We use the following notation to describe a minimum convex optimization problem:
minimize f0(x) subject to fi(x) ≤ 0, i = 1, . . . ,m hi(x) = 0, i = 1, . . . , p to describe the problem of finding an x that minimizes f0(x) among all x that satisfy the conditions fi(x) ≤ 0, i = 1, . . . ,m, and hi(x) = 0, i = 1, . . . , p.
What is the reason for using fi(x)<=0 and hi(x)=0.
How do someone arrived at only these two constraints?