A grid computing system consists of a group of programs and resources that are spread across machines in the grid. A grid system has a dynamic environment and decentralized distributed resources, so it is important to provide efficient scheduling for applications. Task scheduling is an NP-hard problem and deterministic algorithms are inadequate and heuristic algorithms such as particle swarm op...

Determining the appropriate batch size for mini-batch gradient descent is always time consuming as it often relies on grid search. This paper considers a resizable mini-batch gradient descent (RMGD) algorithm based on a multi-armed bandit for achieving best performance in grid search by selecting an appropriate batch size at each epoch with a probability defined as a function of its previous su...

We present, implement, and analyze a spectrum of closely-related planners, designed to gain insight into the relationship between classical grid search and probabilistic roadmaps (PRMs). Building on quasi-Monte Carlo sampling literature, we have developed deterministic variants of the PRM that use low-discrepancy and low-dispersion samples, including lattices. Classical grid search is extended ...

In many areas of design search and optimisation one needs to utilize Computational Fluid Dynamics (CFD) methods in order to obtain a numerical solution of the flow field in and/or around a proposed design. From this solution measures of quality for the design may be calculated, which are then used by the optimisation methods. In large models the processing time for the CFD computations can very...

According to a 2007 global survey of 178 organisational intranets, 3 out of 5 organisations are not satisfied with their intranet search services. However, as intranet data collections become large, effective full-text intranet search services are needed more than ever before. To provide an effective full-text search service based on current information retrieval algorithms, organisations have ...

We consider a non-parametric penalized likelihood approach for model building called likelihood basis pursuit (LBP) that determines the probabilities of binary outcomes given explanatory vectors while automatically selecting important features. The LBP model involves parameters that balance the competing goals of maximizing the log-likelihood and minimizing the penalized basis pursuit terms. Th...

Sparse logistic regression is an important linear classifier in statistical learning, providing an attractive route for feature selection. A popular approach is based on minimizing an l1-regularization term with a regularization parameter λ that affects the solution sparsity. To determine an appropriate value for the regularization parameter, one can apply the grid search method or the Bayesian...

Optimal parameter model finding is usually a crucial task in engineering applications of classification and modelling. The exponential cost of linear search on a parameter grid of a given precision rules it out in all but the simplest problems and random algorithms such as uniform design or the covariance matrix adaptation-evolution strategy (CMA-ES) are usually applied. In this work we shall p...

A Modern wireless broadband system of MIMO OFDM (multiple input multiple output orthogonal frequency division multiplexing) is more popular because of good data transmission rate, robustness against multipath fading and better spectral efficiency. This system provides reliable communication and wider coverage. A main challenge to MIMO OFDM system is the retrieval of channel state information (C...