Saman S. answered 7d
My name is smn I wiil try my best for guiding u best
A basis for a vector space is a set of vectors that is linearly independent and spans .
1) Basis for the span of given vectors
Goal: From vectors , pick a maximal independent subset.
Method (RREF):
1. Form a matrix with the vectors as columns.
2. Row-reduce to RREF.
3. The pivot columns in the RREF tell you which original columns form a basis for .
Tiny example:
.
Notice , so a basis for the span is .
2) Basis of column space / row space / null space of a matrix
Given :
Column space basis: columns of corresponding to pivot columns (found via RREF of ).
Row space basis: pivot rows of the RREF (or take the nonzero rows of RREF).
Null space basis: solve ; express free variables as parameters to get special solutions. Those special solution vectors form a basis of .
3) Orthonormal basis from a spanning set (Gram–Schmidt)
Given independent :
For : , then .
The form an orthonormal basis for .
“Basis of a sequence space”
If you literally mean sequences (like , , or the space of eventually-zero sequences):
The standard (canonical) basis is .
Every sequence can be written as (convergence in the space’s norm as appropriate).
Quick checklist (to “find a basis” fast)
Put vectors as columns → RREF → pick original pivot columns.
Need orthonormal? Run Gram–Schmidt on the indep .