Machine Learning Optimization of Lignin Properties in Green Biorefineries

Comparative Analysis of Machine Learning Algorithms with Optimization Purposes

The field of optimization and machine learning are increasingly interplayed and optimization in different problems leads to the use of machine learning approaches‎. ‎Machine learning algorithms work in reasonable computational time for specific classes of problems and have important role in extracting knowledge from large amount of data‎. ‎In this paper‎, ‎a methodology has been employed to opt...

Geometric Optimization in Machine Learning

Machine learning models often rely on sparsity, low-rank, orthogonality, correlation, or graphical structure. The structure of interest in this chapter is geometric, specifically the manifold of positive definite (PD) matrices. Though these matrices recur throughout the applied sciences, our focus is on more recent developments in machine learning and optimization. In particular, we study (i) m...

Tractable Optimization in Machine Learning

Machine Learning (ML) broadly encompasses a variety of adaptive, autonomous, and intelligent tasks where one must “learn” to predict from observations and feedback. Throughout its evolution, ML has drawn heavily and successfully on optimization algorithms. This relation to optimization is not surprising as “learning” and “adapting” usually lead to problems where some quality function must be op...

Optimization for Machine Learning

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Optimization for Machine Learning