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Math4501 Lecture 1

In many practical problems (ODEs (ordinary differential equations), PdEs (partial differential equations), System of equations)

closed-form analytical solutions are unknown.

-> resort ot computational algorithms (approximation)

For example,

Deep learning classifiers

Root finding

f(x)=i=1naixif(x)=\sum_{i=1}^n a_i x^i

for n5n\geq 5.

find all roots xRx\in \mathbb{R} of f(x)=0f(x)=0.

Investment

Invest a dollars every month return with the rate rr.

g(r)=ai=1n(1+r)i=a[(1+r)n+1(1+r)r]g(r)=a\sum_{i=1}^n (1+r)^i=a\left[\frac{(1+r)^{n+1}-(1+r)}{r}\right]

Say want g(r)=bg(r)=b for some bb.

f(r)=a(1+n)n+1a(1+n)br=0f(r)=a(1+n)^{n+1}-a(1+n)-br=0

use Newton’s method to find rr such that f(r)=0f(r)=0.

Since ff is non-linear, that is f(x+y)f(x)+f(y)f(x+y)\neq f(x)+f(y).

Let

f1(x1,,xm)=0fm(x1,,xm)=0f_1(x_1,\dots, x_m)=0\\ \vdots\\ f_m(x_1,\dots, x_m)=0

be a system of mm equations fRmRm\vec{f} \mathbb{R}^m \to \mathbb{R}^m. and f1(x)=0f_1(\vec{x})=\vec{0}.

If f\vec{f} is linear, note that

f(x)=f([x1xm])=f(x1[100]+x2[010]++xm[001])=x1f([100])+x2f([010])++xmf([001])=Ax\begin{aligned} \vec{f}(\vec{x})&=\vec{f}(\begin{bmatrix}x_1\\ \vdots\\ x_m\end{bmatrix})\\ &=\vec{f}(x_1\begin{bmatrix}1\\ 0\\ \vdots\\ 0\end{bmatrix}+x_2\begin{bmatrix}0\\ 1\\ \vdots\\ 0\end{bmatrix}+\cdots+x_m\begin{bmatrix}0\\ 0\\ \vdots\\ 1\end{bmatrix})\\ &=x_1\vec{f}(\begin{bmatrix}1\\ 0\\ \vdots\\ 0\end{bmatrix})+x_2\vec{f}(\begin{bmatrix}0\\ 1\\ \vdots\\ 0\end{bmatrix})+\cdots+x_m\vec{f}(\begin{bmatrix}0\\ 0\\ \vdots\\ 1\end{bmatrix})\\ &=A\vec{x} \end{aligned}

where ei\vec{e}_i is the ii-th standard basis vector.

Gaussian elimination (LU factorization)

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