Feasibility
Consider
the simple problem
Feasibility
requires
for
all k.
RFSQP
generates iterates that satisfy all inequality constraints
and linear equality constraints.
Why
feasibility?
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From
an application point of view:
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Objective
may not be defined if certain constraints are
violated. For example, the steady-state errors of
a dynamical system are undefined if the system is
not stable. Important for real-time applications.
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May
have to terminate the optimization process after a
prescribed amount of time, in which case it may be
crucial that the sub-optimal solution at least
satisfy some hard constraints.
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In
the context of optimal design, tradeoff
exploration cannot meaningfully take place if some
hard constraints are not first satisfied. It is
thus of great interest to produce iterates that
all satisfy these hard constraints.
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From
an algorithmic point of view:
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The
line search criterion can be based on the decrease
of the objective function, i.e., there is no need
for an artifical "merit function".
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In
the SQP context, whenever the current iterate is
feasible, the QP subproblem has a feasible solution. |
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Last modified:
February 21, 2005
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