The ordered statistics-based list decoding techniques for linear binary block codes of small to medium block length are investigated. The construction of a list of the test error patterns is considered. The original ordered-statistics decoding (OSD) is generalized by assuming segmentation of the most reliable independent positions (MRIPs) of the received bits. The segmentation is shown to overc...

We study sweeping preconditioners for symmetric and positive definite block tridiagonal systems of linear equations. The algorithm provides an approximate inverse that can be used directly or in a preconditioned iterative scheme. These algorithms are based on replacing the Schur complements appearing in a block Gaussian elimination direct solve by hierarchical matrix approximations with reduced...

Gaussian process emulator with separable covariance function has been utilized extensively in modelling large computer model outputs. The assumption of separability imposes constraints on the emulator and may negatively affect its performance in some applications where separability may not hold. We propose a multi-output Gaussian process emulator with a nonseparable autocovariance function to a...

We derive analytic bounds on the convergence factors associated with block relaxation methods for solving the discrete two-dimensional convection-diiusion equation. The analysis applies to the reduced systems derived when one step of block Gaussian elimination is performed on red-black ordered two-cyclic discretizations. We consider the case where centered nite diierence discretiza-tion is used...

We propose a variable decomposition algorithm– greedy block coordinate descent (GBCD)–in order to make dense Gaussian process regression practical for large scale problems. GBCD breaks a large scale optimization into a series of small sub-problems. The challenge in variable decomposition algorithms is the identification of a subproblem (the active set of variables) that yields the largest impro...

The estimation of the frequency slope of a partial from its peak in the DFT spectrum today is possible only if a Gaussian window is used. In the following we derive a new method to estimate the frequency slope of a partial from its DFT spectral peak based on the reassignment operators. Compared to the Gaussian window based method our new method can be used with a much larger variety of windows ...

We propose a new approximate factorization for solving linear systems with symmetric positive definite sparse matrices. In a nutshell the algorithm is to apply hierarchically block Gaussian elimination and additionally compress the fill-in. The systems that have efficient compression of the fill-in mostly arise from discretization of partial differential equations. We show that the resulting fa...