Sentence examples for order optimization from inspiring English sources

A first order optimization method is adopted for that purpose while the gradients required are efficiently derived analytically.

To address this issue, we propose a novel large batch training method which combines recent results in adversarial training (to regularize against 'sharp minima') and second order optimization (to use curvature information to change batch size adaptively during training).

R. L. Raffard and C. J. Tomlin, Second Order Optimization of Ordinary and Partial Differential Equations with Application to Air Traffic Flow, Proceedings of the AACC American Control Conference, Portland, pp. 798-803, June 2005.

This paper introduces a novel approach based on reinforcement learning for class integration test order optimization in the context of integration testing.

To reason about compensation options in case of disturbances, an knowledge-based production order optimization realized in a Manufacturing Execution System (MES) is presented.

During the subsequent interaction order optimization, the method tries to find an order, which satisfies this adjacency demand.

The zero-order optimization method is applied to optimize the various design variables, such as Young's modulus of the material, damping loss factors and the diameters of cylindrical holes, etc., to achieve the desirable acoustic performance of the absorbent lining at a single frequency of interest.

Since our stated problem exhibits a clear ordering of the objectives with respect to their priority, namely junction-cleavage likelihood over neo-immunogenicity, the problem of finding a Pareto-optimal solution can be significantly simplified by applying lexicographical ordered optimization (LO).

We illustrate the relative merits and performance of quasi-Newton and full second-order optimization with a series of examples, showing that even a pulse already optimized using other methods can be visibly improved.

His current research includes large scale training of Neural Networks, stochastic second-order optimization methods, and robust optimization.

Amir has extensive experience in second-order optimization methods, image registration, inverse problems, and large scale parallel computing, developing codes that have been scaled up to 200K cores.