Krylov Subspace and GMRES Algorithm

Krylov Subspace and GMRES Algorithm

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).

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Bernstein Polynomial and Weierstrass Approximation Theorem

Bernstein Polynomial and Weierstrass Approximation Theorem

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

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

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Algebraic Elements and Transcendental Elements
Degrees of Field Extensions
Automorphisms of the Rational Field and the Real Field
The Chinese Remainder Theorem
Operations on Ideals
Rings Without Nontrivial One-Sided Ideals
On the Invertibility of $1-ab$ in a Unital Ring
On the Problem of Elements in a Ring Having More than One One-Sided Inverse