Krylov subspace
In linear algebra, the order-r Krylov subspace generated by an n-by-n matrix A and a vector b of dimension n is the linear subspace spanned by the images of b under the first r powers of A (starting from A 0 = I {\displaystyle A^{0}=I} ), that is, K r ( A , b ) = span { b , A b , A 2 b , … , A r − 1 b } . {\displaystyle {\mathcal {K}}_{r}(A,b)=\operatorname {span} \,\{b,Ab,A^{2}b,\ldots ,A^{r-1}b\}.}
Nº Q1757151 ★
Common · Knowledge
Krylov subspace
In linear algebra, the order-r Krylov subspace generated by an n-by-n matrix A and a vector b of dimension n is the linear subspace spanned by the images of b under the first r powers of A (starting from A 0 = I {\displaystyle A^{0}=I} ), that is, K r ( A , b ) = span { b , A b , A 2 b , … , A r − 1 b } . {\displaystyle {\mathcal {K}}_{r}(A,b)=\operatorname {span} \,\{b,Ab,A^{2}b,\ldots ,A^{r-1}b\}.}
Last price
—
Floor price
—
7-day median
—
30-day sales
0
30-day range
—
In circulation
0
Price history
median
low – high
sales
No sales in this period
Show table
| Date | median | Low | High | sales |
|---|
Sales history
- Last sale
- —
- 30-day average
- —
- 30-day low
- —
- 30-day high
- —
- Sales 7d
- 0
- Sales 30d
- 0
No sales yet.
Anonymous sales: no buyer or seller shown. Figures count player-to-player sales only.
From Wikipedia
In linear algebra, the order-r Krylov subspace generated by an n-by-n matrix A and a vector b of dimension n is the linear subspace spanned by the images of b under the first r powers of A (starting from A 0 = I {\displaystyle A^{0}=I} ), that is, K r ( A , b ) = span { b , A b , A 2 b , … , A r − 1 b } . {\displaystyle {\mathcal {K}}_{r}(A,b)=\operatorname {span} \,\{b,Ab,A^{2}b,\ldots ,A^{r-1}b\}.}
Text: Wikipédia, CC BY-SA 4.0. ·
Related cards
Norm (mathematics)
Length in a vector space
Nº Q956437 ★★★
Transformation matrix
Central object in linear algebra; mapping vectors to vectors
Nº Q1482183 ★★★
Linear algebra
Branch of mathematics that studies vector spaces and linear transformations
Nº Q82571 ★★★★
Gauss–Markov theorem
Statistics theorem that ordinary least squares is the best linear unbiased estimator under certain conditions
Nº Q428134 ★
Schubert calculus
Branch of algebraic geometry
Nº Q7432936 ★
Vandermonde matrix
Mathematical concept
Nº Q579544 ★★