274 research outputs found
Use of static surrogates in hyperparameter optimization
Optimizing the hyperparameters and architecture of a neural network is a long
yet necessary phase in the development of any new application. This consuming
process can benefit from the elaboration of strategies designed to quickly
discard low quality configurations and focus on more promising candidates. This
work aims at enhancing HyperNOMAD, a library that adapts a direct search
derivative-free optimization algorithm to tune both the architecture and the
training of a neural network simultaneously, by targeting two keys steps of its
execution and exploiting cheap approximations in the form of static surrogates
to trigger the early stopping of the evaluation of a configuration and the
ranking of pools of candidates. These additions to HyperNOMAD are shown to
improve on its resources consumption without harming the quality of the
proposed solutions.Comment: http://www.optimization-online.org/DB_HTML/2021/03/8296.htm
Tight-and-Cheap Conic Relaxation for the Optimal Reactive Power Dispatch Problem
The optimal reactive power dispatch (ORPD) problem is an alternating current
optimal power flow (ACOPF) problem where discrete control devices for
regulating the reactive power, such as shunt elements and tap changers, are
considered. The ORPD problem is modelled as a mixed-integer nonlinear
optimization problem and its complexity is increased compared to the ACOPF
problem, which is highly nonconvex and generally hard to solve. Recently,
convex relaxations of the ACOPF problem have attracted a significant interest
since they can lead to global optimality. We propose a tight conic relaxation
of the ORPD problem and show that a round-off technique applied with this
relaxation leads to near-global optimal solutions with very small guaranteed
optimality gaps, unlike with the nonconvex continuous relaxation. We report
computational results on selected MATPOWER test cases with up to 3375 buses
Tuning a variational autoencoder for data accountability problem in the Mars Science Laboratory ground data system
The Mars Curiosity rover is frequently sending back engineering and science
data that goes through a pipeline of systems before reaching its final
destination at the mission operations center making it prone to volume loss and
data corruption. A ground data system analysis (GDSA) team is charged with the
monitoring of this flow of information and the detection of anomalies in that
data in order to request a re-transmission when necessary. This work presents
-MADS, a derivative-free optimization method applied for tuning the
architecture and hyperparameters of a variational autoencoder trained to detect
the data with missing patches in order to assist the GDSA team in their
mission
Constrained stochastic blackbox optimization using a progressive barrier and probabilistic estimates
This work introduces the StoMADS-PB algorithm for constrained stochastic
blackbox optimization, which is an extension of the mesh adaptive direct-search
(MADS) method originally developed for deterministic blackbox optimization
under general constraints. The values of the objective and constraint functions
are provided by a noisy blackbox, i.e., they can only be computed with random
noise whose distribution is unknown. As in MADS, constraint violations are
aggregated into a single constraint violation function. Since all functions
values are numerically unavailable, StoMADS-PB uses estimates and introduces
so-called probabilistic bounds for the violation. Such estimates and bounds
obtained from stochastic observations are required to be accurate and reliable
with high but fixed probabilities. The proposed method, which allows
intermediate infeasible iterates, accepts new points using sufficient decrease
conditions and imposing a threshold on the probabilistic bounds. Using Clarke
nonsmooth calculus and martingale theory, Clarke stationarity convergence
results for the objective and the violation function are derived with
probability one
Extensions à l'algorithme de recherche directe mads pour l'optimisation non lisse
Revue de la littérature sur les méthodes de recherche directe pour l'optimisation non lisse -- Démarche et organisation de la thèse -- Nonsmooth optimization through mesh adaptive direct search and variable neighborhood search -- Parallel space decomposition of the mesh adaptive direct search algorithm -- Orthomads : a deterministic mads instance with orthogonal directions
Quantifying uncertainty with ensembles of surrogates for blackbox optimization
This work is in the context of blackbox optimization where the functions
defining the problem are expensive to evaluate and where no derivatives are
available. A tried and tested technique is to build surrogates of the objective
and the constraints in order to conduct the optimization at a cheaper
computational cost. This work proposes different uncertainty measures when
using ensembles of surrogates. The resulting combination of an ensemble of
surrogates with our measures behaves as a stochastic model and allows the use
of efficient Bayesian optimization tools. The method is incorporated in the
search step of the mesh adaptive direct search (MADS) algorithm to improve the
exploration of the search space. Computational experiments are conducted on
seven analytical problems, two multi-disciplinary optimization problems and two
simulation problems. The results show that the proposed approach solves
expensive simulation-based problems at a greater precision and with a lower
computational effort than stochastic models.Comment: 36 pages, 11 figures, submitte
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