Stochastic Processes and Paths

Definition 1: Let $(\Omega,\mathscr{F},\mathbb{P})$ be a probability space, and let $(X_t)_{t\in T}$ be a family of random variables on $(\Omega,\mathscr{F},\mathbb{P})$, that is, for every $t\in T$, we have
$$
X_t: (\Omega,\mathscr{F})\to (\mathbb{R},\mathcal{B}(\mathbb{R}))
$$
measurable. Then
$$
\begin{aligned}
X:T\times\Omega&\to \mathbb{R}\\\
(t,\omega)&\mapsto X_t(\omega)
\end{aligned}
$$
is called a stochastic process. For fixed $\omega\in \Omega$, we call
$$
\begin{aligned}
X_\omega:T&\to \mathbb{R}\\\
t&\mapsto X(t,\omega)
\end{aligned}
$$
a path, and call the collection of all paths
$$
\mathbb{D} := \{X_\omega\ ;\ \omega\in \Omega\}
$$
the path space.

Remark: Each element $X_\omega$ of the path space is a mapping from $T$ to $\mathbb{R}$.

The KKT conditions (Karush–Kuhn–Tucker Conditions) are one of the most important results in optimization, and readers familiar with optimization will certainly not find them unfamiliar. This article will focus on introducing the motivation behind some key concepts introduced in the proof of the KKT conditions (such as the introduction of the active set and the constraint qualification), and will review the complete proof of the KKT conditions.

In recent years, many fields have involved the problem of solving large-scale sparse systems of equations. When the problem size is extremely large, solving them becomes very difficult. Fortunately, Krylov subspace methods can handle such problems very well, and thus they have also been rated as one of the ten greatest algorithms of the 20th century. In this article, we will introduce the basic idea of the Krylov subspace and the famous GMRES algorithm (Generalized Minimal Residual Method).

The Weierstrass Approximation Theorem tells us that any continuous function $f$ on a closed interval can be uniformly approximated by polynomial functions. In 1912, Bernstein, based on probability theory, constructed Bernstein polynomials and proved the Weierstrass Approximation Theorem. This paper will introduce the essence of how Bernstein polynomials can approximate continuous functions from both the probabilistic and pure analytic perspectives. First, we present the Weierstrass Approximation Theorem:

Weierstrass Approximation Theorem

Theorem 1 (Weierstrass Approximation Theorem): Let $f$ be a continuous function on the closed interval $[a,b]$. For any $\varepsilon > 0$, there exists a polynomial $p$ such that

$$
\max_{x \in [a,b]} |p(x) - f(x)| < \varepsilon
$$

Proposition 1. Let $E=F(u)$, where $u$ is transcendental over $F$. If $K\neq F$ and $K$ is an intermediate field of $E/F$, then $u$ is algebraic over $K$.

Theorem 1. Let $E/F$ be a field extension, and let $a$ be algebraic over $F$. Then
$$
[F(a):F]=\deg f(x),
$$
where $f(x)$ is the minimal polynomial of $a$ over $F$.

Proposition 1. The only automorphism of the rational field $\mathbb{Q}$ is the identity automorphism.

Definition. Let $R$ be a commutative ring with identity. If $I$ and $J$ are two ideals of $R$ such that
$$
I+J=R,
$$
then $I$ and $J$ are said to be coprime.

Proposition 1. Let $I$ and $J$ be ideals of a ring $R$. Then:

1. $IJ \subseteq I$ and $IJ \subseteq J$.

2. $IJ \subseteq I \cap J \subseteq I+J$.

First, let us review the notion of an ideal generated by an element.