This paper presents an error test function usable for the local error contro l and the automatic stepsize selection in the numerical integration of general index-1 and index-2 differentialalgebraic equations (DAEs). This test function makes a compromise between a good approximation of the error arising per step by the discretization (local error) and the order and smoothness assumptions made by...

Markov Random Fields (MRF) minimization is a well-known problem in computer vision. We consider the augmented dual of the MRF minimization problem and develop a Mirror Descent algorithm based on weighted Entropy and Euclidean Projection. The augmented dual problem consists of maximizing a non-differentiable objective function subject to simplex and linear constraints. We analyze the convergence...

Abstract. A strategy for controlling the stepsize in the numerical integration of stochastic differential equations (SDEs) is presented. It is based on estimating the p-th mean of local errors. The strategy leads to stepsize sequences that are identical for all computed paths. For the family of Euler schemes for SDEs with small noise we derive computable estimates for the dominating term of the...

In wireless communications, multi-channel transmission is utilized to obtain high capacity and better throughput efficiency. Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) is considered as spectrally efficient system to achieve high throughput communications. This paper investigates the performance enhancement of MIMO-OFDM system using NLMS, variable ste...

Computations with two flavours of dynamical staggered quarks are quite popular at present. There are a number of possible problems with such calculations such as flavour symmetry breaking and non-locality of the square-root of the fourflavour action. In this investigation we shall ignore these and consider only the possible errors introduced through algorithmic approximations. We propose the us...

The Primal-Dual hybrid gradient (PDHG) method is a powerful optimization scheme that breaks complex problems into simple sub-steps. Unfortunately, PDHG methods require the user to choose stepsize parameters, and the speed of convergence is highly sensitive to this choice. We introduce new adaptive PDHG schemes that automatically tune the stepsize parameters for fast convergence without user inp...

In this work, we present a new proposal for the second-order Adaptive Sigma Delta Modulation (ASDM). The new proposed Adaptation scheme is based on using Operational Transconductance Amplifier (OTAs) as an Integrator and as an Amplifier to adapt the quantizer stepsize to control the voltage gain by feedback the quantizer output through adaptation scheme. The stepsize is changing up or down by t...