Recently, sparse coding has been widely used in many applications ranging from image recovery to pattern recognition. The low mutual coherence of a dictionary is an important property that ensures the optimality of the sparse code generated from this dictionary. Indeed, most existing dictionary learning methods for sparse coding either implicitly or explicitly tried to learn an incoherent dictionary, which requires solving a very challenging non-convex optimization problem. In this paper, we proposed a hybrid alternating proximal algorithm for incoherent dictionary learning, and established its global convergence property. Such a convergent incoherent dictionary learning method is not only of theoretical interest, but also might benefit many sparse coding based applications.

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We give an effective algorithm to compute the Euler characteristics χ(\mbar_{1,n}, \otimes_{i=1}^n L_i^{d_i}). In addition, we give a simple proof of Pandharipande's vanishing theorem H^j (\mbar_{0,n}, \otimes_{i=1}^n L_i^{d_i})=0 for j≥1,di≥0.

We study elliptic curves of the form x^3+y^3=2^p and x^3+y^3=2p^2 where p is any odd prime satisfying p ≡ 2 mod 9 or p ≡ 5 mod 9. We first show that the 3-part of the Birch-Swinnerton-Dyer conjecture holds for these curves. Then we relate their 2-Selmer group to the 2-rank of the ideal class group of Q(\sqrt[3]{p}) to obtain some examples of elliptic curves with rank one and non-trivial 2-part of the Tate-Shafarevich group.