Now showing items 1-5 of 5

    • Dual Variable Metric Algorithms for Constrained Optimization 

      Han, Shih-Ping (Cornell University, 1975-07)
      We present a class of algorithms for solving constrained optimization problems. In the algorithm non-negatively constrained quadratic programming subproblems are iteratively solved to obtain estimates of Lagrange multipliers ...
    • A Globally Convergent Method for Nonlinear Programming 

      Han, Shih-Ping (Cornell University, 1975-08)
      Recently developd Newton and quasi-Newton methods for nonlinear programming possess only local convergence properties. Adopting the concept of the damped Newton method in unconstrained optimization, we propose a stepsize ...
    • Superlinear Convergence of a Minimax Method 

      Han, Shih-Ping (Cornell University, 1978-02)
      To solve a minimax problem Han [1977b] suggested the use of quadratic programs to find search directions. If the matrices in the quadratic programs are positive definite, the method can be shown convergent globally. In ...
    • Superlinearly Convergent Variable Metric Algorithms for General Nonlinear Programming Problems 

      Han, Shih-Ping (Cornell University, 1975-03)
      In this paper variable metric algorithms are extended to solve general nonlinear programming problems. In the algorithm we iteratively solve a linearly constrained quadratic program which contains an estimate of the ...
    • Variable Metric Methods for Minimizing a Class of Nondifferentiable Functions 

      Han, Shih-Ping (Cornell University, 1977-09)
      We develop a class of methods for minimizing a nondifferentiable function which is the maximum of a finite number of smooth functions. The methods proceed by solving iteratively qquadratic programming problems to generate ...