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| Convex Optimization and Approximation -- Electrical Engineering (EL ENG) 227B [3 units] | ||||
| Course Format: Three hours of lecture per week. | ||||
| Prerequisites: 227A or consent of instructor. | ||||
| Description: Convex optimization as a systematic approximation tool for hard decision problems. Approximations of combinatorial optimization problems, of stochastic programming problems, of robust optimization problems (i.e., with optimization problems with unknown but bounded data), of optimal control problems. Quality estimates of the resulting approximation. Applications in robust engineering design, statistics, control, finance, data mining, operations research. | ||||
| (F) El-Ghaoui |
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| Convex Optimization -- Electrical Engineering (EL ENG) 227BT [4 units] | ||||
| Course Format: Three hours of lecture, two hours of laboratory, and one hour of discussion per week. | ||||
| Prerequisites: Mathematics 54 and Statistics 2 or equivalents. | ||||
| Formerly Electrical Engineering 227A | ||||
| Description: Convex optimization is a class of nonlinear optimization problems where the objective to be minimized, and the constraints, are both convex. The course covers some convex optimization theory and algorithms, and describes various applications arising in engineering design, machine learning and statistics, finance, and operations research. The course includes laboratory assignments, which consist of hands-on experiments with the optimization software CVX, and a discussion section. | ||||
| (F,SP) El Ghaoui, Wainwright |
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| Convex Optimization and Approximation -- Electrical Engineering (EL ENG) C227B [3 units] | ||||
| Course Format: Three hours of lecture per week. | ||||
| Prerequisites: Electrical Engineering 227A or Industrial Engineering and Operations Research C227A/Electrical Engineering C227A, or consent of instructor. | ||||
| Description: Convex optimization as a systematic approximation tool for hard decision problems. Approximations of combinatorial optimization problems, of stochastic programming problems, of robust optimization problems (i.e., with optimization problems with unknown but bounded data), of optimal control problems. Quality estimates of the resulting approximation. Applications in robust engineering design, statistics, control, finance, data mining, operations research. Also listed as Industrial Engin and Oper Research C227B. | ||||
| (F,SP) El Ghaoui |
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