! TeX 容量超出，抱歉 [save size=4000] 错误

Question

save_size 的界限是什么？我使用了 Bakoma tex，我只能将 TEXMF.INI 中的 save_size 更改为最大值 7000，如果超过该值，程序就会崩溃。

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%===========================================================
\begin{document}





\begin{exercise}[Perceptron Implementation (5 pts)]
\blue{\textbf{Convention:} All algebraic operations, when applied to a vector
or matrix, are understood to be element-wise (unless otherwise stated).}

\begin{algorithm}[H]
\DontPrintSemicolon
        \KwIn{$X\in\RR^{n\times d}$, $\yv\in \{-1,1\}^n$, $\wv=\zero_d$,
$b=0$, $\mathsf{max\_pass} \in \mathds{N}$}

        \KwOut{$\wv, b, mistake$}

        \For{$t=1, 2, \ldots, \mathsf{max\_pass}$ }{
                $mistake(t) \gets 0$

                \For{$i=1, 2, \ldots, n$}{
                        \If{$y_i (\inner{\xv_i}{\wv}+b) \leq 0$}{
                                $\wv \gets \wv + y_i\xv_i$ \tcp*{$\xv_i$
is the $i$-th row of $X$}

                                $b \gets b + y_i$

                                $mistake(t) \gets mistake(t) + 1$
                        }       
                }
        }
        \caption{The perceptron algorithm.}
        \label{alg:perceptron}
\end{algorithm}

Implement the perceptron in \Cref{alg:perceptron}. Your implementation should
take input as $X = [\xv_1^\top, \ldots, \xv_n^\top]^\top \in \RR^{n \times
d}$, $\yv \in \{-1,1\}^{n}$, an initialization of the hyperplane parameters
$\wv\in\RR^{d}$ and $b\in \RR$, and the maximum number of passes of the training
set [suggested $\mathsf{max\_pass} = 500$]. Run your perceptron algorithm
on the \href{https://archive.ics.uci.edu/ml/datasets/spambase}{\magenta{\textsf{spambase}}}
dataset (available on \href{https://cs.uwaterloo.ca/~y328yu/mycourses/480/assignment.html}{\magenta{course
website}}), and plot the number of mistakes ($y$-axis) \wrt the number of
passes ($x$-axis).

\ans{   }

\end{exercise}

\begin{exercise}[Linear Regression (5 pts)]
Recall that ridge regression refers to the following $\ell_2$ norm regularized
linear regression problem:
\begin{align}
\label{eq:rr}
\min_{\wv} ~ \tfrac12\|X\wv - \yv\|_2^2 + \lambda \|\wv\|_2^2,
\end{align}
where $X \in \RR^{n\times d}, \yv \in \RR^n$ and $\wv \in \RR^d$. Obviously,
setting $\lambda = 0$ we recover ordinary linear regression. 
\begin{enumerate}
\item (3 pts) Prove that ridge regression with any $\lambda > 0$ is equivalent
to ordinary linear regression after performing the following data augmentation:
\begin{align}
\label{eq:da1}
X &\gets \begin{bmatrix} X \\ \sqrt{2\lambda} I_{d \times d} \end{bmatrix}
\\
\label{eq:da2}
\yv &\gets \begin{bmatrix} \yv \\ 0_{d} \end{bmatrix},
\end{align}
where $I_{d\times d}$ is the $d\times d$ identity matrix and $0_d$ is the
$d$-dimensional zero vector.

\ans{}

\item (2 pts) Explain the data augmentation step \eqref{eq:da1}-\eqref{eq:da2}.
[E.g., what kind of data are we adding to the training set? how many of them?
their dimension? what effect are they bringing to the weight vector $\wv$?]

\ans{}
\end{enumerate}
\end{exercise}


\end{document}