATISTICS AND PROBABILITY II MATH254 University of Liverpool Assignment

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Assignment-daixieTM为您提供利物浦大学University of Liverpool STATISTICS AND PROBABILITY II MATH254统计与概率代写代考辅导服务!

Instructions:

It’s good to hear that it covers both discrete and continuous random variables, as well as important distributions like the geometric, exponential, and normal distributions.

I also appreciate that the module highlights the importance of measure theory and probability as a foundation for this material. This should help students gain a deeper understanding of the underlying concepts and be better prepared to apply them in real-world situations.

Overall, it sounds like a valuable module for students interested in actuarial science, financial mathematics, statistics, and the physical sciences.

统计与概率代写|STATISTICS AND PROBABILITY II MATH254 University of Liverpool Assignment

问题 1.

Corrupted by their power, the judges running the popular game show America’s Next Top Mathematician have been taking bribes from many of the contestants. Each episode, a given contestant is either allowed to stay on the show or is kicked off.

If the contestant has been bribing the judges she will be allowed to stay with probability 1. If the contestant has not been bribing the judges, she will be allowed to stay with probability $1 / 3$.

Suppose that $1 / 4$ of the contestants have been bribing the judges. The same contestants bribe the judges in both rounds, i.e., if a contestant bribes them in the first round, she bribes them in the second round too (and vice versa).
(a) If you pick a random contestant who was allowed to stay during the first episode, what is the probability that she was bribing the judges?

证明 .

(a) We first compute $P\left(S_1\right)$ using the law of total probability.
$$
P\left(S_1\right)=P\left(S_1 \mid B\right) P(B)+P\left(S_1 \mid H\right) P(H)=1 \cdot \frac{1}{4}+\frac{1}{3} \cdot \frac{3}{4}=\frac{1}{2} .
$$
We therefore have (by Bayes’ rule) $P\left(B \mid S_1\right)=P\left(S_1 \mid B\right) \frac{P(B)}{P\left(S_1\right)}=1 \cdot \frac{1 / 4}{1 / 2}=\frac{1}{2}$.

问题 2.

(b) If you pick a random contestant, what is the probability that she is allowed to stay during both of the first two episodes?

证明 .

(b) Using the tree we have the total probability of $S_2$ is
$$
P\left(S_2\right)=\frac{1}{4}+\frac{3}{4} \cdot \frac{1}{3} \cdot \frac{1}{3}=\frac{1}{3}
$$

问题 3.

(c) If you pick random contestant who was allowed to stay during the first episode, what is the probability that she gets kicked off during the second episode?

证明 .

(c) We want to compute $P\left(L_2 \mid S_1\right)=\frac{P\left(L_2 \cap S_1\right)}{P\left(S_1\right)}$.
From the calculation we did in part (a), $P\left(S_1\right)=1 / 2$. For the numerator, we have (see the tree)
$$
P\left(L_2 \cap S_1\right)=P\left(L_2 \cap S_1 \mid B\right) P(B)+P\left(L_2 \cap S_1 \mid H\right) P(H)=0 \cdot \frac{1}{3}+\frac{2}{9} \cdot \frac{3}{4}=\frac{1}{6}
$$
Therefore $P\left(L_2 \mid S_1\right)=\frac{1 / 6}{1 / 2}=\frac{1}{3}$.

这是一份2023年的利物浦大学University of Liverpool STATISTICS AND PROBABILITY II MATH254代写的成功案例

度量空间和微积分代写|METRIC SPACES AND CALCULUS MATH242 University of Liverpool Assignment

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Assignment-daixieTM为您提供利物浦大学University of Liverpool METRIC SPACES AND CALCULUS MATH242度量空间和微积分代写代考辅导服务!

Instructions:

Metric spaces and calculus are two important areas of mathematics that are closely related to each other.

A metric space is a set of points, along with a metric or distance function that assigns a non-negative real number to every pair of points in the set. The metric satisfies certain properties such as the triangle inequality, which states that the distance between any two points in the set is always less than or equal to the sum of the distances between those points and a third point in the set. Examples of metric spaces include Euclidean space, the space of continuous functions, and the space of square integrable functions.

Calculus, on the other hand, is the branch of mathematics that deals with the study of rates of change and accumulation. It includes the study of limits, derivatives, integrals, and differential equations. Calculus is used in many areas of science and engineering, such as physics, economics, and engineering.

The study of calculus is closely related to metric spaces, as calculus often involves the analysis of functions defined on metric spaces. In particular, the concept of continuity, which is central to calculus, is closely related to the topology of a metric space. The notions of limit, derivative, and integral can all be defined in the context of metric spaces, and many of the results of calculus carry over to more general metric spaces.

度量空间和微积分代写|METRIC SPACES AND CALCULUS MATH242 University of Liverpool Assignment

问题 1.

Let $\gamma$ be the semi-circle connecting $(0,0)$ and $(2,0)$ that sits in the half plane where $y \geq 0$. Given $\mathbf{f}(x, y)=\left(2 x+\cos y,-x \sin y+y^7\right)$, calculate $\int \mathbf{f} \cdot d \gamma$. If your calculation requires justification from a theorem we proved in class, state the theorem you are using.

证明 .

Notice that $D_1 f_2(x, y)=-\sin y=D_2 f_1(x, y)$. Since $f(x, y)$ is defined on all of $\mathbb{R}^2$, which is convex, we conclude $f(x, y)$ is a gradient field. Thus the integral of $f(x, y)$ from $(0,0)$ to $(2,0)$ is independent of the path. Let us integrate on a straight line $s:[0,2] \rightarrow \mathbb{R}^2$ defined by $s(t)=(t, 0)$ :
$$
\int_C\left(2 x+\cos y,-x \sin y+y^7\right) d s=\int_0^2\left(2 t+\cos 0,-t \sin 0+0^7\right) \cdot(1,0) d t=\left[t^2+t\right]_0^2=6
$$

问题 2.

Let $f(x, y, z)=x^2+y^2+z^2$. Prove $f$ is differentiable at $(1,1,1)$ with linear transformation $T(x, y, z)=2 x+2 y+2 z$.

证明 .

Solution To prove $f$ is differentiable with total derivative $T$ as described we need to show
$$
\lim {|\mathbf{v}| \rightarrow 0} \frac{f(\mathbf{v}+(1,1,1))-f(1,1,1)-T(\mathbf{v})}{|\mathbf{v}|}=0 $$ Now observe that $$ f(\mathbf{v}+(1,1,1))-f(1,1,1)-T(\mathbf{v})=\left(v_1+1\right)^2+\left(v_2+1\right)^2+\left(v_3+1\right)^2-3-2 v_1-2 v_2-2 v_3=v_1^2+v_2^2+v_3^2 $$ Thus $$ \lim {|\mathbf{v}| \rightarrow 0} \frac{f(\mathbf{v}+(1,1,1))-f(1,1,1)-T(\mathbf{v})}{|\mathbf{v}|}=\lim {|\mathbf{v}| \rightarrow 0} \frac{|\mathbf{v}|^2}{|\mathbf{v}|}=\lim {|\mathbf{v}| \rightarrow 0}|\mathbf{v}|=0 .
$$
It follows that $f$ is differentiable at $(1,1,1)$ with the total derivative as described.

问题 3.

Consider $f(x, y)=(x y+y)^{10}$ on the square $Q=[0,1] \times[0,1]$. Evaluate $\iint_Q f d x d y$.

证明 .

The function
$$
f(x, y)=(x y+y)^{10}
$$
is continous on $\mathbb{R}^2$. Thus, $\int_0^1 f(x, y) d x$ is integrable for all $y \in[0,1]$ so one can apply Fubini’s Theorem to get:
$$
\iint_Q(x y+y)^{10} d x d y=\int_0^1 \int_0^1(x y+y)^{10} d x d y=\int_0^1\left[\frac{(x y+y)^{11}}{11 y}\right]_0^1 d y=\int_0^1 \frac{\left(2^{11}-1\right) y^{10}}{11} d y=\frac{2047}{121}
$$

这是一份2023年的利物浦大学University of Liverpool METRIC SPACES AND CALCULUS MATH242代写的成功案例

复数函数代写|COMPLEX FUNCTIONS MATH243 University of Liverpool Assignment

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Assignment-daixieTM为您提供利物浦大学University of Liverpool COMPLEX FUNCTIONS MATH243复数函数代写代考辅导服务!

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Harmonic functions are a class of functions that arise naturally in many areas of mathematics and physics, including the study of partial differential equations, potential theory, and complex analysis.

In essence, a function is said to be harmonic if it satisfies Laplace’s equation, which is a partial differential equation that arises frequently in the study of physical systems. Laplace’s equation has many interesting properties, and solutions to this equation often exhibit beautiful symmetries and patterns.

Harmonic functions have many practical applications as well. For example, they are used in electrical engineering to model the flow of electric current through conductive materials, and they are also important in fluid dynamics, where they are used to study the behavior of fluids in motion.

Overall, the theory of harmonic functions is a rich and fascinating subject, with many connections to other areas of mathematics and physics. It is definitely worth exploring further if you are interested in these topics.

复数函数代写|COMPLEX FUNCTIONS MATH243 University of Liverpool Assignment

问题 1.

Find all solutions $z$ to equation $z^3=-8 i$.

证明 . $\begin{aligned} z^3=8 e^{-i \pi / 2} & \Longrightarrow z=2 e^{-i\left(\frac{\pi}{6}+\frac{2 \text { nin }}{3}\right)}, 0 \leq n \leq 2, \ & \Longrightarrow z=2 i \text { or } z=\sqrt{3}-i \text { or } z=-\sqrt{3}-i .\end{aligned}$

问题 2.

Evaluate the integral
$$
\int_{|z-1|=\frac{1}{2}} \frac{d z}{(1-z)^3} .
$$

证明 .

$\begin{aligned} \int_{|z-1|=\frac{1}{2}} \frac{d z}{(1-z)^3} & =\int_0^{2 \pi} \frac{i e^i t / 2}{\left(-e^{i t} / 2\right)^3} d t \ & =\frac{-i}{2} \cdot 8 \int_0^{2 \pi} e^{-i \cdot 2 t} d t \ & =-\left.4 i \frac{e^{-2 i t}}{-2 i}\right|_0 ^{2 \pi} \ & =0 .\end{aligned}$

问题 3.

Evaluate the integral
$$
\int_\gamma \frac{e^z+z}{z-2} d z
$$
in the two cases: 1) $\gamma={z:|z|=1}$;
2) $\gamma={z:|z|=3}$.

证明 .

1) $\int_{|z|=1} \frac{e^2+z}{z-2} d z=0$, since $2 \notin{z:|z|<1}$.
2) $\int_{|z|=3} \frac{e^z+z}{z-2} d z=2 \pi i\left(e^2+2\right)$, since $n(\gamma, 2)=1$.

这是一份2023年的利物浦大学University of Liverpool COMPLEX FUNCTIONS MATH243 代写的成功案例

经典力学代写|CLASSICAL MECHANICS MATH228 University of Liverpool Assignment

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Assignment-daixieTM为您提供利物浦大学University of Liverpool CLASSICAL MECHANICS MATH228经典力学代写代考辅导服务!

Instructions:

Classical mechanics is a branch of physics that deals with the study of the motion of objects under the influence of forces. It was first developed by Sir Isaac Newton in the 17th century and is based on three fundamental laws of motion:

  1. The law of inertia: An object at rest will remain at rest and an object in motion will continue in a straight line at a constant speed unless acted upon by an external force.
  2. The law of acceleration: The acceleration of an object is directly proportional to the force applied to it and inversely proportional to its mass. This is expressed mathematically as F = ma, where F is the force applied to the object, m is its mass, and a is its acceleration.
  3. The law of action-reaction: For every action, there is an equal and opposite reaction.

Classical mechanics also includes the study of energy and momentum, and how they are conserved in various physical processes. It has numerous applications in engineering, physics, and astronomy, and is the foundation for the development of modern physics.

经典力学代写|CLASSICAL MECHANICS MATH228 University of Liverpool Assignment

问题 1.

A driven oscillator is described by
$$
\ddot{x}+\omega_o^2 x=\frac{F}{m} \cos (\gamma t+\alpha) .
$$
We found that the solution off resonance is
$$
x(t)=B \cos \left(\omega_o t+\beta\right)+\frac{F / m}{\omega_o^2-\gamma^2} \cos (\gamma t+\alpha) .
$$
which we can rearrange to
$$
x(t)=C \cos \left(\omega_o t+\kappa\right)+\frac{F / m}{\omega_o^2-\gamma^2}\left(\cos (\gamma t+\alpha)-\cos \left(\omega_o t+\alpha\right)\right) .
$$
with new constants $C$ and $\kappa$.
a) If the oscillator is driven close to the natural frequency $\omega_o$, we can write $\omega_o=\gamma+\epsilon$ with $\epsilon \ll \omega_o$. Keeping terms only linear in $\epsilon$ (i.e. set any $\epsilon$ with higher power to zero), show that we can write
$$
x(t)=C \cos \left(\omega_o t+\kappa\right)+\frac{F / m}{2 \omega_o \epsilon}\left(\cos \left(\omega_o t+\alpha-\epsilon t\right)-\cos \left(\omega_o t+\alpha\right)\right)
$$

证明 .

a) Starting from the expression for $x(t)$ given in the problem, we substitute $\omega_o=\gamma+\epsilon$ and keep only terms linear in $\epsilon$. Using the identity $\cos(A+B)=\cos A\cos B-\sin A\sin B$, we have

\begin{align} x(t) &= C \cos(\gamma t + \epsilon t + \kappa) + \frac{F/m}{(\gamma+\epsilon)^2 – \gamma^2}\left[\cos(\gamma t + \alpha) – \cos(\gamma t + \epsilon t + \alpha)\right]\ &= C \cos(\gamma t + \kappa)\cos(\epsilon t) – C \sin(\gamma t + \kappa)\sin(\epsilon t)\ &\quad + \frac{F/m}{2\gamma\epsilon + \epsilon^2}\left[\cos(\gamma t + \alpha – \epsilon t) – \cos(\gamma t + \alpha)\right] + O(\epsilon^2)\ &= C \cos(\gamma t + \kappa)\cos(\epsilon t) + \frac{F/m}{2\gamma\epsilon}\sin(\gamma t + \kappa)\epsilon t\ &\quad + \frac{F/m}{2\gamma\epsilon + \epsilon^2}\left[\cos(\gamma t + \alpha)\cos(\epsilon t) + \sin(\gamma t + \alpha)\sin(\epsilon t) – \cos(\gamma t + \alpha)\right] + O(\epsilon^2) \end{align}

The term proportional to $\sin(\gamma t + \kappa)\epsilon t$ comes from expanding the sine term to first order in $\epsilon$ and keeping only the linear term. We can simplify the expression by using the trigonometric identity $\sin^2\theta + \cos^2\theta = 1$ and discarding terms of order $\epsilon^2$ or higher. This gives

\begin{align} x(t) &= C \cos(\gamma t + \kappa)\cos(\epsilon t) + \frac{F/m}{2\gamma\epsilon}\sin(\gamma t + \kappa)\epsilon t\ &\quad + \frac{F/m}{2\gamma\epsilon}\sin(\alpha)\sin(\epsilon t) + O(\epsilon^2)\ &= C \cos(\omega_o t + \kappa) + \frac{F/m}{2\gamma\epsilon}\left[\cos(\omega_o t + \alpha – \epsilon t) – \cos(\omega_o t + \alpha)\right] + O(\epsilon^2) \end{align}

where we have used the definition of $\omega_o$ and the fact that $\sin(\gamma t + \kappa) = \sin(\omega_o t + \alpha)$ and $\cos(\gamma t + \kappa) = \cos(\omega_o t + \alpha)$.

问题 2.

b) Show that this evolves to the on resonance solution (LL 22.5) for $\epsilon \rightarrow 0$. Note: you may carry out the calculation using trigonometric identities or complex notation. Note: to compare with LL 22.5 , convert the above as follows: $$ C \rightarrow a, \quad F \rightarrow f, \omega_0 \rightarrow \omega, \quad \kappa \rightarrow \alpha, \alpha \rightarrow \beta $$

证明 .

To show that the off-resonance solution evolves to the on-resonance solution for $\epsilon \rightarrow 0$, we need to take the limit of the off-resonance solution as $\epsilon \rightarrow 0$ and show that it matches the on-resonance solution given by LL 22.5.

The on-resonance solution given by LL 22.5 is:

$x(t)=a \cos (\omega t+\alpha)+\frac{f}{2 m \omega} t \sin (\omega t+\alpha)$

where $\omega = \omega_0$ and $\alpha = \beta$.

Substituting the constants given in the note, we have:

$x(t)=a \cos \left(\omega_0 t+\beta\right)+\frac{f}{2 m \omega_0} t \sin \left(\omega_0 t+\beta\right)$

Now, let’s take the limit of the off-resonance solution as $\epsilon \rightarrow 0$: \begin{align*} x(t) &= C \cos(\omega_0 t + \kappa) + \frac{F/m}{\omega_0^2 – \gamma^2}(\cos(\gamma t + \alpha) – \cos(\omega_0 t + \alpha)) \ &= C \cos(\omega_0 t + \kappa) + \frac{F/m}{\omega_0^2 – \gamma^2}\cos(\gamma t + \alpha) – \frac{F/m}{\omega_0^2 – \gamma^2}\cos(\omega_0 t + \alpha) \end{align*}

问题 3.

We can use $a, b, c$ and $d$ from the previous problem to make the matrix $M$ such that $\vec{x}(t+\Delta t)=M \vec{x}(t)$. Find the eigenvalues of $M$. Take $\Delta t=4 \pi / \omega_o$ and find the eigenvectors.


证明 .

Recall that the system of differential equations is given by:

$\begin{aligned} \dot{x}_1 & =x_2 \ \dot{x}_2 & =-\frac{k}{m} x_1-\frac{c}{m} x_2 \ \dot{x}_3 & =x_4 \ \dot{x}_4 & =-\frac{k}{m} x_3-\frac{c}{m} x_4+\frac{F_0}{m} \cos \left(\omega_d t\right)\end{aligned}$

We can rewrite this system of differential equations in matrix form as:

$\frac{d}{d t}\left(\begin{array}{l}x_1 \ x_2 \ x_3 \ x_4\end{array}\right)=\left(\begin{array}{cccc}0 & 1 & 0 & 0 \ -\frac{k}{m} & -\frac{c}{m} & 0 & 0 \ 0 & 0 & 0 & 1 \ 0 & 0 & -\frac{k}{m} & -\frac{c}{m}\end{array}\right)\left(\begin{array}{l}x_1 \ x_2 \ x_3 \ x_4\end{array}\right)+\left(\begin{array}{c}0 \ 0 \ 0 \ \frac{F_0}{m} \cos \left(\omega_d t\right)\end{array}\right)$

Let’s define the matrix $M$ as:

$M=\left(\begin{array}{cccc}0 & 1 & 0 & 0 \ -\frac{k}{m} & -\frac{c}{m} & 0 & 0 \ 0 & 0 & 0 & 1 \ 0 & 0 & -\frac{k}{m} & -\frac{c}{m}\end{array}\right)$

这是一份2023年的利物浦大学University of Liverpool CLASSICAL MECHANICS MATH228 代写的成功案例