Solving systems of phaseless equations via Kaczmarz methods: A proof of concept study

We study the Kaczmarz methods for solving systems of quadratic equations, i.e., the generalized phase retrieval problem. The methods extend the Kaczmarz methods for solving systems of linear equations by integrating a phase selection heuristic in each iteration and overall have the same per iteration computational complexity. Extensive empirical performance comparisons establish the computational advantages of the Kaczmarz methods over other state-of-the-art phase retrieval algorithms both in terms of the number of measurements needed for successful recovery and in terms of computation time. Preliminary convergence analysis is presented for the randomized Kaczmarz methods

Assignments of ΛQ\Lambda_Q and ΞQ\Xi_Q baryons in the heavy quark-light diquark picture

We apply a new mass formula which is derived analytically in the relativistic flux tube model to the mass spectra of $\Lambda_Q$ and $\Xi_Q$ (\emph{Q} = \emph{c} or \emph{b} quark) baryons. To this end, the heavy quark-light diquark picture is employed. We find that all masses of the available $\Lambda_Q$ and $\Xi_Q$ states can be understood well. The assignments to these states do not appear to contradict the strong decay properties. $\Lambda_c(2760)^+$ and $\Xi_c(2980)$ are assigned to the first radial excitations with $J^P = 1/2^+$. $\Lambda_c(2940)^+$ and $\Xi_c(3123)$ might be the 2\emph{P} states. The $\Lambda_c(2880)^+$ and $\Xi_c(3080)$ are the good 1\emph{D} candidates with $J^P = 5/2^+$. $\Xi_c(3055)$ is likely to be a 1\emph{D} state with $J^P = 3/2^+$. $\Lambda_b(5912)^0$ and $\Lambda_b(5920)^0$ favor the 1\emph{P} assignments with $J^P = 1/2^-$ and $3/2^-$, respectively. We propose a search for the $\tilde{\Lambda}_{c2}(5/2^-)$ state which can help to distinguish the diquark and three-body schemes.Comment: 9 tables, more discussions and references adde

Many Hard Examples in Exact Phase Transitions with Application to Generating Hard Satisfiable Instances

This paper first analyzes the resolution complexity of two random CSP models (i.e. Model RB/RD) for which we can establish the existence of phase transitions and identify the threshold points exactly. By encoding CSPs into CNF formulas, it is proved that almost all instances of Model RB/RD have no tree-like resolution proofs of less than exponential size. Thus, we not only introduce new families of CNF formulas hard for resolution, which is a central task of Proof-Complexity theory, but also propose models with both many hard instances and exact phase transitions. Then, the implications of such models are addressed. It is shown both theoretically and experimentally that an application of Model RB/RD might be in the generation of hard satisfiable instances, which is not only of practical importance but also related to some open problems in cryptography such as generating one-way functions. Subsequently, a further theoretical support for the generation method is shown by establishing exponential lower bounds on the complexity of solving random satisfiable and forced satisfiable instances of RB/RD near the threshold. Finally, conclusions are presented, as well as a detailed comparison of Model RB/RD with the Hamiltonian cycle problem and random 3-SAT, which, respectively, exhibit three different kinds of phase transition behavior in NP-complete problems.Comment: 19 pages, corrected mistakes in Theorems 5 and

Painless Breakups -- Efficient Demixing of Low Rank Matrices

Assume we are given a sum of linear measurements of $s$ different rank-$r$ matrices of the form $y = \sum_{k=1}^{s} \mathcal{A}_k ({X}_k)$. When and under which conditions is it possible to extract (demix) the individual matrices ${X}_k$ from the single measurement vector ${y}$? And can we do the demixing numerically efficiently? We present two computationally efficient algorithms based on hard thresholding to solve this low rank demixing problem. We prove that under suitable conditions these algorithms are guaranteed to converge to the correct solution at a linear rate. We discuss applications in connection with quantum tomography and the Internet-of-Things. Numerical simulations demonstrate empirically the performance of the proposed algorithms