Improvement In Convergence Time Research Articles

A New Method to Accelerate PPP Convergence Time by using a Global Zenith Troposphere Delay Estimate Model

This paper presents a new algorithm to accelerate Precise Point Positioning (PPP) convergence. The main idea is to consider the station tropospheric zenith total delay, which is obtained by a global zenith troposphere delay estimate model, as virtual observation and combine it with phase and pseudo-range observations to formulate observation equations. Without relying on any other external enhancement information, it only requires four satellites to quickly complete the positioning with centimetre-level accuracy. Compared with the conventional method, the new one brings about 15% improvement in convergence time.

Fluxomers: a new approach for 13C metabolic flux analysis

BackgroundThe ability to perform quantitative studies using isotope tracers and metabolic flux analysis (MFA) is critical for detecting pathway bottlenecks and elucidating network regulation in biological systems, especially those that have been engineered to alter their native metabolic capacities. Mathematically, MFA models are traditionally formulated using separate state variables for reaction fluxes and isotopomer abundances. Analysis of isotope labeling experiments using this set of variables results in a non-convex optimization problem that suffers from both implementation complexity and convergence problems.ResultsThis article addresses the mathematical and computational formulation of 13C MFA models using a new set of variables referred to as fluxomers. These composite variables combine both fluxes and isotopomer abundances, which results in a simply-posed formulation and an improved error model that is insensitive to isotopomer measurement normalization. A powerful fluxomer iterative algorithm (FIA) is developed and applied to solve the MFA optimization problem. For moderate-sized networks, the algorithm is shown to outperform the commonly used 13CFLUX cumomer-based algorithm and the more recently introduced OpenFLUX software that relies upon an elementary metabolite unit (EMU) network decomposition, both in terms of convergence time and output variability.ConclusionsSubstantial improvements in convergence time and statistical quality of results can be achieved by applying fluxomer variables and the FIA algorithm to compute best-fit solutions to MFA models. We expect that the fluxomer formulation will provide a more suitable basis for future algorithms that analyze very large scale networks and design optimal isotope labeling experiments.

Fast Parallel and Adaptive Updates for Dual-Decomposition Solvers

Dual-decomposition (DD) methods are quickly becoming important tools for estimating the minimum energy state of a graphical model. DD methods decompose a complex model into a collection of simpler subproblems that can be solved exactly (such as trees), that in combination provide upper and lower bounds on the exact solution. Subproblem choice can play a major role: larger subproblems tend to improve the bound more per iteration, while smaller subproblems enable highly parallel solvers and can benefit from re-using past solutions when there are few changes between iterations. We propose an algorithm that can balance many of these aspects to speed up convergence. Our method uses a cluster tree data structure that has been proposed for adaptive exact inference tasks, and we apply it in this paper to dual-decomposition approximate inference. This approach allows us to process large subproblems to improve the bounds at each iteration, while allowing a high degree of parallelizability and taking advantage of subproblems with sparse updates. For both synthetic inputs and a real-world stereo matching problem, we demonstrate that our algorithm is able to achieve significant improvement in convergence time.

Estimating building dimensions from synthetic aperture radar image sequences

The problem of automatically extracting building dimensions from synthetic aperture radar (SAR) image sequences of urban scenes is considered. An algorithm based on the delineation of shadows using active contours constrained by a simple wire-frame building model has been developed and demonstrated using SAR imagery of a village on Salisbury plain. The core of the algorithm is a novel technique for target delineation involving multiple active contours evolving simultaneously. In particular, a technique referred to as multiple hypothesis delineation, in which contours can be in several states simultaneously, is developed and shown to lead to considerable improvement in convergence time and delineation accuracy when used to delineate multiple targets in close proximity. The technique is applied to the automatic estimation of building dimensions by delineation of shadows in a sequence of SAR images of an urban scene. The estimation of building dimensions is automatic; user interaction is limited identifying a building of interest and a region of background clutter close to the building. Results are presented for six different buildings, in each case two SAR images were used in the estimation process separated in illumination angle by either 28° or 90°. The estimates of building dimensions are compared with the actual building dimensions obtained from architectural drawings. The algorithm was found to perform robustly and provide reasonably accurate estimates of the building dimensions, typically within ∼10% of the true values.

Efficient SVM Regression Training with SMO

The sequential minimal optimization algorithm (SMO) has been shown to be an effective method for training support vector machines (SVMs) on classification tasks defined on sparse data sets. SMO differs from most SVM algorithms in that it does not require a quadratic programming solver. In this work, we generalize SMO so that it can handle regression problems. However, one problem with SMO is that its rate of convergence slows down dramatically when data is non-sparse and when there are many support vectors in the solution—as is often the case in regression—because kernel function evaluations tend to dominate the runtime in this case. Moreover, caching kernel function outputs can easily degrade SMO's performance even more because SMO tends to access kernel function outputs in an unstructured manner. We address these problems with several modifications that enable caching to be effectively used with SMO. For regression problems, our modifications improve convergence time by over an order of magnitude.

ACCELERATED MONTE CARLO FOR PARTICLE DISPERSION

We discuss adaptive importance sampling for Markov chains. Such chains arise in many simulation problems, such as related to airborne particle dispersion. Through use of experimental design for simulation runs and regression analysis of the output, importance sampling transition kernels are defined which substantially improve convergence times.

The convergence of a PLL with variable parameters

AbstractA phase‐locked loop with variable parameters is considered. In such a system it is possible to obtain a short convergence time together with a small bandwidth. It is supposed that the PLL's parameters cannot be switched and there is a restriction on the velocity of the parameter variation.An optimal strategy of parameter variation is obtained which gives an improvement in convergence time by a factor of about 30 in comparision to fixed parameter and small‐bandwidth PLLs.