NEWTON-TYPE CURVILINEAR SEARCH METHOD FOR CONSTRAINED OPTIMIZATION

An algorithm is presented that minimizes a nonlinear function in many variables under equality constraints by generating a monotonically improving sequence of feasible points along curvilinear search paths obeying an initialvalue system of differential equations. The derivation of the differential equations is based on the idea of a steepest descent curve for the objective function on the feasible region. Our method for small stepsize behaves as the generalized reduced gradient algorithm, whereas for large enough stepsize the constrained equivalent of Newton's method for unconstrained minimization is obtained. © 1979.