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Material Type: Assignment; Class: OPTIMIZATION; Subject: Mathematics; University: University of California - Los Angeles; Term: Winter 2005;
Typology: Assignments
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Math 164, Lecture 2, Vese Homework #3, due on Friday, January 27, 2005
Remarks:
[1] Prove that a function f is concave if and only if −f is convex.
[2] Let f be a convex function on the convex set S of Rn. Let k be a nonzero scalar, and define g(x) = kf (x). Prove that if k > 0 then g is a convex function on S, and if k < 0 then g is a concave function of S.
[3] Consider a feasible region S defined by a set of linear constraints
S = {x : Ax ≤ b},
where A is an m × n matrix and b is a column vector. (a) Prove that S is convex. (b) Derive the conditions that must be satisfied by a feasible direction p at a point x ∈ S.
[4] Consider the problem minimize f (x) subject to x 1 + 2x 2 + 3x 3 = 6, x 1 ≥ 0 , x 2 ≥ 0 , x 3 ≥ 0. (a) Find the sets of all feasible directions at points xa = (0, 0 , 2)T^ , xb = (3, 0 , 1)T^ , and xc = (1, 1 , 1)T^. (b) Using (a), verify that p = (3, 0 , −1)T^ is a feasible direction for xc = (1, 1 , 1)T^ ; then find an upper bound on the step length α so that xc + αp is a feasible point, with p = (3, 0 , −1)T^.