MATH 3490

Optimization

(Lab required) This course introduces the theory and practice of optimization. Both unconstrained and constrained problems are considered, as well as continuous and discrete optimization. Topics include linear programming, unconstrained optimization, constrained nonlinear optimization and integer programming. Applications to Statistics and Data Science will be explored. Prerequisites: (one of MATH 2090, MATH 2091, the former MATH 2300, the former MATH 2301, the former MATH 2350, or the former MATH 2352) and (one of MATH 2150, MATH 2151, MATH 2720, MATH 2721, or the former MATH 2750).

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course_MATH_3490 cluster_equiv_1 cluster_equiv_2 MATH 3490 MATH 3490 MATH 2090 MATH 2090 MATH 2090->MATH 3490 MATH 2150 MATH 2150 MATH 2091 MATH 2091 MATH 2091->MATH 2090 or MATH 2150->MATH 3490 MATH 2720 MATH 2720 MATH 2720->MATH 2150 or MATH 2151 MATH 2151 MATH 2151->MATH 2720 or MATH 2721 MATH 2721 MATH 2721->MATH 2151 or

Winter 2020 section

Lecture section

  • A01
    Instructor: Shaun Lui
    CRN: 58360
    Lecture Location: TBA
    Dates: January 06 – April 07
    Time: 10:30–11:20, M.W.F.
    Tutorial Location: TBA
    Dates: January 06 – April 07
    Time: 13:30–14:20, W.