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5.0
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5.0
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1,000
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99%
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| Name | Grades | Rating | |||
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Not teaching in Spring 2026 | |||||
SYSM 6305 Justin Ruths | |||||
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| Name | Grades | Rating | |||
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Not teaching in Spring 2026 | |||||
SYSM 6305 Justin Ruths | |||||
Grades: 495
Median GPA: B
Mean GPA: 2.776
2.8
Professor rating
4.8
Difficulty
20
Ratings given
45%
Would take again
Optimization Theory and Practice
SYSM 6305
Erik Jonsson School of Engineering and Computer Science
Basics of optimization theory, numerical algorithms, and applications. The course is divided into three main parts: linear programming (simplex method, duality theory), unconstrained methods (optimality conditions, descent algorithms and convergence theorems), and constrained minimization (Lagrange multipliers, Karush-Kuhn-Tucker conditions, active set, penalty and interior point methods). Applications in engineering, operations, finance, statistics, etc. will be emphasized. Students will also use Matlab's optimization toolbox to obtain practical experience with the material. 3 credit hours.
Offering Frequency: Each year
Grades: 45
Median GPA: A
Mean GPA: 3.719
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Grades: 495
Median GPA: B
Mean GPA: 2.776
2.8
Professor rating
4.8
Difficulty
20
Ratings given
45%
Would take again
Optimization Theory and Practice
SYSM 6305
Erik Jonsson School of Engineering and Computer Science
Basics of optimization theory, numerical algorithms, and applications. The course is divided into three main parts: linear programming (simplex method, duality theory), unconstrained methods (optimality conditions, descent algorithms and convergence theorems), and constrained minimization (Lagrange multipliers, Karush-Kuhn-Tucker conditions, active set, penalty and interior point methods). Applications in engineering, operations, finance, statistics, etc. will be emphasized. Students will also use Matlab's optimization toolbox to obtain practical experience with the material. 3 credit hours.
Offering Frequency: Each year
Grades: 45
Median GPA: A
Mean GPA: 3.719
Click a checkbox to add something to compare.