I am familiar with LP relaxation for ILP (or IP). Assume we concern with integer minimization problem, which we formalize using ILP; we then relax the ILP into LP and we say that the LP provides a lower bound for the ILP. Why is this correct?
I do understand that a feasible solution for the ILP is a feasible solution to the LP, and the reversed is not always so, i.e. a feasible solution for the LP is not necessarily a feasible solution for the ILP.
Can one please point out and explain briefly why it is so?
As you say, a feasible solution for the ILP is a feasible solution for the LP. So if the LP has an optimal solution with objective value $\alpha$, this implies there is no feasible solution for the LP with objective value $< \alpha$, and in particular no feasible solution for the ILP with objective value $<\alpha$. That's what it means for $\alpha$ to be a lower bound for the ILP.