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Nonlinear Optimization

Description

The Nonlinear Optimization course is a comprehensive overview of real analysis in finite dimensional spaces, with a focus on the current state of the art in nonlinear optimization. This course is designed to provide doctoral students with a thorough understanding of the theoretical underpinnings and convergence of various optimization approaches, as well as practical guidelines for their use and implementation.

Course Details

• The course covers a range of topics, including:
  • Unconstrained and constrained optimization
  • Linear and quadratic programming
  • Lagrange and conic duality theory
  • Interior-point algorithms and theory
  • Lagrangian relaxation
  • Generalized programming
  • Semi-definite programming
• Algorithmic methods used in the class include:
  • Steepest descent
  • Newton's method
  • Conditional gradient and sub-gradient optimization
  • Interior-point methods
  • Penalty and barrier methods

Course Period

The course period is from 17/01/22 onwards.