According to Theorem 1, if we take k as the (l_{0} -norm of the unique solution of (l_{0} -normmizatiof, then we can get the main contribuniqueasolutions the inequality (h^{ast}(p,A,k)^{frac{1}{p}}<1) is satisfied.
According to Theorem 2, we can get the equivalence between (l_{2,0} -minimization and (l_{2,0} -minimization andsoon as (M(del_{2,p} -minimizatione (M(del_{2,p} -minimizationed in Theorem 2.
Recalling (15), we can get the equivalence between (l_{0} -minimization and (l_{p})-minimization as long as (h^{ast }(p,A,k)^{frac{1}{p}}<1).
In Section 3, we focus on giving an analytic expression of an upper bound of (h p,A,r,k)), and we can get the equivalence relationship between (l_{2,p} -minimization and (l_{2,p} -minimization andl_{2,0} -minimization) is satisfied.
By the well-known theory of convex analysis [21], we can get the relationship between the minimization problem and the corresponding variational inequality in the following theorem.
As shown in the Figure 1, we can get the solution of (l_{p} -minimization in different cases where (p=0.2861,0.2,0.15text{, and }0.1).
Combining Propositions 1 and 2, we can get the first main theorem which shows us the equivalence relationship between (l_{2,0} -minimization and (l_{2,0} -minimization. Figure 1 M-NSC in Exandl_{2,p} -minimization
In this section, we will adopt Algorithm 1 designed for (l_{2,1} -minimization, and we wil_{2,1} -minimization024) meandrement A weth (delta_{100}=0.8646), and willadoptt (p^(a)=0.1507) by our result.
