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Grades: 316
Median GPA: B
Mean GPA: 2.661
2.3
Professor rating
4.1
Difficulty
22
Ratings given
31%
Would take again
Convex Optimization
EEGR 6397
Erik Jonsson School of Engineering and Computer Science
Introduction to convex optimization, with a focus on recognizing and solving convex optimization problems that arise in applications. Convex sets, convex functions, operations preserving convexity, convex optimization problems, quasi-convex, linear, and quadratic optimization, geometric and semi-definite programming, generalized inequalities, vector optimization, the Lagrange dual problem, optimality conditions, sensitivity analysis, applications in approximation and fitting, statistical estimation, and geometric problems, overview of numerical linear algebra, descent methods, Newton's method, handling equality constraints, introduction to interior point methods. 3 credit hours.
Offering Frequency: Based on student interest and instructor availability
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Grades: 316
Median GPA: B
Mean GPA: 2.661
2.3
Professor rating
4.1
Difficulty
22
Ratings given
31%
Would take again
Convex Optimization
EEGR 6397
Erik Jonsson School of Engineering and Computer Science
Introduction to convex optimization, with a focus on recognizing and solving convex optimization problems that arise in applications. Convex sets, convex functions, operations preserving convexity, convex optimization problems, quasi-convex, linear, and quadratic optimization, geometric and semi-definite programming, generalized inequalities, vector optimization, the Lagrange dual problem, optimality conditions, sensitivity analysis, applications in approximation and fitting, statistical estimation, and geometric problems, overview of numerical linear algebra, descent methods, Newton's method, handling equality constraints, introduction to interior point methods. 3 credit hours.
Offering Frequency: Based on student interest and instructor availability
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