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Jorge Nocedal.

Projected Hessian updating algorithms for nonlinearly constrained optimization online

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Choose a convergence tolerance TOL.



Choose ^Qj^Q. where B is a t x n matrix.
Evaluate f^.go.c^.AQ.



Factor A = [Y 7.]



Set k -*- 0.
^peat



Solve \ Py = ~ and 6 > such that if II x^-x^H < e and

II B. - Z (xj.)'W(x^,X j(.)ll < 5, then Algorithm 3.1 is well defined and
'\ -^ x^ at a one-step n-superiinear rate.

Proof : The Jacohian of (2.10) is given by (2.13), which is easilv
seen to beLipschitz continuous at x = x^. Furthermore, (2. 13) reduces
to (2.11) at X = X*, where it is nonsinguiar by Assunption 1 (see the
remarks following (2.12)). The procf follows almost immediately from
the results given by Dennis and More (1977, p. 58). The onlv
modification necessary is to check that the partial Rrovden update,
using the exact A(x) instead of an approximation, retains the
superlinear convergence property. This is easilv verified.

e

One interesting aspect of the oartiai Brovden method is that X
does not appear anywhere in the statement of the algorithm. The
nultlpliers are involved only implicitly, in the form of the Jacobian
(2.11). However, (3. 2d) is not the only possible definition of y^
giving a t X n update with Q-superlinear convergence. In particular,



-21-
it Is clear from the results In the next section that reolacine; (3. 2d)
by any of (4.1d,e or f) will give a Q-superlLnearlv convergent
algorithm. The proof is substantially longer, since these definitions
of y. do not result from applying a Rrovden or partiaJ. Broyden method
to any system of equations such as (2,iO),

Let us now make some more general remarks. We define a method to
be a linearized cons traint quasi-Newton method for solving (l.i) if it
consists of the iteration:



A(x^)^



P =



"zCx^>'fg(x^)'

c(x^)



(3.3)



"■k+l ~ '^k



Xu + P



where we assume only that R ["^^x)) = W(A(x)'} with Z(x) continuously
dif f erentiable in the region of "lltiterest and where 1^%} is any sequence
of t X n matrices. Xfe have from the discussion in !^ection 2 that the
Newton iteration (2.4), or enuivalently (2 . 6) or ( 2. 7) , and the
Newton-type iteration (2.12), are linearized constraint quasi-Newton
methods. The same is true of the methods of Han, Tapia and Powell,
which replace W in (2.4) bv a suitable approximation, and the partial
Brovden nethod given by Algorithm 3.1. Now an iteration of the form
(3.3), which is intended to solve (l.i), may equivaiently be '/iewed as
a method to solve the nonlinear system (2.10). We may therefore apply
the Dennis and More characterization of o-superlinear convergence (see
Dennis and More (1977, p. 51)j and immediately obtain, using (2.11):



Theorem 3.2: Let .\ssunption 1 hold. If a ilnearizeri constraint
auasl-Newton nt; thod (3.3) produces a sequence {x^^} conver0, both oapers suggest using the
°oweli (1978a) 'lodif Ication of the BFGS update to guarantee the pronertv
of inherited positive def initeness. ^^ollowing Powell's results, they
show that under certain conditions, which include the assumptions that
X. -»■ x^ and that the approximation matrices remain bounded, a two-step
O-superlinear rate of convergence is achieved.

Let us examine definitions (a) and (b) more closely. It is



_2q-
cieariy possible to construct examples where H s, II is verv small (or
zero) while U y, II , using any of (d) through (


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Online LibraryJorge NocedalProjected Hessian updating algorithms for nonlinearly constrained optimization → online text (page 2 of 4)