Let \(\mathbb{K}\in \{\mathbb{R}, \mathbb{C}\}\) and \(s\le N\). A vector \(x\in \mathbb{K}^N\) is \(s\)-sparse if
\[ \lvert \supp x\rvert \le s, \]where \(\supp x\) denotes the support of \(x\) .
The subset of all \(s\)-sparse vectors is denoted by \(\mathbb{K}^N_s\).
Remarks
- \(\mathbb{K}_s^N\) is not a subspace .
See also Link to heading
- best s-term approximation
- [compressive sensing problem](system_of_linear_equations.md#Compressive sensing case)