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This course concentrates on recognizing and solving convex optimization problems that arise in applications. The syllabus includes: 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; interior-point methods; applications to signal processing, statistics and machine learning, control and mechanical engineering, digital and analog circuit design, and finance.

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You should have good knowledge of linear algebra and exposure to probability. Exposure to numerical computing, optimization, and application fields is helpful but not required; the applications will be kept basic and simple. You will use matlab and CVX to write simple scripts, so some basic familiarity with matlab is helpful. We will provide some basic Matlab tutorials. Get personalized course recommendations, track subjects and courses with reminders, and more.

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A Course on Convex Geometry

Overview Related Courses Reviews. Overview This course concentrates on recognizing and solving convex optimization problems that arise in applications. Taught by Stephen Boyd. Tags usa. University of Melbourne Discrete Optimization via Coursera. Browse More Mathematics courses. I suggest this course mainly to people that have at least a Bachelor degree in engineering field. Was this review helpful to you? This is an amazing course. Teaches the theory behind and to solve numerically convex optimization problems.

Hws are solved writing progams in Matlab making use of the cvx library developed by Prof. Elliptope and Fantope. Euclidean Distance Matrices. EDM cone faces. Extreme Directions. Face Recognition. Farkas Lemma. Fermat point. Fifth Metric Property. Jensen's Inequality. Jobs in Optimization. Kissing Number. Harold W.

  1. Spatial Pattern in Plankton Communities.
  2. Development of the Nervous System.
  3. Convex Optimization - Convex Geometry.

Linear Algebra. Linear Matrix Inequality.

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  • Proximity Problems. Quasiconvex Functions. Rank Constraint. Justin Romberg. Michael Saunders. Schoenberg Criterion. Semidefinite Programming.