(Sequential) Quadratic Programming

Solving procedures for quadratic programming with optional equality and inequality constraints, which can be used for by sequential quadratic programming (SQP). Similar to Newton-Raphson methods in the unconstrained case, sequential quadratic programming solves non-linear constrained optimization problems by iteratively solving linear approximations of the optimality conditions of such a problem (cf. Powell (1978) ; Nocedal and Wright (1999, ISBN: 978-0-387-98793-4)). The Hessian matrix in this strategy is commonly approximated by the BFGS method in its damped modification proposed by Powell (1978) . All methods are implemented in C++ as header-only library, such that it is easy to use in other packages.