This work studies solution methods for approximating a given tensor by sum of R rank-1 tensors with one or more the latent factors being orthonormal. Such problem arises from applications such as image processing, joint singular value decomposition, and independent component analysis. Most existing algorithms are iterative type, while approximation type limited. By exploring multilinearity orth...

We study joint chance constrained programs (JCCPs). JCCPs are often non-convex and non-smooth, and thus are generally challenging to solve. In this paper, we propose a logarithmsum-exponential smoothing technique to approximate a joint chance constraint by the difference of two smooth convex functions and use a sequential convex approximation algorithm, coupled with a Monte Carlo method, to sol...

This paper addresses the Tardy/Lost penalty minimization on a single machine. According to this penalty criterion, if the tardiness of a job exceeds a predefined value, the job will be lost and penalized by a fixed value. Besides its application in real world problems, Tardy/Lost measure is a general form for popular objective functions like weighted tardiness, late work and tardiness with reje...

The unified infinite dimensional model structure which assumes on its base to develop the method and algorithms of systems rational approximation and identification is proposed for distributed parameter systems with discrete inputs and outputs. The considered truncated realization converges to infinite-dimensional non-rational model of system for nuclear type operators. Approximation is represe...

F. Fallside We develop a sequential adaptation algorithm for radial basis function (RBF) neural networks of Gaussian nodes, based on the method of successive F-Projections. This method makes use of each observation efficiently in that the network mapping function so obtained is consistent with that information and is also optimal in the least L 2-norm sense. The RBF network with the F-Projectio...

In this paper, a sequential message-passing decoding algorithm of low-density parity-check (LDPC) codes by partitioning check nodes is analyzed. This decoding algorithm shows better bit error rate (BER) performance than the conventional message-passing decoding algorithm, especially for the small number of iterations. Analytical results tell us that as the number of partitioned subsets of check...