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Convex Optimization: Fundamentals, Algorithms & Applications | CMU Graduate Course

Carnegie-Mellon University

Comprehensive introduction to convex optimization, covering theory, algorithms, and practical applications for graduate students in machine learning, statistics, and related fields.

University CoursesMachine Learning

Introduction

This course provides a comprehensive introduction to convex optimization, covering fundamental theory, algorithms, and applications. It is designed for graduate students in machine learning, statistics, operations research, and related fields.

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Highlights

  • Covers the fundamentals of convex sets and functions, optimization theory, and canonical problem forms
  • Explores first-order optimization methods such as gradient descent, subgradient methods, and proximal gradient descent
  • Discusses advanced topics in convex optimization, including duality, interior-point methods, and semidefinite programming
  • Offers practical experience through homework assignments and programming exercises

Recommendation

This course is highly recommended for graduate students interested in optimization, machine learning, and related fields. It provides a strong theoretical foundation and practical skills for solving a wide range of convex optimization problems.

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