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EE364A: Convex Optimization I (Stanford Univ.). Taught by Professor Stephen Boyd, this course concentrates on recognizing and solving convex optimization problems that arise in engineering.

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English [CC]

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Description

Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Interiorpoint methods. Applications to signal processing, control, digital and analog circuit design, computational geometry, statistics, and mechanical engineering. (from see.stanford.edu)

Course content

  • Lecture 01 – Introduction Unlimited
  • Lecture 02 – Guest Lecturer: Jacob Mattingley, Convex Sets and Their Applications Unlimited
  • Lecture 03 – Logistics, Convex Functions, Examples Unlimited
  • Lecture 04 – Quasiconvex Functions, Examples, Log-Concave and Log-Convex Functions Unlimited
  • Lecture 05 – Optimal and Locally Optimal Points, Convex Optimization Problem Unlimited
  • Lecture 06 – Linear-Fractional Program, Quadratic Program Unlimited
  • Lecture 07 – Generalized Inequality Constraints, Semidefinite Program (SDP) Unlimited
  • Lecture 08 – Lagrangian, Least-Norm Solution Of Linear Equations, Dual Problem Unlimited
  • Lecture 09 – Complementary Slackness, Karush-Kuhn-Tucker (KKT) Conditions, Sensitivity Unlimited
  • Lecture 10 – Applications: Norm Approximation, Penalty Function Approximation, … Unlimited
  • Lecture 11 – Statistical Estimation, Maximum Likelihood Estimation Unlimited
  • Lecture 12 – Geometric Problems Unlimited
  • Lecture 13 – Linear Discrimination, Nonlinear Discrimination, Numerical Linear Algebra Unlimited
  • Lecture 14 – Numerical Linear Algebra Background, Factorizations Unlimited
  • Lecture 15 – Algorithm – Unconstrained Minimization Unlimited
  • Lecture 16 – Unconstrained Minimization (cont.), Equality Constrained Minimization Unlimited
  • Lecture 17 – Equality Constrained Minimization (cont.), Interior-Point Methods Unlimited
  • Lecture 18 – Logarithmic Barrier, Central Path, Barrier Method, Feasibility Unlimited
  • Lecture 19 – Interior-Point Methods, Barrier Method (Review), Generalized Inequalities Unlimited

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