Generating asymptotically optimal broadcasting schedules to minimize average waiting time

The performance of a Video-on-Demand broadcasting scheme is commonly evaluated by the maximum waiting time encountered by the customer before viewing can start. This paper addresses the issue of minimizing the average waiting time. Recently, we proposed Harmonic Block Windows scheduling to specifically minimize the average waiting time for given bandwidth. Here, we present an efficient heuristic algorithm that generates asymptotically optimal Harmonic Block Windows schedules. Using simulation, we demonstrate that, as we increase the “block size”, the normalized average waiting time of these schedules approaches the theoretical minimum achievable by any “fixed start points” schedule.     

An improved simulated annealing algorithm for bandwidth minimization

In this paper, a simulated annealing algorithm is presented for the bandwidth minimization problem for graphs. This algorithm is based on three distinguished features including an original internal representation of solutions, a highly discriminating evaluation function and an effective neighborhood. The algorithm is evaluated on a set of 113 well-known benchmark instances of the literature and compared with several state-of-the-art algorithms, showing improvements of some previous best results.     

ISB-tree: A new indexing scheme with efficient expected behaviour

We present the interpolation search B-tree (ISB-tree), a new cache-aware indexing scheme that supports update operations (insertions and deletions) in $O(1)$ worst-case block transfers and search operations in $O({\log }_B\log n)$ expected block transfers, where B represents the disk block size and n denotes the number of stored elements. The expected search bound holds with high probability for a large class of (unknown) input distributions. The worst-case search bound of our indexing scheme is $O({\log }_{B}n)$ block transfers. Our update and expected search bounds constitute a considerable improvement over the $O({\log }_{B}n)$ worst-case block transfer bounds for search and update operations achieved by the B-tree and its numerous variants. This is also verified by an accompanying experimental study.     

An efficient gradient method with approximate optimal stepsize for the strictly convex quadratic minimization problem

In this paper, a new type of stepsize, approximate optimal stepsize, for gradient method is introduced to interpret the Barzilai–Borwein (BB) method, and an efficient gradient method with an approximate optimal stepsize for the strictly convex quadratic minimization problem is presented. Based on a multi-step quasi-Newton condition, we construct a new quadratic approximation model to generate an approximate optimal stepsize. We then use the two well-known BB stepsizes to truncate it for improving numerical effects and treat the resulted approximate optimal stepsize as the new stepsize for gradient method. We establish the global convergence and R-linear convergence of the proposed method. Numerical results show that the proposed method outperforms some well-known gradient methods.     

An efficient multistep iteration scheme for systems of nonlinear algebraic equations associated with integral equations

The aim of this research is to present a new iterative procedure in approximating nonlinear system of algebraic equations with applications in integral equations as well as partial differential equations (PDEs). The presented scheme consists of several steps to reach a high rate of convergence and also an improved index of efficiency. The theoretical parts are furnished, and several computational tests mainly arising from practical problems are given to manifest its applicability.     

An efficient difference scheme for the coupled nonlinear fractional Ginzburg–Landau equations with the fractional Laplacian

In this article, an efficient difference scheme for the coupled fractional Ginzburg–Landau equations with the fractional Laplacian is studied. We construct the discrete scheme based on the implicit midpoint method in time and a weighted and shifted Grünwald difference method in space. Then, we prove that the scheme is uniquely solvable, and the numerical solutions are bounded and unconditionally convergent in the norm. Finally, numerical tests are given to confirm the theoretical results and show the effectiveness of the scheme.     

Traffic assignment model with fuzzy level of travel demand: An efficient algorithm based on quasi-Logit formulas

The place of fuzzy concepts in traffic assignment (TA) models has been studied in recent literature. Keeping fuzzy level of travel demand in mind, we propose a new TA model in which the travel costs of links are depended on their congestion. From the results of such fuzzy TA model, network planners are able to estimate the number of travelers on network links. By using zero–one variables, the proposed model is transformed into a crisp mixed-integer problem with respect to path-flow variables. In order to produce the Logit flows from this problem, Damberg et al. algorithm is modified. Then, the level of certainty is maximized and perceived travel delays are minimized. For a fixed certainty degree, the obtained solution, which is named the fuzzy equilibrium flow, satisfies a quasi-Logit formula similar to ordinary expression of the Logit route choice model. Eventually, we examine the quality of different path enumeration techniques in the proposed model.