1,004 research outputs found
COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting
We introduce COCO, an open source platform for Comparing Continuous
Optimizers in a black-box setting. COCO aims at automatizing the tedious and
repetitive task of benchmarking numerical optimization algorithms to the
greatest possible extent. The platform and the underlying methodology allow to
benchmark in the same framework deterministic and stochastic solvers for both
single and multiobjective optimization. We present the rationales behind the
(decade-long) development of the platform as a general proposition for
guidelines towards better benchmarking. We detail underlying fundamental
concepts of COCO such as the definition of a problem as a function instance,
the underlying idea of instances, the use of target values, and runtime defined
by the number of function calls as the central performance measure. Finally, we
give a quick overview of the basic code structure and the currently available
test suites.Comment: Optimization Methods and Software, Taylor & Francis, In press,
pp.1-3
COCO: Performance Assessment
We present an any-time performance assessment for benchmarking numerical
optimization algorithms in a black-box scenario, applied within the COCO
benchmarking platform. The performance assessment is based on runtimes measured
in number of objective function evaluations to reach one or several quality
indicator target values. We argue that runtime is the only available measure
with a generic, meaningful, and quantitative interpretation. We discuss the
choice of the target values, runlength-based targets, and the aggregation of
results by using simulated restarts, averages, and empirical distribution
functions
Modeling and Analysis Generic Interface for eXternal numerical codes (MAGIX)
The modeling and analysis generic interface for external numerical codes
(MAGIX) is a model optimizer developed under the framework of the coherent set
of astrophysical tools for spectroscopy (CATS) project. The MAGIX package
provides a framework of an easy interface between existing codes and an
iterating engine that attempts to minimize deviations of the model results from
available observational data, constraining the values of the model parameters
and providing corresponding error estimates. Many models (and, in principle,
not only astrophysical models) can be plugged into MAGIX to explore their
parameter space and find the set of parameter values that best fits
observational/experimental data. MAGIX complies with the data structures and
reduction tools of ALMA (Atacama Large Millimeter Array), but can be used with
other astronomical and with non-astronomical data.Comment: 12 pages, 15 figures, 2 tables, paper is also available at
http://www.aanda.org/articles/aa/pdf/forth/aa20063-12.pd
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