Improving the efficiency of variational methods for solving strongly nonsymmetric linear algebraic equation system received in convection-diffusion problems

An efficient algorithm for implementing the mathematical model of convection-diffusion transport with a dominant convection is proposed. Preconditioned Krylov subspace methods are used to solve strongly nonsymmetric systems of linear algebraic equations. Theoretical convergence analysis of product triangular skew-symmetric preconditioners is carried out. A series of numerical experiments have confirmed the technique’s efficiency.     

Improving the efficiency of scanline algorithms

Design rule checking and net extraction of VLSI circuits are complex processes, involving many steps. At the leaf level, however, we must consider geometric shape-shape interactions. It is very important that these operations are as efficient as possible, as a slight saving at the leaf level will be reflected as a significant enhancement if the overall process. It is demonstrated that an intelligent preprocessing step, applied once to each mask layer, and an ordered decomposition of compound geometric operations into binary atoms, can make an important contribution to process efficiency.     

Improving the efficiency of a Godunov-based free surface flow model

Models for simulating turbulent, nonhydrostatic, free surface flow are highly complex and require the combination of several different physical phenomena. Consequently, there are many different approaches to design such a model. This paper explores ways to improve the numerical efficiency of an existing free surface flow model without severely sacrificing its accuracy. Specifically, this paper investigates alternative methods for integrating diffusion as well as extending the model to nonhydrostatic flow.     

Improving the time efficiency of proving theorems using a learning mechanism

In this paper, we develop a new approach for enhancing the time efficiency of proving theorems by using a learning mechanism. A system is proposed for analyzing a set of theorems and observing those features that often affect the speed at which the theorems are proved. The system uses the learning mechanism for choosing between two well known theorem-provers, namely, Resolution–Refutation (TGTP) and Semantic Trees (HERBY). A three-step process has been implemented. The first step is to prove a set of theorems using the above two theorem provers. A training set of two classes of theorems is thus created. Each class represents those theorems that have been proven in less time using a particular theorem prover. The second step is to train neural networks on both classes of theorems in order to construct an internal representation of the decision boundary between the two classes. In the last step, a voting scheme is invoked in order to combine the decisions of the individual neural networks into a final decision. The results achieved by the system when working on the standard theorems of the Stickel Test Set are shown. Those results confirm the feasibility of our approach to inte-grate a learning mechanism into the process of automated theorem proving.     

Improving the efficiency of direct-memory-access output operations

The improvements in throughput expected from direct-memory-access channels are not realized with simple input/output routines. For output operations, the improvements can be realized through the provision of a separate data buffer area in memory that may be accessed by the peripheral while the primary buffer area is being manipulated by the program. The method may be extended to peripherals that require error checking after output.     

Improving the delivery efficiency of the customer order scheduling problem in a job shop

The focus of this paper is customer order scheduling (COS) problem, where each order consists of a set of jobs that must be shipped as one batch at the same time. In COS each job is part of a customer order and the make-up of the jobs in the order are pre-specified. Most of the existing research deals with COS in a single machine or in a parallel machine shop for developing an optimal solution. COS is common in a normal job shop, and the more complex the shop, the more complex the scheduling. Most existing research has focused on trying to reduce the completion time of the batch. That is, the focus is only on the point in time the last job is finished, while ignoring the actual duration of the jobs within the same order. The longer it takes to complete all the jobs within an order the more it increases the stock of finished goods and the more it deteriorates the efficiency of the logistics and the supply chain management.A new dispatching rule, referred to as Minimum Flow Time Variation (MFV), has been proposed for COS in a normal job shop, in order to reduce the total time it takes to complete all jobs within the same order. That is, the individual completion times of all jobs for the same customer order will be controlled in order to improve the shipping performance. In the simulation test and statistical analysis, the level of work in process (WIP) under the MFV rule in the finished goods warehouse is reduced by more than 70% compared to any other method. The MFV method will efficiently reduce the stock level of finished goods, and controls the waiting time required before they can be shipped. Depending on the environmental factors, the performance of our proposed method will become increasingly significant the more complex the system.     

Improving the computational efficiency of an agent-based spatiotemporal model of livestock disease spread and control

Agent-based models (ABMs) are well suited to representing the spatiotemporal spread and control of disease in a population. The explicit modelling of individuals in a large population, however, can be computationally intensive, especially when models are stochastic and/or spatially-explicit. Large-scale ABMs often require a highly parallel platform such as a high-performance computing cluster, which tends to confine their utility to university, defence and scientific research environments. This poses a challenge for those interested in modelling the spread of disease on a large scale with access only to modest hardware platforms.The Australian Animal DISease (AADIS) model is a spatiotemporal ABM of livestock disease spread and control. The AADIS ABM is able to complete complex national-scale simulations of disease spread and control on a personal computer. Computational efficiency is achieved through a hybrid model architecture that embeds equation-based models inside herd agents, an asynchronous software architecture, and a grid-based spatial indexing scheme.     

Improving the efficiency of preconditioning for iterative methods

Techniques based on the idea of preconditioning and on the element-by-element concept have significantly improved the efficiency of classical iterative methods in conventional as well as in parallel hardware environment. In this work two preconditioning approaches based on the incomplete Choleski factorization have been further refined, with the result that both storage requirements and solution times have been greatly improved when processing large structural problems. The partial preconditioning method is also employed to develop a framework for constructing a global preconditioner, without the need to store the complete coefficient matrix, for accelerating an iterative element-by-element solution procedure.     

Improving the efficiency of inductive logic programming systems

Inductive logic programming (ILP) is a sub‐field of machine learning that provides an excellent framework for multi‐relational data mining applications. The advantages of ILP have been successfully demonstrated in complex and relevant industrial and scientific problems. However, to produce valuable models, ILP systems often require long running times and large amounts of memory. In this paper we address fundamental issues that have direct impact on the efficiency of ILP systems. Namely, we discuss how improvements in the indexing mechanisms of an underlying logic programming system benefit ILP performance. Furthermore, we propose novel data structures to reduce memory requirements and we suggest a new lazy evaluation technique to search the hypothesis space more efficiently. These proposals have been implemented in the April ILP system and evaluated using several well‐known data sets. The results observed show significant improvements in running time without compromising the accuracy of the models generated. Indeed, the combined techniques achieve several order of magnitudes speedup in some data sets. Moreover, memory requirements are reduced in nearly half of the data sets. Copyright © 2008 John Wiley & Sons, Ltd.     

Improving the efficiency of nondeterministic independent and-parallel systems

We present the design and implementation of the and-parallel component of ACE. ACE is a computational model for the full Prolog language that simultaneously exploits both or-parallelism and independent and-parallelism. A high-performance implementation of the ACE model has been realized and its performance reported in this paper. We discuss how some of the standard problems which appear when implementing and-parallel systems are solved in ACE. We then propose a number of optimizations aimed at reducing the overheads and the increased memory consumption which occur in such systems when using previously proposed solutions. Finally, we present results from an implementation of ACE which includes the optimizations proposed. The results show that ACE exploits and-parallelism with high efficiency and high speedups. Furthermore, they also show that the proposed optimizations, which are applicable to many other and-parallel systems, significantly decrease memory consumption and increase speedups and absolute performance both in forward execution and during backtracking.     

Improving the efficiency of automated protocol implementation using Estelle

Correctness and runtime efficiency are essential properties of software in general and of high-speed protocols in particular. Establishing correctness requires the use of FDTs during protocol design, and to prove the protocol code correct with respect to its formal specification. Another approach to boost confidence in the correctness of the implementation is to generate protocol code automatically from the specification. However, the runtime efficiency of this code is often insufficient. This has turned out to be a major obstacle to the use of FDTs in practice. One of the FDTs currently applied to communication protocols is Estelle. We show how runtime efficiency can be significantly improved by several measures carried out during the design, implementation and runtime of a protocol. Recent results of improvements in the efficiency of Estelle-based protocol implementations are extended and interpreted.     

A square root of a matrix approach to obtain the solution to a steady-state matrix Riccati equation

By utilizing the square root of a matrix approach, a method of generating the solution to a steady-state matrix Riccati type equation (under certain restrictions) is presented. This approach not only yields a closed form expression for the Riccati solution, but also converts the original Riccati equation into other equations which may have numerical or computational advantages. An example is worked out for a second-order case.     

Improving the efficiency of a hyperlinking-based theorem prover by incremental evaluation with network structures

Recently Lee and Plaisted proposed a theorem-proving method, the hyperlinking proof procedure, to eliminate duplication of instances of clauses during the process of inference. A theorem prover, CLIN, which implements the procedure was also constructed. In this implementation, redundant work on literal unification checking, partial unification checking, and duplicate instance checking is performed repetitively, resulting in a large overhead when many rounds of hyperlinking are needed for an input problem. We propose a technique that maintains information across rounds in shared network structures, so that the redundant work in each hyperlinking round can be avoided. Empirical performance comparison has been done between CLIN and CLIN-net, which is the theorem prover with shared network structures, and some results are shown. Problems related to memory overhead and literal ordering are discussed.Supported by National Science Council under grants NSC 81-0408-E-110-509 and NSC-82-0408-E-110-045. A preliminary version of this paper appeared in Proceedings of International Conference on Computing and Information (Sudbury, Ontario Canada, May 1993).     

A method for computing a root of a single nonlinear equation,including its multiplicity

A second order iteration method is proposed for the simultaneous computation of a root and its multiplicity of a single nonlinear equation. This method consists essentially in a Steffenson-like acceleration of a Newton-Raphson process that depends on a parameter which is forced to converge to the multiplicity.     

Improving the efficiency of the dynamic analysis of mass flow networks

Dynamic network analysis algorithms are studied with a view to improving computational efficiency. The main possibility is an appropriate choice of space and time intervals. Using concepts from generalized 4-pole theory t the paper deduces relations for determining near-optimal Δx and Δt values by a prescribed computational error. As an example, the general relations are applied to the analysis of gas pipe networks.     

Camera calibration with enclosing ellipses by an extended application of generalized eigenvalue decomposition

Conics-based calibration has been widely inves- tigated in the past several years. The primary method is to utilize the generalized eigenvalue decomposition (GED) of conics. In this paper, we extend the GED method to deal with a general case: two enclosing ellipses. We construct a link between enclosing ellipses and confocal conics. The homography is, thus, decomposed to three components, each of which corresponds to a clear geometrical meaning. Other general conics cases including separate case, intersecting case and hyperbolas case are also discussed. Experiments with simulated and real data demonstrate the good performance of our camera calibration algorithms.     

Improving the efficiency of multipath traffic via opportunistic traffic scheduling

Multipath routing, as defined by OSPF extensions and other protocols, enables a network’s traffic to be split among two, or more, possibly disjoint paths. Advantages of multipath vs. unipath routing include load balancing, reduced latency, and improved throughput. However, once the control plane establishes multiple routes, a policy is needed for efficiently splitting traffic among the selected paths. In this paper, we introduce opportunistic multipath scheduling (OMS), a technique for exploiting short-term variations in path quality to minimize delay, while simultaneously ensuring that the splitting rules dictated by the routing protocol are satisfied. We develop a performance model of OMS and derive an asymptotic lower bound on the performance of OMS as a function of path conditions (mean, variance, and Hurst parameter) for self-similar traffic. Finally, we use an extensive simulation-based performance study to evaluate the accuracy of the analytical model, explore the impact of OMS on TCP throughput and performance of real-time traffic, and study the impact of factors such as delayed measurements.     

Improving the efficiency and fairness of eXplicit Control Protocol in multi-bottleneck networks

The Transmission Control Protocol (TCP) has been extensively credited for the stability of the Internet. However, as the product of bandwidth and latency increases, TCP becomes inefficient and prone to instability. The eXplicit Control Protocol (XCP) is a novel and promising congestion control protocol that outperforms TCP in terms of efficiency, fairness, convergence speed, persistent queue length and packet loss rate. However, the XCP equilibrium solves a constrained max–min fairness problem instead of a standard max–min fairness problem. The additional constraint under XCP leads to inefficiency and unfairness for the topologies that have multiple bottleneck links.In this paper, according to classical control theory, we propose an XCP bandwidth compensation algorithm on basis of the proportional integral controller (PI-XCP), which reconfigures the available bandwidth variable from the fixed hardware determined physical link capacity value to a configuration value that can be dynamically changed. Through a reasonable online compensation, PI-XCP gets efficient and fair bandwidth allocation in a multi-bottleneck network. Extensive simulations have shown that PI-XCP indeed achieves this goal. Simulations also have shown that PI-XCP preserves good properties of XCP, including fast convergence, negligible queue length and zero packet loss rate. Compared with iXCP, an enhancement to address the XCP equilibrium problem, PI-XCP decreases the computational complexity significantly, and achieves more effective control in highly dynamic situations, especially in the presence of short-lived flows.