[ont.events] UW Num. Anal. Semi., Dr. Overton on "Projected Hessian Updating Algorithms for Nonlinearly Constrained Optimization"

mwang@watmath.UUCP (mwang) (02/23/84)

_D_E_P_A_R_T_M_E_N_T _O_F _C_O_M_P_U_T_E_R _S_C_I_E_N_C_E
_U_N_I_V_E_R_S_I_T_Y _O_F _W_A_T_E_R_L_O_O
_S_E_M_I_N_A_R _A_C_T_I_V_I_T_I_E_S

_N_U_M_E_R_I_C_A_L _A_N_A_L_Y_S_I_S _S_E_M_I_N_A_R
                           - Friday, March 2, 1984.

Dr. M.L. Overton of Courant Institute, New York Univer-
sity,  will speak on ``Projected Hessian Updating Algo-
rithms for Nonlinearly Constrained Optimization.''

TIME:                2:30 PM  (Please Note)

ROOM:              MC 6091A

ABSTRACT

Consider the nonlinearly constrained optimization prob-
lem:

      min f(x) subject to c(x) = 0 , i = 1,...,m.

where the minimization is over x in R  and  where  f(x)
and the {c(x)} are smooth functions.

We first discuss several Newton-type methods which have
been  proposed  for solving this problem.  We then con-
sider quasi-Newton methods, which avoid forming Hessian
matrices  but  update  an appropriate approximating ma-
trix.  The standard method updates an approximation  to
the  full  Hessian of the Lagrangian function.  We sug-
gest a new Broyden-type method which was  suggested  by
the  work of Goodman.  This method updates an n x (n-m)
matrix which approximates a ``one-sided projected  Hes-
sian''  of a Lagrangian function.  The method is easily
shown to have a local  one-step  Q-superlinear  conver-
gence  property.   We  also  give  a  new  proof of the
Boggs-Tolle-Wang necessary and sufficient condition for
Q-superlinear  convergence  of  a class of quasi-Newton
methods for solving this problem.  Finally, we describe
an  algorithm  which  updates  an  approximation  to  a
``two-sided projected Hessian,'' a symmetric matrix  of
order n-m which can be expected to be positive definite
near a solution.  This idea was suggested  in  1978  by
Murray and Wright and a particular variant has recently
been analysed by Coleman and Conn.  We present  several
new variants of this algorithm and show that under cer-
tain conditions they  all  have  a  local  two-step  Q-
superlinear  convergence property, even though only one
set of gradients is evaluated per iteration.

                   February 23, 1984