Survey Of Optimization Methods Research Articles

A Survey of Optimization Methods From a Machine Learning Perspective.

Machine learning develops rapidly, which has made many theoretical breakthroughs and is widely applied in various fields. Optimization, as an important part of machine learning, has attracted much attention of researchers. With the exponential growth of data amount and the increase of model complexity, optimization methods in machine learning face more and more challenges. A lot of work on solving optimization problems or improving optimization methods in machine learning has been proposed successively. The systematic retrospect and summary of the optimization methods from the perspective of machine learning are of great significance, which can offer guidance for both developments of optimization and machine learning research. In this article, we first describe the optimization problems in machine learning. Then, we introduce the principles and progresses of commonly used optimization methods. Finally, we explore and give some challenges and open problems for the optimization in machine learning.

電力システム運用における最適化技術

電力システム運用における最適化技術

A Survey of Optimization Methods in Optimal Control for Distributed Parameter Systems

Optimal control problems for systems governed by partial differential equations are now solved currently. Restricting ourselves to open loop controls, the techniques used are essentially adaptations of those used for the control of lumped systems. Most successful of all is the gradient method although quasi Newton and conjugate gradient methods could be used ; however the selection of an efficient step size rule is more than ever crucial. The evaluation of the cost function is so expensive that the search for a one dimensional minimum must be avoided. Linear instantaneous constraints on the control and/or the states variables can be handled by the gradient projection method and/or duality or penalization.

Commentary: A Survey of Optimization Methods

Commentary: A Survey of Optimization Methods