MATH 164
Optimization
Description: Lecture, three hours; discussion, one hour. Enforced requisites: courses 115A, 131A. Not open for credit to students with credit for former Electrical Engineering 136. Fundamentals of optimization. Linear programming: basic solutions, simplex method, duality theory. Unconstrained optimization, Newton method for minimization. Nonlinear programming, optimality conditions for constrained problems. Additional topics from linear and nonlinear programming. P/NP or letter grading.
Units: 4.0
Units: 4.0
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5 of 6
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Most Helpful Review
I really like Professor Vese. She does go over fewer examples than other professors, but if anyone asks a question about the material, she'll gladly go over it again in more depth. Attend her office hours! She's patient, willing to go over the problems, and really wants to make sure you understand what's going on. Her tests and homework are fair. I feel like she has a high level of concern for her students. Plus, she'd thank us for each lecture! :)
I really like Professor Vese. She does go over fewer examples than other professors, but if anyone asks a question about the material, she'll gladly go over it again in more depth. Attend her office hours! She's patient, willing to go over the problems, and really wants to make sure you understand what's going on. Her tests and homework are fair. I feel like she has a high level of concern for her students. Plus, she'd thank us for each lecture! :)
Most Helpful Review
Great professor for optimization: really organized and logical so as long as you're paying attention you won't get lost. He tends to do more of the abstract in class, but the homeworks are half practical. It makes some of the problems tough, but go to office hours and he'll figure out where you're confused and guide you until you get it. His tests are very straightforward. Challenging, but no surprises. I think 5 problems on the midterm and 10 or 12 on the final. Everything on both the midterm and the final were things gone over and emphasized in class. Definitely one of the better math professors at UCLA.
Great professor for optimization: really organized and logical so as long as you're paying attention you won't get lost. He tends to do more of the abstract in class, but the homeworks are half practical. It makes some of the problems tough, but go to office hours and he'll figure out where you're confused and guide you until you get it. His tests are very straightforward. Challenging, but no surprises. I think 5 problems on the midterm and 10 or 12 on the final. Everything on both the midterm and the final were things gone over and emphasized in class. Definitely one of the better math professors at UCLA.
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5 of 6
