Grid search and manual search are the most widely used strategies for hyper-parameter optimization. This paper shows empirically and theoretically that randomly chosen trials are ...
Many large content publishers use multiple content distribution networks to deliver their content, and many industrial systems have become available to help a broader set of conte...
Hongqiang Harry Liu, Ye Wang, Yang Richard Yang, H...
Abstract. Surface reconstruction from a set of noisy point measurements has been a well studied problem for several decades. Recently, variational and discrete optimization approac...
The traveling salesman problem with time windows is known to be a really difficult benchmark for optimization algorithms. In this paper, we are interested in the minimization of th...
We present methods to answer two basic questions that arise when benchmarking optimization algorithms. The first one is: which algorithm is the `best' one? and the second one:...
Following from part I, which presents a retrospective on optimization, we focus here on areas that are recent active research topics and are likely to strongly influence the futur...
The sequential parameter optimization (spot) package for R (R Development Core Team, 2008) is a toolbox for tuning and understanding simulation and optimization algorithms. Model-...
In this paper, a parallel evolutionary multi-criteria optimization algorithm: DGA and DRMOGA are applied to block layout problems. The results are compared to the results of SGA an...
Large-margin structured estimation methods minimize a convex upper bound of loss functions. While they allow for efficient optimization algorithms, these convex formulations are n...
Olivier Chapelle, Chuong B. Do, Quoc V. Le, Alexan...
Many popular optimization algorithms, like the Levenberg-Marquardt algorithm (LMA), use heuristic-based "controllers" that modulate the behavior of the optimizer during ...