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Grades: 442
Median GPA: B
Mean GPA: 2.991
4.5
Professor rating
3.6
Difficulty
9
Ratings given
88%
Would take again
Convex Optimization in Systems and Controls
MECH 6327
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, functions, and optimization problems. Basic convex analysis. Least-squares, linear and quadratic programs, second-order cone programs, semidefinite programming. Optimality conditions, duality theory, theorems of alternative, and applications. Descent and interior-point methods. Applications in systems and control, including trajectory optimization, model predictive control, stability and control design via linear matrix inequalities, and semialgebraic techniques. 3 credit hours.
Offering Frequency: Based on student interest and instructor availability
Grades: 17
Median GPA: A
Mean GPA: 3.784
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Grades: 442
Median GPA: B
Mean GPA: 2.991
4.5
Professor rating
3.6
Difficulty
9
Ratings given
88%
Would take again
Convex Optimization in Systems and Controls
MECH 6327
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, functions, and optimization problems. Basic convex analysis. Least-squares, linear and quadratic programs, second-order cone programs, semidefinite programming. Optimality conditions, duality theory, theorems of alternative, and applications. Descent and interior-point methods. Applications in systems and control, including trajectory optimization, model predictive control, stability and control design via linear matrix inequalities, and semialgebraic techniques. 3 credit hours.
Offering Frequency: Based on student interest and instructor availability
Grades: 17
Median GPA: A
Mean GPA: 3.784
Click a checkbox to add something to compare.
