Hidden quasi one-dimensional superconductivity in Sr2_2RuO4_4

Using an asymptotically exact weak coupling analysis of a multi-orbital Hubbard model of the electronic structure of \SRO, we show that the interplay between spin and charge fluctuations leads unequivocally to triplet pairing which originates in the quasi-one dimensional bands. The resulting superconducting state spontaneously breaks time-reversal symmetry and is of the form $\Delta \sim p_x + i p_y \hat{z}$ with sharp gap minima and a d-vector that is only {\it weakly} pinned. The supercondutor is topologically {\it trivial} and hence lacks robust chiral Majorana fermion modes along the boundary. The absence of topologically protected edge modes could explain the surprising absence of experimentally detectable edge currents in this system.Comment: 5 pages, 3 figure

Particle-hole condensates of higher angular momentum in hexagonal systems

Hexagonal lattice systems (e.g. triangular, honeycomb, kagome) possess a multidimensional irreducible representation corresponding to $d_{x^2-y^2}$ and $d_{xy}$ symmetry. Consequently, various unconventional phases that combine these $d$-wave representations can occur, and in so doing may break time-reversal and spin rotation symmetries. We show that hexagonal lattice systems with extended repulsive interactions can exhibit instabilities in the particle-hole channel to phases with either $d_{x^2-y^2}+d_{xy}$ or $d+id$ symmetry. When lattice translational symmetry is preserved, the phase corresponds to nematic order in the spin-channel with broken time-reversal symmetry, known as the $\beta$ phase. On the other hand, lattice translation symmetry can be broken, resulting in various $d_{x^2-y^2}+d_{xy}$ density wave orders. In the weak-coupling limit, when the Fermi surface lies close to a van Hove singularity, instabilities of both types are obtained in a controlled fashion.Comment: 6 pages, 3 figures. Journal reference adde

Guaranteed Rank Minimization via Singular Value Projection

Minimizing the rank of a matrix subject to affine constraints is a fundamental problem with many important applications in machine learning and statistics. In this paper we propose a simple and fast algorithm SVP (Singular Value Projection) for rank minimization with affine constraints (ARMP) and show that SVP recovers the minimum rank solution for affine constraints that satisfy the "restricted isometry property" and show robustness of our method to noise. Our results improve upon a recent breakthrough by Recht, Fazel and Parillo (RFP07) and Lee and Bresler (LB09) in three significant ways: 1) our method (SVP) is significantly simpler to analyze and easier to implement, 2) we give recovery guarantees under strictly weaker isometry assumptions 3) we give geometric convergence guarantees for SVP even in presense of noise and, as demonstrated empirically, SVP is significantly faster on real-world and synthetic problems. In addition, we address the practically important problem of low-rank matrix completion (MCP), which can be seen as a special case of ARMP. We empirically demonstrate that our algorithm recovers low-rank incoherent matrices from an almost optimal number of uniformly sampled entries. We make partial progress towards proving exact recovery and provide some intuition for the strong performance of SVP applied to matrix completion by showing a more restricted isometry property. Our algorithm outperforms existing methods, such as those of \cite{RFP07,CR08,CT09,CCS08,KOM09,LB09}, for ARMP and the matrix-completion problem by an order of magnitude and is also significantly more robust to noise.Comment: An earlier version of this paper was submitted to NIPS-2009 on June 5, 200