This paper presents two polynomial time algorithms for the constant capacitated lot sizing problem with a batch production. We give several optimality properties for the general problem. Assuming constant production capacity, constant batch size and WagnerWhitin cost structure, we derive O(T 4) and O(T 6) time algorithms respectively for the case with production capacity multiple of the batch s...

Active learning techniques have gained popularity in reducing human effort to annotate data instances for inducing a classifier. When faced with large quantities of unlabeled data, such algorithms automatically select the salient and representative samples for manual annotation. Batch mode active learning schemes have been recently proposed to select a batch of data instances simultaneously, ra...

It has been experimentally observed that distributed implementations of mini-batch stochastic gradient descent (SGD) algorithms exhibit speedup saturation and decaying generalization ability beyond a particular batch-size. In this work, we present an analysis hinting that high similarity between concurrently processed gradients may be a cause of this performance degradation. We introduce the no...

In this paper we propose a novel approach to automatically determine the batch size in stochastic gradient descent methods. The choice of the batch size induces a trade-off between the accuracy of the gradient estimate and the cost in terms of samples of each update. We propose to determine the batch size by optimizing the ratio between a lower bound to a linear or quadratic Taylor approximatio...

this paper addresses a production and outbound distribution scheduling problem in which a set of jobs have to be process on a single machine for delivery to customers or to other machines for further processing. we assume that there is a sufficient number of vehicles and the delivery costs is independent of batch size but it is dependent on each trip. in this paper, we present an artificial imm...

When using active learning, smaller batch sizes are typically more efficient from a learning efficiency perspective. However, in practice due to speed and human annotator considerations, the use of larger batch sizes is necessary. While past work has shown that larger batch sizes decrease learning efficiency from a learning curve perspective, it remains an open question how batch size impacts m...

The effects of ultrasonication of corn slurry, on particle size distribution and enzymatic hydrolysis was studied for the dry-grind mill ethanol industry. Two independent ultrasonic experiments were conducted at a frequency of 20 kHz; in batch and continuous systems. The ground corn slurry (33% m/v) was pumped at flow rates 10-28 L/min in continuous flow experiments, and sonicated at constant a...

The batch is an important technique for delivering video over Internet or VoD. It is a key method to improve effect for video multicast. In this paper, we research the batch strategy of proxy cache for streaming media using dynamic cache, proposed the three kind of cache algorithm for proxy cache: window batch, size batch, efficient batch. These methods increased the length of batch, solved the...

Semiconductor memories have, since 1970, been a significant factor in data storage. This has been especially true in Random Access memories and is becoming true of the slower serial access memories. The manufacture of semiconductor memories takes place in two stages; the batch manufacture of the dice as wafers and the piece-by-piece assembly of the good dice into packages. It is the batch manuf...

Large batch size training of Neural Networks has been shown to incur accuracy loss when trained with the current methods. The precise underlying reasons for this are still not completely understood. Here, we study large batch size training through the lens of the Hessian operator and robust optimization. In particular, we perform a Hessian based study to analyze how the landscape of the loss fu...