Yes, that's true. But in optimization domain the concept of "convexity" is understood in terms of set, not always from the 2nd derivative of a function. Because you might have search spaces where you are not able to differentiate the objective function at all. In those cases the "convex" means a "convex set".
Sure: you can define convexity for a function that is equivalent to the 2nd derivative definition in the case that the function is twice-differentiable, using only the definition of the convexity of a set.
Define the epigraph of a function to be the set given by {(x, t) | f(x) ≤ t}. Then, we say f is a convex function iff the epigraph is a convex set.
This is equivalent (exercise for the reader!) to the usual definition that a function f is convex iff f((1-t)x + ty) ≤ (1-t)f(x) + tf(y), for all 0 ≤ t ≤ 1, with x, y in the domain of f.
Note that neither of these two definitions require differentiability (or twice-differentiability), but the definitions are equivalent in this case.[0]
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[0] For proofs of all of these statements see B&V's Convex Optimization.
No need to define 'convex optimization' here, that follows from the definition of a convex function: F(ax + (1-a)y) <= aF(x) + (1-a) F(y) for 0<=a<=1 and x,y in domain of F. For the inequality to be satisfied ax + (1-a)y has to be in the domain. That mean the domain is convex.
