Brief Introduction

This course systematically introduces the basic concepts, core methods, and classic algorithms of optimization theory. It is divided into three main parts: unconstrained optimization, constrained optimization, and advanced topics and algorithms.

The course begins with the foundations of unconstrained optimization, including necessary and sufficient conditions for minima, line search methods, and gradient-based methods. It then moves to constrained optimization, covering the geometry of constraints, Lagrange multipliers, KKT conditions, and linear programming. The final part introduces advanced topics, including convex and non-convex optimization, duality theory, stochastic gradient descent, and evolutionary algorithms.

Time

2026-07-06 ~ 2026-07-09  13:20 - 17:05

Lecturers

Antonios Varvitsiotis, Singapore University of Technology and Design

Venue

Room 802, No.3 Teaching Building, Shanghai University of Finance & Economics

Application and Registration

No registration fee.

Program

  1. Review of calculus and linear algebra

  2. Introduction to line search

  3. Gradient descent and its neighbors

  4. Constrained optimization

  5. Lagrange multiplier and KKT condition

  6. Linear programming

  7. From convex to nonconvex optimization

  8. Duality