Efficient Hessian computation using sparse matrix derivatives in RAM notation

Efficient Hessian computation using sparse matrix derivatives in RAM notation.

This article proposes a new, more efficient method to compute the minus two log likelihood, its gradient, and the Hessian for structural equation models (SEMs) in reticular action model (RAM) notation. The method exploits the beneficial aspect of RAM notation that the matrix derivatives used in RAM are sparse. For an SEM with K variables, P parameters, and P' entries in the symmetrical or asymm...

Efficient Sparse Matrix-matrix Products Using Colorings

Sparse matrix-matrix products appear in multigrid solvers and computational methods for graph theory. Some formulations of these products require the inner product of two sparse vectors, which have inefficient use of cache memory. In this paper, we propose a new algorithm for computing sparse matrix-matrix products by exploiting matrix nonzero structure through the process of graph coloring. We...

Efficient Maliciously Secure Multiparty Computation for RAM

A crucial issue, that mostly affects the performance of actively secure computation of RAM programs, is the task of reading/writing from/to memory in a private and authenticated manner. Previous works in the active security and multiparty settings are based purely on the SPDZ (reactive) protocol, hence, memory accesses are treated just like any input to the computation. However, a garbled-circu...

Appendix 4.6.b Efficient Computation of Minimum Variance Wavefront Reconstructors Using Sparse Matrix Techniques

Efficient computation of sparse structures

Basic graph structures such as maximal independent sets (MIS’s) have spurred much theoretical research in randomized and distributed algorithms, and have several applications in networking and distributed computing as well. However, the extant (distributed) algorithms for these problems do not necessarily guarantee fault-tolerance or load-balance properties. We propose and study “lowaverage deg...