Non-linear least squares solvers are used across a broad range of offline and real-time model fitting problems. Most improvements the basic Gauss-Newton algorithm tackle convergence guarantees or leverage sparsity underlying problem structure for computational speedup. With success deep learning methods leveraging large datasets, stochastic optimization received recently lot attention. Our work...

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...

We develop adaptive replicated designs for Gaussian process metamodels of stochastic experiments. Adaptive batching is a natural extension sequential design heuristics with the benefit replication growing as response features are learned, inputs concentrate, and metamodeling overhead rises. Motivated by problem learning level set mean simulator response, we five novel schemes: Multi-Level Batch...

In a video-on-demand (VOD) environment, batch-ing of requests is often applied to reduce the I/O demand and increase the availability of VOD services. This scheme, however, unfairly forces rst comers to wait for subscribers arriving late at the batch. Some of these victims may become impatient and decide to renege. To address this issue, we introduce a chaining technique which allows a client s...