A generic and fast C++ optimization framework
This provides a practical tool for researchers and developers in machine learning who require efficient and customizable optimization, though it is incremental as it builds on existing library design principles.
The authors tackled the need for a flexible and fast optimization framework in machine learning by developing a generic C++ system for the mlpack library, which supports a wide range of objective functions and allows easy implementation of new optimizers and functions with minimal code.
The development of the mlpack C++ machine learning library (http://www.mlpack.org/) has required the design and implementation of a flexible, robust optimization system that is able to solve the types of arbitrary optimization problems that may arise all throughout machine learning problems. In this paper, we present the generic optimization framework that we have designed for mlpack. A key priority in the design was ease of implementation of both new optimizers and new objective functions to be optimized; therefore, implementation of a new optimizer requires only one method and implementation of a new objective function requires at most four functions. This leads to simple and intuitive code, which, for fast prototyping and experimentation, is of paramount importance. When compared to optimization frameworks of other libraries, we find that mlpack's supports more types of objective functions, is able to make optimizations that other frameworks do not, and seamlessly supports user-defined objective functions and optimizers.