socp
SOCP, or second-order cone programming, is a class of convex optimization problems characterized by linear objectives and constraints that include second-order (quadratic) cone inequalities. It sits between linear programming and semidefinite programming in expressiveness and is widely used for problems involving Euclidean norms and distances.
A standard form of an SOCP minimizes a linear objective subject to linear equalities and a set
SOCPs are solvable in polynomial time by interior-point methods and can handle large-scale, sparse problems efficiently.
Applications span engineering, finance, and data science. Notable areas include signal processing, control and robotics, wireless
Most SOCPs can be solved by modern optimization software, often by reformulating them into conic or SDP