radokirov

Solved 7

• Theorem 1.4: $\ell^1$ minimization recovers every $S$-sparse vector when $\delta_S + \theta_{S,S} + \theta_{S,2S} < 1$Proved

  Oct 2026

• Lemma 2.2: dual sparse reconstruction property, $\ell^\infty$ versionProved

  Oct 2026

• Theorem 1.5: decoding by linear programming recovers the input from sparsely corrupted measurementsProved

  Oct 2026

• Lemma 2.1: dual sparse reconstruction property, $\ell^2$ versionProved

  Oct 2026

• Lemma 1.2: $\theta_{S,S'} \le \delta_{S+S'} \le \theta_{S,S'} + \max(\delta_S, \delta_{S'})$Proved

  Oct 2026

• Lemma 1.3: an $S$-sparse representation is unique when $\delta_{2S} < 1$Proved

  Oct 2026

• Unit positive mollification preserves eta0 second variation at most forty-eightProved

  Sep 2026

Posted 0

No theorems posted yet.