几何学|MATH3033 Geometry代写 UWA代写

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这是一份uwa西澳大学MATH3033的成功案例

几何学|MATH3033 Geometry代写 UWA代写

Concretely parametrize the sphere in the usual way:
$$
x_{1}=\sin \theta \sin \phi, \quad x_{2}=\sin \theta \cos \phi, \quad x_{3}=\cos \theta
$$
then with the poles removed the range of values is $0<\theta<\pi, 0 \leq \phi<2 \pi$. The antipodal map is
$$
\theta \mapsto \pi-\theta, \quad \phi \mapsto \phi+\pi .
$$
We can therefore identify the space of lines in $\mathbf{R}^{2}$ as the pairs
$$
(\theta, \phi) \in(0, \pi) \times[0, \pi]
$$

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MATH3033 COURSE NOTES :

We can add symmetric bilinear forms: $(B+C)(v, w)=B(v, w)+C(v, w)$ and multiply by a scalar $(\lambda B)(v, w)=\lambda B(v, w)$ so they form a vector space isomorphic to the space of symmetric $n \times n$ matrices which has dimension $n(n+1) / 2$. If we take a different basis
$$
w_{i}=\sum_{j} P_{j i} v_{j}
$$
then
$$
B\left(w_{i}, w_{j}\right)=B\left(\sum_{k} P_{k i} v_{k}, \sum_{\ell} P_{\ell j} v_{\ell}\right)=\sum_{k, \ell} P_{k i} B\left(v_{k}, v_{\ell}\right) P_{\ell j}
$$
so that the matrix $\beta_{i j}=B\left(v_{i}, v_{j}\right)$ changes under a change of basis to
$$
\beta^{\prime}=P^{T} \beta P .
$$












拓扑结构和分析|MATH3032 Topology and Analysis代写 UWA代写

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这是一份uwa西澳大学MATH3032的成功案例

拓扑结构和分析|MATH3032 Topology and Analysis代写 UWA代写

By Fubini’s theorem, it follows that for $k \neq j$ that
$$
\int_{\Omega_{n}}\left(X_{k}-x\right)\left(X_{j}-x\right) d \mu_{z}^{n}=\left[\int_{{0,1}}(\omega-x) d \mu_{z}(\omega)\right]^{2}=0
$$
and
$$
\int_{\Omega_{n}}\left(X_{k}-x\right)^{2} d \mu_{z}^{n}=\int_{{0,1}}(\omega-x)^{2} d \mu_{z}(\omega)=(1-x)^{2} x+x^{2}(1-x) \leq 2
$$
Combining the last three displayed equations shows that
$$
\mu_{z}^{n}\left(\left|S_{n}-x\right|>\epsilon\right) \leq \frac{1}{n^{2} \epsilon^{2}} 2 n=\frac{2}{n \epsilon^{2}}
$$
which combined with $\mathrm{Eq}$. (5.4) implies that
$$
\sup {z \in[0,1]}\left|f(x)-p{n}(x)\right| \leq \frac{4 M}{n \epsilon^{2}}+\delta_{c}
$$
and therefore
$$
\lim {n \rightarrow \infty} \sup \sup {z \in[0,1]}\left|f(x)-p_{n}(x)\right| \leq \delta_{c} \rightarrow 0 \text { as } \epsilon \rightarrow 0
$$

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MATH3032 COURSE NOTES :

To prove the claim, let $V_{y}$ be an open neighborhood of $y$ such that $\left|f-g_{z y}\right|<\epsilon$ on $V_{y}$ so in particular $f<\epsilon+g_{z y}$ on $V_{y}$. By compactness, there exists $\Lambda \subset \subset X$ such that $X=\bigcup_{y \in \Lambda} V_{y}$. Set
$$
g_{z}(z)=\max \left{g_{z y}(z): y \in \Lambda\right},
$$
then for any $y \in \Lambda, f<\epsilon+g_{z y}<\epsilon+g_{z}$ on $V_{y}$ and therefore $f<\epsilon+g_{z}$ on $X$. Moreover, by construction $f(x)=g_{z}(x)$.

We now will finish the proof of the theorem. For each $x \in X$, let $U_{z}$ be a neighborhood of $x$ such that $\left|f-g_{z}\right|<\epsilon$ on $U_{z}$. Choose $\Gamma \subset \subset X$ such that $X=\bigcup_{z \in \Gamma} U_{z}$ and define $$ g=\min \left{g_{z}: x \in \Gamma\right} \in \mathcal{A} . $$ Then $f0$ is arbitrary it follows that $f \in \overline{\mathcal{A}}=\mathcal{A}$.












代数结构和对称性|MATH3031 Algebraic Structures and Symmetry代写 UWA代写

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这是一份uwa西澳大学MATH3031的成功案例

代数结构和对称性|MATH3031 Algebraic Structures and Symmetry代写 UWA代写

Let $A$ be a non-empty subset of a semihypergroup (H, )). We say that $A$ is a complete part of $(H$, $\circ)$ if, for every $n \in \mathbb{N}-{0}$ and $\left(x_{1}, x_{2}, \ldots, x_{n}\right) \in H^{n}$,
$$
\left(x_{1} \circ \cdots \circ x_{n}\right) \cap A \neq \emptyset \Longrightarrow\left(x_{1} \circ \cdots \circ x_{n}\right) \subseteq A .
$$
Clearly, the set $H$ is a complete part, and the intersection $\mathcal{C}(X)$ of all the complete parts containing a non-empty set $X$ is called the complete closure of $X$. If $X$ is a complete part of $(H, 0)$ then $\mathcal{C}(X)=X$. If $(H, 0)$ is a semihypergroup and $\varphi: H \rightarrow H / \beta^{}$ is the canonical projection, then, for every non-empty set $A \subseteq H$, we have $\mathcal{C}(A)=\varphi^{-1}(\varphi(A))$. Moreover, if $(H, 0)$ is a hypergroup, then $$ \mathcal{C}(A)=\varphi^{-1}(\varphi(A))=A \circ \omega H=\omega_{H} \circ A $$ A hypergroup $(H, \circ)$ is said to be complete if $x \circ y=\mathcal{C}(x \circ y)$, for all $(x, y) \in H^{2}$. If $(H, \circ)$ is a complete hypergroup, then $$ x \circ y=\mathcal{C}(a)=\beta^{}(a) .
$$
for every $(x, y) \in H^{2}$ and $a \in x \circ y$

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MATH3031 COURSE NOTES :

  1. The image of $A$ under $f$ is denoted by $f(A)$ or
    $$
    C M_{f(A)}(y)= \begin{cases}\mathrm{V}{f(x)-y} C M{A}(x) & \text { if } f^{-1}(y) \neq \varnothing \ 0 & \text { otherwise. }\end{cases}
    $$
  2. The inverse image of $B$ under $f$ is denoted by $f^{-1}(B)$ where $C M_{f-1}(B)(x)=C M_{B}(f(x))$.












复杂系统|MATH3024 Complex Systems代写 UWA代写

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这是一份uwa西澳大学MATH3024的成功案例

复杂系统|MATH3024 Complex Systems代写 UWA代写

Inserting for the $J_{i j}$ and cosidering $i \in I_{x}$ we get
$$
m_{i}=\tanh \left[\beta \sum_{\boldsymbol{x}^{\prime}} Q\left(\boldsymbol{x}, \boldsymbol{x}^{\prime}\right) p_{\boldsymbol{x}^{\prime}} m_{\boldsymbol{x}^{\prime}}+\beta h_{i}\right]
$$
where we have introduced sub-lattice (equilibrium) magnetizations $m_{x}$ via
$$
m_{x}=\frac{1}{\left|I_{\boldsymbol{x}}\right|} \sum_{i \in I_{\boldsymbol{x}}} m_{i}
$$
Inserting this into (5.13) we get
$$
m_{\boldsymbol{x}}=\frac{1}{\left|I_{\boldsymbol{x}}\right|} \sum_{i \in I_{\boldsymbol{x}}} \tanh \left[\beta \sum_{\boldsymbol{x}^{\prime}} Q\left(\boldsymbol{x}, \boldsymbol{x}^{\prime}\right) p_{\boldsymbol{x}^{\prime}} m_{\boldsymbol{x}^{\prime}}+\beta h_{i}\right]=\left\langle\tanh \left[\beta \sum_{\boldsymbol{x}^{\prime}} Q\left(\boldsymbol{x}, \boldsymbol{x}^{\prime}\right) p_{\boldsymbol{x}^{\prime}} m_{\boldsymbol{x}^{\prime}}+\beta h\right]\right\rangle_{h},
$$

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MATH3024 COURSE NOTES :

Introducing the overlap vector
$$
m=\sum_{\xi} p_{\xi} \xi m_{\xi}=\left\langle\xi m_{\xi}\right\rangle_{\xi}
$$
we see that the fpe’s can be written as
$$
m_{\boldsymbol{\xi}}=\tanh [\beta \boldsymbol{\xi} \cdot \boldsymbol{m}]
$$
which after multiplying by $\boldsymbol{\xi}$ and averaging over $\boldsymbol{\xi}$ gives
$$
\boldsymbol{m}=\langle\boldsymbol{\xi} \tanh [\beta \boldsymbol{\xi} \cdot \boldsymbol{m}]\rangle_{\boldsymbol{\xi}},
$$
or, in components
$$
m_{\mu}=\left\langle\xi^{\mu} \tanh \left[\beta \sum_{\nu} \xi^{\nu} m_{\nu}\right]\right\rangle_{\xi},
$$
Note that $m_{\mu}$ is nothing but the overlap of the equilibrium spin-configuration with the pattern $\xi_{i}^{\mu}$,
$$
m_{\mu}=\frac{1}{N} \sum_{i} \xi_{i}^{\mu}\left\langle S_{i}\right\rangle=\sum_{\xi} p_{\xi} \xi^{\mu} m_{\xi}
$$











科学和工业建模|MATH3022 Scientific and Industrial Modelling代写 UWA代写

0

这是一份uwa西澳大学MATH3022的成功案例

科学和工业建模|MATH3022 Scientific and Industrial Modelling代写 UWA代写

Similarly, there are many matrix norms. In particular, for each vector norm $|\cdot|$, we define a matrix norm by
$$
|A|=\max {\boldsymbol{x} \neq 0} \frac{|\boldsymbol{A} x|}{|x|} . $$ Computing a matrix norm using this definition is a difficult problem, but fortunately there are shortcuts. It can be shown that for an $m \times n$ matrix $\boldsymbol{A}$ $$ \begin{aligned} |\boldsymbol{A}|{1} &=\max {j} \sum{i=1}^{m}\left|a_{i j}\right|, \
|\boldsymbol{A}|_{2} &=\sqrt{\max {j} \lambda{j}\left(\boldsymbol{A}^{} \boldsymbol{A}\right),} \ |\boldsymbol{A}|_{\infty} &=\max {i} \sum{j=1}^{n}\left|a_{i j}\right|,
\end{aligned}
$$
where $\lambda_{j}\left(\boldsymbol{A}^{} \boldsymbol{A}\right)(j=1, \ldots, n)$ denotes the eigenvalues of the conjugate transpose of $\boldsymbol{A}$ times $\boldsymbol{A}$. One other norm, the Frobenius norm, is also useful. It is the 2 -norm of the matrix $\boldsymbol{A}$ after stacking its columns:
$$
|\boldsymbol{A}|_{F}=\sqrt{\sum_{j=1}^{n} \sum_{i=1}^{m}\left|a_{i j}\right|^{2}} .
$$

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MATH3022 COURSE NOTES :

We might want to know how close the solution to this problem is to our original problem. To answer this question, define
$$
\begin{aligned}
\epsilon_{A} & \equiv \frac{|\Delta A|}{|A|}, \
\epsilon_{b} & \equiv \frac{|\Delta b|}{|b|} \
\kappa & \equiv|A|\left|A^{-1}\right|
\end{aligned}
$$
Then if $\kappa \epsilon_{A}<1$ we have the bound
$$
\frac{|x-y|}{|x|} \leq \frac{\kappa}{1-\kappa \epsilon_{A}}\left(\epsilon_{A}+\epsilon_{b}\right)
$$
Consult a standard text such as $[64,119,139]$ for a proof of this and related results, valid for any of the norms $(1,2, \infty)$ we have considered. We already used a special case of this inequality, when $\epsilon_{b}=0$.











非线性动力学和混沌|MATH3021 Nonlinear Dynamics and Chaos代写 UWA代写

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这是一份uwa西澳大学MATH3021的成功案例

非线性动力学和混沌|MATH3021 Nonlinear Dynamics and Chaos代写 UWA代写

$$
\begin{aligned}
p\left(\mathbf{r} \mid \mathbf{s}{\mathbf{i}}\right) &=\prod{k=0}^{(N-1)} p\left(r_{k} \mid s_{i, k}\right) \
k &=0,1, \ldots,(N-1)
\end{aligned}
$$
leading to
$$
\begin{aligned}
p\left(\mathbf{r} \mid \mathbf{s}{\mathbf{i}}\right) &=\frac{1}{\sqrt{\pi N{0}}} \exp \left[-\frac{\sum_{k=0}^{(N-1)}\left(r_{k}-s_{i, k}\right)^{2}}{N_{0}}\right] \
i &=0,1, \ldots,(N-1)
\end{aligned}
$$
The term in the exponential is the distance between the received vector $\mathbf{r}$ and $\mathbf{s}{\mathrm{i}}$ where $$ \mathbf{r}=\left[r{0}, r_{1}, r_{2}, \ldots, r_{N-1}\right]
$$
and
$$
\mathbf{s}{\mathbf{i}}=\left[s{i, 0} s_{i, 1}, s_{i, 2}, \ldots s_{i,(N-1)}\right]
$$
where, for example,
$$
s_{3}=\left[0_{3,0} 0_{3,1}, s_{3,2}, 1_{3,3}, \ldots, 0_{3,(N-1)}\right]
$$

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MATH3021 COURSE NOTES :

$$
g_{n+1}-g_{n}=q_{n}=1-\left(\frac{1}{2}+n\right) \ln \left(1+\frac{1}{n}\right)=-\frac{1}{12 n^{2}}+\frac{1}{12 n^{3}}+\ldots
$$
First note that $\mathcal{L}^{-1} q=p^{-2} \mathcal{L}^{-1} q^{\prime \prime}$ which can be easily evaluated by residues since
$$
q^{\prime \prime}=\frac{1}{n}-\frac{1}{n+1}-\frac{1}{2}\left(\frac{1}{(n+1)^{2}}+\frac{1}{n^{2}}\right)
$$
Thus, with $\mathcal{L}^{-1} g_{n}:=G$ we get
$$
\begin{aligned}
&\left(e^{-p}-1\right) G(p)=\frac{1-\frac{p}{2}-\left(\frac{p}{2}+1\right) e^{-p}}{p^{2}} \
&g_{n}=\int_{0}^{\infty} \frac{1-\frac{p}{2}-\left(\frac{p}{2}+1\right) e^{-p}}{p^{2}\left(e^{-p}-1\right)} e^{-n p} d p
\end{aligned}
$$











网络科技|MATH3002 Network Science代写 UWA代写

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这是一份uwa西澳大学MATH3002的成功案例

网络科技|MATH3002 Network Science代写 UWA代写

If $A$ is the adjacency matrix of a graph $G, \alpha>0$, and $\lambda_{1}$ the largest eigenvalue of $A$, then
$$
\lambda_{1}<\frac{1}{\alpha} \Longleftrightarrow \sum_{k=1}^{\infty} \alpha^{k} A^{k} \text { converges. }
$$
For the proof see, e.g., [208].
Assuming convergence we find a closed form expression for the status index of Katz:
$$
c_{K}=\sum_{k=1}^{\infty} \alpha^{k}\left(A^{T}\right)^{k} \mathbf{1}{n}=\left(\left(I-\alpha A^{T}\right)^{-1}\right) \mathbf{1}{n}
$$
or, in another form
$$
\left(I-\alpha A^{T}\right) \boldsymbol{c}{K}=\mathbf{1}{n}
$$
an inhomogeneous system of linear equations emphasizing the feedback nature of the centrality: the value of $\boldsymbol{c}{K}(i)$ depends on the other centrality values $c{K}(j)$, $j \neq i$.

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MATH3002 COURSE NOTES :

Taking then $y(x)=e^{\frac{2}{3} x^{3 / 2}} h\left(\frac{2}{3} x^{3 / 2}\right)$ we get
$$
h^{\prime \prime}+\left(2+\frac{1}{3 s}\right) h^{\prime}+\frac{1}{3 s} h=0
$$
and with $H=\mathcal{L}^{-1}(h)$ we get
$$
p(p-2) H^{\prime}=\frac{5}{3}(1-p) H
$$
The solution is
$$
H=C p^{-5 / 6}(2-p)^{-5 / 6}
$$
and it can be easily checked that any integral of the form
$$
h=\int_{0}^{\infty e^{i \phi}} e^{-p s} H(p) d p
$$
for $\phi \neq 0$ and $\operatorname{Re}(p s)>0$, is a solution of yielding the expression











概率论基础与应用|STAT2062 Fundamentals of Probability with Applications代写 UWA代写

0

这是一份uwa西澳大学STAT2062的成功案例

概率论基础与应用|STAT2062 Fundamentals of Probability with Applications代写 UWA代写

$$
w_{t}=w_{x}^{2}
$$
which can be solved by characteristics. We take $w_{x}=u$ and get for $u$ the quasilinear equation
$$
u_{t}=2 u u_{x}
$$
with a particular solution $u=-x /(2 t)$, giving $w=-x^{2} /(4 t)$. We thus take $w=-x^{2} /(4 t)+\delta$ and get for $\delta$ the equation
$$
\delta_{t}+\frac{x}{t} \delta_{x}+\frac{1}{2 t}=\delta_{x}^{2}+\delta_{x x}
$$
where we have separated the relatively small terms to the right side. We would normally solve the leading equation and continue the process, but for this equation we note that $\delta=-\frac{1}{2} \ln t$ solves not only the leading equation, but the full equation. Thus
$$
w=-\frac{x^{2}}{4 t}-\frac{1}{2} \ln t
$$
which gives the classical heat kernel
$$
\psi=\frac{1}{\sqrt{t}} e^{-\frac{x^{2}}{4 t}}
$$
This exact solvability is of course rather accidental, but a perturbation approach formally works in a more PDE general context.

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STAT2062 COURSE NOTES :

$\int 3 y_{0}^{2}+w_{2}:=\phi_{1}+w_{2}\left(\phi_{1}\right.$ is a formal power series $)$. To leading order, we obtain
$$
w_{2}^{\prime}=3 y_{0} e^{-x}
$$
and thus $w_{2}=\phi_{2} e^{-x}$ where $\phi_{2}$ is a power series. Continuing this process of iteration, we can see inductively that $w$ must be of the form
$$
w=-x+\sum_{k=0}^{\infty} \phi_{k} e^{-k x}
$$
where $\phi_{k}$ are formal power series, which means
$$
y=\sum_{k=0}^{\infty} e^{-k x} y_{k}
$$











纯粹数学入门 MATH2031代 Introduction to Pure Mathematics代写 UWA代写

0

这是一份uwa西澳大学MATH2031的成功案例

纯粹数学入门 MATH2031代 Introduction to Pure Mathematics代写 UWA代写

the only consistent balance ${ }^{8}$ is between $-\epsilon^{2} w^{\prime 2}$ and $V(x)-E$ with $\epsilon^{2} w^{\prime \prime}$ much smaller than both. For that to happen we need
$$
\epsilon^{2} U^{-1} h^{\prime} \ll 1 \text { where } h=w^{\prime}
$$
We place the term $\epsilon^{2} h^{\prime}$ on the right side of the equation and set up the iteration scheme
$$
h_{n}^{2}=\epsilon^{-2} U-h_{n-1}^{\prime} ; \quad h_{-1}=0
$$
or
$$
h_{n}=\pm \frac{\sqrt{U}}{\epsilon} \sqrt{1-\frac{\epsilon^{2} h_{n-1}^{\prime}}{U}} ; \quad h_{-1}=0
$$
Under the square root can be Taylor expanded around $1 .$
$$
h_{n}=\pm \frac{\sqrt{U}}{\epsilon}\left(1-\frac{1}{2} \epsilon^{2} \frac{h_{n-1}^{\prime}}{U}-\frac{1}{8} \epsilon^{4}\left(\frac{h_{n-1}^{\prime}}{U}\right)^{2}+\cdots\right)
$$

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MATH2031 COURSE NOTES :

i.e., $a>(\epsilon /|\alpha|)^{2 / 3}$ and for small enough $\epsilon$ this is also enough to ensure preservation of $\mathcal{B}$. We thus find that the equation $\delta=J(\delta)$ has a unique solution and that, furthermore, $|\delta| \leq$ const.e. Using this information and which implies
$$
|J(\delta)| \leq \frac{\epsilon A}{B^{2}} 2 A B^{-1} \epsilon
$$
we easily get that, for some constants $C_{i}>0$ independent on $\epsilon$,
$$
\left|\delta-\delta_{0}\right| \leq C_{1} \epsilon|\delta| \leq C_{1} \epsilon\left|\delta_{0}\right|+C_{1} \epsilon\left|\delta-\delta_{0}\right|
$$
and thus
$$
\left|\delta-\delta_{0}\right| \leq C_{2} \epsilon\left|\delta_{0}\right|
$$
and thus, applying again Laplace’s method we get
$$
\delta \sim \frac{-\epsilon F^{\prime}}{2 F}
$$











概率和随机过程 Probability and Stochastic Processes STAT4061

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这是一份uwa西澳大学STAT4061的成功案例

概率和随机过程 Probability and Stochastic Processes STAT4061


Let $Z$ have the standard normal distribution, Let $V$ be 0 or 1 with probabilities $1-p, p$ independent of $Z$. Then
$$
\varepsilon=\left[\sigma_{1}(1-V)+\sigma_{2} V\right] Z= \begin{cases}\sigma_{1} Z, & \text { if } \quad V=0 \ \sigma_{2} Z, & \text { if } \quad V=1\end{cases}
$$
has the “contaminated normal distribution,” with c.d.f.
$$
F(x)=(1-p) \Phi\left(x / \sigma_{1}\right)+p \Phi\left(x / \sigma_{2}\right)
$$
$F$ has mean $E(\varepsilon)=0$, variance $\operatorname{Var}(\varepsilon)=(1-p) \sigma_{1}^{2}+p \sigma_{2}^{2}$. The density of $E$ is plotted.

Now consider simple linear regression $Y_{i}=\beta_{0}+\beta_{i} x_{i}+\varepsilon_{i}$ for $i=1, \ldots, n$, where the $\varepsilon_{i}$ ‘s are a random sample from $F$ above. Take $n=10, x_{i}=i$ for $i=1, \ldots, 9$ and $x_{10}=30$. Take $\sigma_{1}=2, \sigma_{2}=20, p=0.2$.

Thus, $d_{10,10}$ as defined in Eicher’s Theorem is $\left(\frac{1}{10}\right)+\frac{(30-7.5)^{2}}{622.5}=0.913$,

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STAT4061 COURSE NOTES :

$$
e_{i}=e_{i}\left(b_{0}, b_{1}\right)=y_{i}-\left(b_{0}+b_{1} x_{i}\right) \quad \text { and } \quad Q\left(b_{0}, b_{1}\right)=\sum \rho\left(e_{i}\left(b_{0}, b_{1}\right)\right)
$$
Let
$$
Q^{0}\left(b_{0}, b_{1}\right)=\frac{\partial}{\partial b_{0}} Q\left(b_{0}, b_{1}\right)=\sum \psi\left(e_{i}\right)
$$
and
$$
Q^{1}\left(b_{0}, b_{1}\right)=\frac{\partial}{\partial b_{1}} Q\left(b_{0}, b_{1}\right)=\sum \psi\left(e_{i}\right) x_{i}
$$