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Convex Optimization MCQ Questions & Answers

Convex Optimization MCQs : This section focuses on the "Basics" of Convex Optimization. These Multiple Choice Questions (MCQs) should be practiced to improve the Convex Optimization skills required for various interviews (campus interview, walk-in interview, company interview), placement, entrance exam and other competitive examinations.

Question 1

___________ is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets.

A. Convex cosmology

B. Convex networks

C. Convex optimization

D. Convex circuits

Question 2

Linear Programming also called Linear Optimization.

A. TRUE

B. FALSE

C. Can be true or false

D. Can not say

Question 3

The basic nature of Linear Programming is to ________________ an objective function with subject to some constraints

A. maximize

B. minimize

C. maximize or minimize

D. None of the above

Question 4

Which of the following conditions which are imposed on the model?

A. find the constraints

B. find the vertices of the feasible region

C. find the value of the objective function at these vertices

D. All of the above

Question 5

A norm is a function that gives a strictly _______ value to a vector or a variable.

A. No

B. Positive

C. Negative

D. None of the above

Question 6

_________ is a function which gives a scalar to a pair of vectors.

A. Inner product

B. Outer product

C. Both A and B

D. No product

Question 7

The union of two convex sets _________ convex.

A. may be

B. may not be

C. may or may not be

D. None of the above

Question 8

The intersection of two convex sets is always __________.

A. concave

B. convex

C. Both A and B

D. None of the above

Question 9

Empty and singleton sets are both affine and convex set.

A. Yes

B. No

C. Can be yes or no

D. Can not say

Question 10

The __________ of a set of points in S is the boundary of the smallest convex region that contain all the points of S inside it or on its boundary.

A. Affine set

B. Convex set

C. Convex cones

D. Convex hull