1. science
2. mathematics
3. algebra

A robust combined trust region–line search
exact penalty structured approach for
constrained nonlinear least squares problems
N. Mahdavi-Amiri
Sharif University of Technology
Tehran, Iran
M. R. Ansari
IKI University
Qazvin, Iran
The 8th International Conference of Iranian Operations
Research Society
Outline
•
•
•
•
•
•
•
•
•
•
Problem description and structure
Exact penalty method
Trust region and line search methods
Step directions: trust region projected quadratic subproblems
Penalty parameter updating strategy
Projected structured Hessian approximation
Special nonsmooth line search
Computational results
Conclusions
References
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Problem description and structure
Constrained NonLinear Least Squares Problem (CNLLSP):
Objective function
Equality constraints
Inequality constraints
Functional
constraints
where,
,
and
,
: (smooth functions)
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Notations:
(Transpose of Jacobian)
(Gradient of objective function)
(Hessian of objective function)
where,
1
2
Note:
By knowing
, we can conveniently compute
and part 1 of the Hessian
.
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Exact penalty method
Aim:
CNLP
UCNLP
Continuously differentiable:
• Fletcher (1973): equality constrained.
• Glad and Polak: (1979) equality and inequality
constrained.
Nondifferentiable:
• Zangwill: (1967).
• Pietrzykowski: (1969).
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Some attractive features:
• They overcome difficulties posed by degeneracy.
• They overcome difficulties posed by inconsistent constraint
linearization.
• They are successful in solving certain classes of problems
in which standard constraint qualifications do not hold.
• They are used to ensure feasibility of subproblems and
improve robustness.
Coleman and Conn (1982): CNLP implementation
Mahdavi-Amiri and Bartels (1989): CNLLS implementation
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Pertinent to our scheme:
• Dennis, Gay and Welsh (1981): structured DFP update for
UNLP and for UNLLS, in particular.
• Nocedal and Overton (1985): quasi-Newton update of
projected Hessian for CNLP.
• Mahdavi-Amiri and Bartels (1989): projected structured
update for CNLLS.
• Dennis and Walker (1981): local Q-superlinear convergence
for the structured PSB and DFP methods, in general, and for
UNLLS, in particular.
• Dennis, Martinez and Tapia (1989): structured principle, and
local Q-superlinear convergence of structured BFGS update
for UNLP.
• Mahdavi-Amiri and Ansari (2012): Global convergent exact
penalty projected structured Hessian updating schemes.
• Mahdavi-Amiri and Ansari (2013): Local superlinearly
convergent exact penalty projected structured Hessian
updating schemes.
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-exact penalty function
(violation adds penalty)
where,
(1-norm of the
constraint violations)
Note: There exists fixed
for any
:
,
local minimizer of
local minimizer of
(CNLP)
Note:
is not differentiable everywhere.
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“ -active’’ merit function:
where,
( -violated equalities)
( -violated inequalities)
Notes:
1)
2)
is differentiable in a neighborhood of
.
,
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More notations:
• active equality constraint indices:
• active inequality constraint indices:
• active constraint indices:
• active constraints gradient matrix:
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First-order necessary conditions: If
of
that
then there exist “multipliers’’
(a)
is a stationary point:
(b)
is in-kilter:
is a local minimizer
, for
, such
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Second-order sufficiency conditions: If first-order necessary
conditions are satisfied and
then
is an isolated local minimizer of
.
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Using QR decomposition of
,
where
: basis for
: basis for
,
,
,
and
as identities,
(projected or reduced
gradient)
stationarity
Note:
Computationally,
is accepted.
for a small
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Regions of interest: (
, stationary tolerance parameter)
Multiplier estimates in Region 2:
using QR decomposition of
.
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Trust Region and Line Search Methods
Trust region: for smooth functions
•
are reliable and robust.
•
•
can be applied to ill-conditioned problems.
•
can be used for both convex and non-convex
approximate models.
have strong global convergence properties.
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Line search for nonsmooth exact penalty function
Near a nondifferentiable point the quadratic subproblem cannot
approximate
properly in a large interval (this may happen
in any iterate, whenever
moves to a point where an inactive
constraint becomes active). Therefore, the algorithm may have
some inadequate iterations.
To overcome this difficulty, we propose a special line search
strategy that makes use of the structure of least squares and
examines all nondifferentiable points in the trust region along a
given descent direction h for a proper minimizer of
.
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Step Directions:
1) global step direction:
.
a) global horizontal step direction:
b) global vertical step direction:
2) dropping step direction:
.
(Region 1)
.
.
3) Newton step direction:
.
a) asymptotic horizontal step direction:
b) vertical step direction:
. (Region 2)
.
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Use of step direction:
Region 1:(
)
compute
;
perform a line search on
update :
;
to get
Region 2:(
)
compute ,
;
if
is out-of-kilter for any
then
compute dropping step direction ;
perform a line search on
to get
update :
;
else (the
are in-kilter)
compute Newton step direction
;
update :
(no line search).
;
;
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Trust Region Projected Quadratic Subproblems
Computing horizontal steps: (global and local)
Define
(approximation of projected Hessian)
exact part
(quadratic subproblem)
(
is trust region radius and
a prescribed constant)
is
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Region 1 (global)
Compute
and set
: use
,
,
Region 2 (local)
Compute
: use estimated , and set
.
.
Solution of this subproblem is shown by
.
Computing vertical steps: enforce activity
A Newton iterate
Notes:
• stands for
or .
• if necessary, we cut back
to ensure that
.
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Computing dropping step:
1) Find an index
2) Solve:
where,
is the
corresponding to most out-of-kilter
,
th unit vector and
Note:
•
•
gives a local, first-order decrease in
, and
moves the
away from zero up to the first-order.
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Quadratic subproblems:
1) When
is
feasible:
(local
quadratic
subproblem)
which is a penalty parameter free quadratic subproblem
(thus, no computational difficulties in the local phase).
2) When
:
(auxiliary quadratic
subproblem)
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Note: The last auxiliary quadratic subproblem
determines the optimal improvement in quadratic feasibility
achievable inside the trust region.
helps to balance the progress towards both optimality and
feasibility in the exact penalty approach.
Solution of auxiliary subproblem is shown by
Byrd, Nocedal and
Waltz (2008)
Here
.
Linear subproblem
Quadratic subproblem
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Penalty parameter updating strategy
Essential for practical success (robustness) in the context of
penalty methods:
too small a
a
too large
inefficient behavior and slow
convergence
possible convergence to an infeasible
stationary point.
Note:
We allow the possibility of both increasing (at times with a
safeguard) or decreasing value of . .
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a weak progress towards feasibility by
in comparison with the optimal
achievable reduction predicted by
a very good progress towards feasibility
by
, but a poor improvement in
optimality
Note:
•
•
decrease
(improve
feasibility)
increase
(improve
optyimality)
is updated
only in Region 1 (global phase), and
when,
(that is,
is not -feasible).
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Notations:
the predicted decrease in the objective function
:
the predicted improvement in measure of infeasibility:
the predicted decrease in
:
best predicted improvement in measure of
infeasibility achievable inside the trust region
predicted improvement in measure of
infeasibility
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Reduce
: (e.g.,
)
is reduced (if necessary) to satisfy followings:
1) predicted reduction in infeasibility of constraints is a fraction
of the best possible predicted reduction; that is,
(e.g.,
)
2) penalty function provides a sufficient improvement of feasibility;
that is,
(e.g.,
)
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Increase
: (e.g.,
)
In practice (on a few problems), to ensure satisfaction of (1) and
(2) above,
may become too small too quickly.
Conditions for such an occurrence:
• cases (1) and (2) above are satisfied ( does not decrease),
• there is a very good improvement in feasibility; that is,
(e.g.,
)
• objective function is not optimal and could be improved; that is,
• there is a poor improvement in optimality
(e.g.,
)
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Therefore,
while
increase
(e.g.,
)
(e.g.,
)
.
Note:
If the new
does not satisfy (1) and (2), then
back to its previous value.
safeguard
(to avoid attaining too large a
too soon)
is set
increase a limited number of times
at most 2 times in one iteration and
at most 6 times in all the iterations
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Step acceptance
For a trial point
, define
(actual reduction)
Also, for a step direction
=
,
or
, define
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Predicted reduction:
• global step:
• dropping step:
• Newton step:
A trial point
,
,
,
is accepted if
and
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Updating the trust region radius:
For current step
:
Note:
Locally (in Region 2):
•
•
.
there exists a fixed
:
.
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Projected structured Hessian approximation
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Notations:
• violated equality constraints gradient matrix:
• violated inequality constraints gradient matrix:
• violated equality vector of signs:
Let
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Assumption: (
)
Region 2 (locally)
Secant condition: Mahdavi-Amiri and Bartels (1989)
where,
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Structured Broyden’s family update: Engels and Martinez: (1991)
Set
BFGS:
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DFP:
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Special nonsmooth Line Search Strategy
(for global or dropping direction)
:a
descent search direction.
: step length along .
Breakpoint : A point where derivative of
new constraint becomes active).
does not exist (when a
Note:
A minimizer exists and a minimum occurs either at a breakpoint,
or at a point where
is zero.
Motivation: Consider the
and the to be linear.
Approximate
using linear approximations of the
denoted by . Let
and the ,
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Derivative of
and
Right and left derivatives of
Similar notations are used for
approximation of
)
( , as a linear
Note:
• Breakpoints occur at zeros of the .
• If
then (by linearity of
gives a breakpoint.
• Breakpoints are computed approximately
using linear approximations.
)
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Strategy:
Examine breakpoints sequentially, and find a minimum
at one of the following situations:
(i) a breakpoint
(Figure 1), where
Figure 1
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(ii) or at a point, where
.
Figure 2
Note:
By the used linearizations, all derivatives are computed
without any new objective or constraint function evaluation.
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Computational considerations
If something goes wrong computationally, then
In region 1 (global
(
)
( to change
large)
or
too small
Failure
In region 2 (asymptotic
too small
otherwise
)
)
Failure
(to have
, and
transfer to global phase)
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Computational results
32 CNLLS problems from Hock and Schittkowski (1981)
number of variables:
number of constraints:
7 CNLLS randomly generated test problems from Bartels
and Mahdavi-Amiri (1986)
number of variables:
number of constraints:
objective function and constraints: all nonlinear,
Lagrangian Hessian at solution: positive definite for problems
1, 4 and 6, and indefinite for problems 2, 3, 5 and 7.
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6 CNLLS problems from Biegler, Nocedal, Schmid and
Ternet: 2000
number of variables:
number of constraints:
objective function and constraints: all nonlinear.
12 CNLLS problems from Luksan and Vlcek (1986)
number of variables:
number of constraints:
objective function and constraints: all nonlinear.
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Comparisons:
6 methods tested by Hock and Schittkowski (1981).
3 algorithms of KNITRO (Byrd, Nocedal and Waltz 2006):
Interior/Direct, Interior/CG, Active-set.
Method of Mahdavi-Amiri and Bartels (1989).
Method of Bidabadi and Mahdavi-Amiri (2013).
Two algorithms provided by ‘fmincon’ in MATLAB.
Features of our code:
Language:
Compiler:
Linear algebra:
Computer:
C++,
Microsoft visual C++ 6.0,
Simple C++ Numerical Toolkit,
x86-based PC, 1667Mhz processor.
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Performance profile:
Means of comparison: Number of objective function
evaluations.
,
Define
: Number
of objective function evaluations in algorithm
problem .
for
: Number of algorithms.
Performance function for algorithm :
,
where
is the total number of problems and
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Legends:
Tr-LS: our combined trust region-line search method.
B: method of Bidabadi and Mahdavi-Amiri.
H: best methods tested by Hock and Schittkowski.
MA: Method of Mahdavi-Amiri and Bartels.
D: Interior/Direct algorithm of KNITRO.
CG: Interior/CG algorithm of KNITRO.
AS: Active-set algorithm of KNITRO.
FM1: Active-set algorithm provided by ‘fmincon’ in MATLAB.
FM2: Interior point algorithm provided by ‘fmincon’ in
MATLAB.
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Conclusions
two-step superlinear rate of convergence
effective new scheme for approximating and
updating projected structured Hessian
practical adaptive scheme for the penalty
parameter updates
efficient combined trust region-line search
strategy help in speeding up the global
iterations
Employment of an effective trust region
radius adjustment
competitive as compared to other methods
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Thank you for your attention!