First year
research advances
Research
Advances
Perturbed
dumped Newton and SQPIP methods for
discretized optimal control problems
Researchers.
V. Ruggiero, S. Bonettini, F. Tinti.
Advances.
The research activity of the first year
started from the initial idea that the
Interior Point (IP) Newton method can be
thought of as an inexact Newton method for
the solution of the KarushKhunTucker (KKT)
nonlinear system with sign constraints on a
subset of the variables.
This remark allows to study the IP method
convergence with respect to the perturbation
parameter, the solver of the inner linear
system arising at each step and the
steplength reduction rules.
From this viewpoint, it is possible to
employ an iterative inner solver that uses
an adaptive stopping rule, which avoids
useless inner iterations when the
iterates are far from the solution and
yields an efficient as well as globally
convergent scheme.
The global convergence as well as the
superlinear local convergence of the inexact
scheme, already known for quadratic
programming (QP) problems, was generalized
in [DR03a] to nonlinear programming (NLP)
problems.
The assumptions for the method convergence
were pointed out in [DR04], as a
generalization of those given by Tapia et
al. for the exact IP Newton scheme.
In particular, by checking one of these
assumptions at the software level, one is
able to automatically emphasize critical
situations where no convergence can be
achieved due to a badly chosen starting
point.
The convergence theory of the inexact Newton
methods can be generalized to include
nonmonotone backtraking strategies [B05].
Hence, given the theoretical analogies with
the IP method, one is allowed to include
these techniques in the IP method itself,
thus improving its efficiency.
For the case of QP problems with a sparse
Hessian and a special structure constraint
matrix (such as in problems arising from the
discretization of linearquadratic optimal
control problems, or from newtwork flow
problems, or from stochastic programming
problems, etc.), a parallel version of the
inexact IP method was developed [DR03b].
For the numerical implementation of this
scheme, the preconditioned conjugate
gradient method seems a suitable inner
solver, both in the normal equations case
and in the indefinite reduced KKT system
case, which is obtainable from the inner
linear system.
In this context, some parallel
preconditioners were identified that are
suitable for linearquadratic optimal
control problems, while interesting
results were obtained on the spectrum of the
resulting indefinite preconditioned reduced
KKT systems [DRZ03,
DR02], that generalize
those obtained by Luksan (1998).
The proposed approach (IP coupled with
generalized PCG, named GIPPCG) was
implemented in parallel FORTRAN for the Cray
T3E and SGI Origin platforms, equipped with
MPI communications routines.
The efficiency and the scalability of this
code was evaluated on asset allocation under
uncertainty problems and on discrete optimal
control problems, sized up to 105
[DR03b].
For more general NLP problems, the
interpretation of the reduced KKT system as
the Lagrange necessary conditions for the QP
problem minimum, suggests to combine the
inexact IP Newton method with the
Hestenes multipliers method
[BGR05].
In this way, each inner solver step requires
solving a positive definite system, whose
dimension is given by the number of the
problem variables, where the powerful
techniques for sparse matrices Cholesky
factorization (by Ng and Peyton) can be
fruitfully employed. In [BGR04]
this approach
is compared with other direct methods known
in the literature. In particular, the
proposed method shows its efficiency in the
solution of NLP problems coming from the
finite difference discretization of
semielliptic optimal control problems (Mittelmann,
Maurer), sized up to 4.104 and at times
unsolved by the well known codes MINOS,
Lancelot and SNOPT.
We analyzed from the numerical viewpoint
the class of projection methods for solving
pseudomonotone variational inequality
problems. We focused on some specific
extragradienttype methods that do not
require differentiability of the operator
and we address particular attention to the
steplength choice. Subsequently, we analyzed
the hyperplane projection methods in which
we construct an appropriate hyperplane which
strictly separates the current iterate from
the solutions of the problem. Finally, in
order to illustrate the effectiveness of the
proposed methods, we reported the results of
a numerical experimentation in (http://dm.unife.it/~tinti).
Future research will still involve the study
of projection methods for solving the
quasivariational inequality problems
proposed by Noor in [A.M. Noor, New Zealand
Journal of Mathematics, 1997.
[Top]
Variable
projection methods: a successful approach to
SVMs and largescale simply constrained QP
Researchers.
L. Zanni, G. Zanghirati.
Advances.
A Generalized Variable Projection method (GVPM)
for Quadratic Programming (QP) was developed,
based on an adaptive alternation of the two
BarzilaiBorwain rules for the steplength
selection [SZZ05a].
Its convergence properties were analyzed
both theoretically and experimentally [SZZ04b].
A parallel version of the GVPM was
implemented on multiprocessor systems, as
the inner QP solver in a decomposition
technique designed to solve largescale QP
problems arising from the training of
support vector machines (SVMs) [SZZ04b].
[Top]
Adaptive
diagonal spacefilling curves: linking
geometric Lipschitz and Bayesian
approaches for nondifferentiable global
optimization
Researchers.
Ya. D. Sergeyev, D. Lera, D. Kvasov.
Advances.
Concerning the "information" class
of the Global Optimization methods:
 sufficient
convergence conditions were
established [MPS02] for a new
multidimensional diagonal algorithm
based on the "local tuning"
technique to adaptively estimate the
Lipschitz local constants;
 an
effective monodimensional method,
which applies the "local tuning"
technique to the objective function
behaviour, was generalized to the
multidimensional case through the
diagonal approach with two different
search domain partitioning
strategies, thus obtaining
geometricallike diagonal methods
whose convergence condition was
proved. The results of a wide
numerical experimentation have
clearly shown that the new methods,
based on the adaptive estimation of
the local Lipschitz constants in
each search subdomain, are better
than the traditional diagonal
optimisation methods [KPS03];
 the
convergence theory was developed for
of a new algorithm, based on
adaptive diagonal curves joining the
ideas of both diagonal methods and
Peano curves. The numerical
experimentation confirms that the
new method advantages increase with
the problem size, as theoretically
predicted [KS03];
 multidimensional
constrained global optimization
problems were considered on a
feasible region, constituted by a
finite number of robust and
nonconvex subregions, where both
the problem objective function and
the problem constraints are
multiextremal functions not
necessarily differentiable, which
satisfy the Lipschitz condition with
unknown Lipschitz constants. To
solve such a problem a new method
was proposed, based on the
spacefilling Peano curves in the
search domain. The convergence
properties of this new method were
obtained and its good performance
was shown by the numerical tests
[SPF03].
Some
innovative global optimization techniques
are discussed in [StS03], including the use of
fractals for the optimization problem size
reduction, the "index scheme" for
constrained optimization problems and the
nonredundant parallel approach to
accelerate the optimum search procedure. New
domain decomposition algorithms for
Lipschitzian functions were considered,
where a new uniform normbased decomposition
technique was introduced, which is able to
adaptively select the subdomains containing
the global optima of a multiextremal
continuous real function on a compact set S,
which is also Lipschitzian with respect to
the maximum norm. A new algorithm was
proposed, based on a combination of a local
minimization technique with a sizereducing
procedure, able to decrease the measure of
the region containing the global optima. By
using the function Lipschitz condition, at
each step k the algorithm selects a
point x(k) from a uniform
probability distribution and constructs a
set sequence S(k) (interval
union) such that the global optimum is not
included. Such a procedure satisfies the
probability convergence conditions [GL02].
Concerning the interval analysis methods:
 a
new method was proposed to construct
monodimensional support functions
which are more powerful with respect
to the number of evaluations, the
selection and the elimination of
search intervals, thus obtaining a
substantial improvement of the
optimisation procedure. A wide
numerical comparison on a large set
of multiextremal test functions has
shown that the new technique is in
average almost twice as fast as the
"interval analysis"
methods currently used for global
optimisation [CGMS03];
 an
effective multidimensional
algorithm was proposed, which uses
the whole information available
during the search procedure to fully
estimate the objective function
behaviour [MCGST04];
 three
new algorithms, which are based on
"interval analysis" and
"branchandbound" global
optimisation approaches, were
proposed to solve the minimal
rootfinding problem for a set of
monodimensional multiextremal
nondifferentiable functions.
The
novelty of these algorithms essentially lies
in improved intervals elimination criteria
and a better choice of the function
evaluations order in both points and
intervals. These techniques accelerate the
search with respect to "interval
analysis" methods traditionally used to
solve this problem [CGS02]. The numerical
solution of the minimal rootfinding problem
is also the subject of [S06b].
Two sets of test functions were introduced
to verify the performance of numerical
methods for Lipschitzian monodimensional
constrained global optimization problems,
where both the objective function and the
constraints can be multiextremal.
Furthermore, both the differentiable and the
nondifferentiable cases were considered.
These test functions were used in the
numerical testing of a Pijavskiitype method
combined with a nondifferentiable penalty
function [FSP02].
It was also proposed a procedure to generate
three test functions classes (differentiable,
continuously differentiable and twice
continuously differentiable) for "blackbox"like
multidimensional global optimisation; this
procedure was also implemented in a package
of C routines [GKLS03].
The
computational complexity of local and global
minimization algorithms for real functions
was studied. In particular, the complexity
of "local search"based "multistart"type
algorithms for global minimization of real
functions on compact sets was considered: in
this context, it was proven that the number
of function evaluations needed to
solve the problem up to a given accuracy
depends linearly on the problem size, as
well as on other parameters connected to the
objective function. Particular attention was
devoted to a new definition by Stephens and
Baritompa (1998) on the "local
information" that an algorithm uses
during its execution: three new definitions
were then given of "local information",
"global information" and "function
structure information" [GL07,
GL05].
[Top]
Symbolic
calculus and parallel computing for error
propagation automatic control
Researchers.
G. Spaletta.
Advances.
An analysis is proposed on the feasibility
for the software control of the error
propagation, by employing the arbitrarily
high precision available in symbolic
calculus systems, toghether with the large
computing and memory resources of parallel
architectures.
A study and an implementation of tools are
considered, to analyze and improve the
stability features of available methods,
within a particular computer algebra
environment, that of Mathematica. A
verification is looked at on the possibility
of employing "Significance Arithmetic"
in a more and more complicated flow of
computations. The focus is on the numerical
geometric integration of differential
equations: within the mentioned environment,
known or novel methods are implemented
according to criteria that allow, inside
such a framework, the error propagation
control and thus the methods stability.
The geometric integration of differential
equations is originally designed, and it is
meeting an increasing interest among
resaerchers, to answer the quest for a
solution that mantains the flow qualitative
properties. Many functionalities have been
and are developped, in Mathematica, for the
analysis and the automatic derivation of
numeric solvers of high order, that retain
certain stability characteristics. The
numeric, symbolic, graphics and parallel
capabilities of Mathematica are exploited.
In [SS02a] a new orthogonal projection method
is described, that solves differential
systems retaining orthonormality, while in
[SS02b] a solver is presented that is suitable
to separable Hamiltonian systems. In both
cases the finite representation of "Significance
Arithmetic" is employed, as it is also
in [SS03a], in which algorithms to solve
structured differential systems are
implemented via a novel "increment"
formulation, combined with the known
technique of "compensated sum", in
order to automatically control and minimize
the error propagation.
[Top]
Parallel
domain decomposition techniques for
spacevariant blurred images restoration
Researchers.
F. Zama, E. Loli Piccolomini, G. Landi, M.
Bertaja.
Advances.
Linear and nonlinear iterative
regularization methods was studied. An
efficient descent algorithm for the
computation of both the solution and the
regularization parameter of the Tikhonov
method is proposed in [ZL05]. A "Total
Variation"based iterative
regularization method is studied in [LL05]
for the dynamic magnetic resonance image
reconstruction problem.
[Top]
A
parallel computing approach to dynamic
magnetic resonance imaging
Researchers.
F. Zama, E. Loli Piccolomini, G. Landi, M.
Bertaja.
Advances.
The domain decomposition method was
applied in a parallel MATLAB environment for
the reconstruction of row blocks in
sequences of dynamic magnetic resonance
images [LLZ03], using open source software for
PCLinux clusters.
Dissemination
and Training Activities
V.
Ruggiero, Analysis of the Newton Inexact
InteriorPoint Method for Large Scale
Nonlinear Optimization Problems,
tutorial at the INdAM scientific meeting
"OPT2003  Numerical Methods for Local
and Global Optimization: Sequential and
Parallel Algorithms", Cortona (Italy),
July 1420, 2003.
Ya.D.
Sergeyev, Nonredundant parallelism and
adaptive schemes for univariate and
multivariate Lipschitz global optimization
problems, tutorial at the INdAM
scientific meeting "OPT2003  Numerical
Methods for Local and Global Optimization:
Sequential and Parallel Algorithms",
Cortona (Italy), July 1420, 2003.
L.
Zanni, Metodi del gradiente proiettato
per l'Ottimizzazione non lineare, 20
hours minicourse for the Mathematics
Doctorate, University of Modena and Reggio
Emilia (Italy), Springer 2003.

