1,491 research outputs found
Fitting IVIM with Variable Projection and Simplicial Optimization
Fitting multi-exponential models to Diffusion MRI (dMRI) data has always been
challenging due to various underlying complexities. In this work, we introduce
a novel and robust fitting framework for the standard two-compartment IVIM
microstructural model. This framework provides a significant improvement over
the existing methods and helps estimate the associated diffusion and perfusion
parameters of IVIM in an automatic manner. As a part of this work we provide
capabilities to switch between more advanced global optimization methods such
as simplicial homology (SH) and differential evolution (DE). Our experiments
show that the results obtained from this simultaneous fitting procedure
disentangle the model parameters in a reduced subspace. The proposed framework
extends the seminal work originated in the MIX framework, with improved
procedures for multi-stage fitting. This framework has been made available as
an open-source Python implementation and disseminated to the community through
the DIPY project
International Conference on Continuous Optimization (ICCOPT) 2019 Conference Book
The Sixth International Conference on Continuous Optimization took place on the campus of the Technical University of Berlin, August 3-8, 2019. The ICCOPT is a flagship conference of the Mathematical Optimization Society (MOS), organized every three years. ICCOPT 2019 was hosted by the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) Berlin. It included a Summer School and a Conference with a series of plenary and semi-plenary talks, organized and contributed sessions, and poster sessions.
This book comprises the full conference program. It contains, in particular, the scientific program in survey style as well as with all details, and information on the social program, the venue, special meetings, and more
(Global) Optimization: Historical notes and recent developments
Recent developments in (Global) Optimization are surveyed in this paper. We collected and commented quite a large number of recent references which, in our opinion, well represent the vivacity, deepness, and width of scope of current computational approaches and theoretical results about nonconvex optimization problems. Before the presentation of the recent developments, which are subdivided into two parts related to heuristic and exact approaches, respectively, we briefly sketch the origin of the discipline and observe what, from the initial attempts, survived, what was not considered at all as well as a few approaches which have been recently rediscovered, mostly in connection with machine learning
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