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Problems related to convex functions, their properties, and feasible regions defined by linear constraints. Topics include proving that a function is concave if and only if its negative is convex, the effect of scaling on convex functions, and the definition and properties of feasible directions and step lengths. Students of mathematics, particularly those in calculus or optimization courses, may find this document useful for studying, summarizing, or completing assignments.
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Math 164: Homework #3, due on Wednesday, April 22
[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^.