Nonconvex Optimization for Statistical Estimation

Abstract

The project aims to address the urgent need for a comprehensive investigation and development of novel mathematical theories and efficient computational methods to deal with important classes of coupled nonconvex nondifferentiable optimization problems arising from modern statistical estimation problems and beyond. Emphasis is placed on bridging theory, computation, and applications by rigorous mathematics. The research methodology is based on unified formulations of the empirical risk minimization in statistical estimation and planning problems under uncertainty as coupled nonconvex nondifferentiable optimization problems with exploitable structures.

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Document Details

Document Type
Technical Report
Publication Date
Jan 24, 2023
Accession Number
AD1230481

Entities

People

  • Jong-shi Pang

Tags

Readers

  • Computational Modeling and Simulation
  • Distributed Systems and Data Platform Development
  • Military Engineering.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms