Conic optimization is a subfield of convex optimization that studies the theory and algorithms for solving optimization problems defined by minimizing a convex function over the intersection of an affine subspace and a convex cone. This framework provides a general structure that unifies various important classes of convex problems, including linear, second-order cone, and semidefinite programming, serving as a powerful methodology for modeling and solving problems across diverse application domains.