6 research outputs found
Supremum-Norm Convergence for Step-Asynchronous Successive Overrelaxation on M-matrices
Step-asynchronous successive overrelaxation updates the values contained in a
single vector using the usual Gau\ss-Seidel-like weighted rule, but arbitrarily
mixing old and new values, the only constraint being temporal coherence: you
cannot use a value before it has been computed. We show that given a
nonnegative real matrix , a and a vector such that , every iteration of
step-asynchronous successive overrelaxation for the problem , with , reduces geometrically the -norm of the current error by a factor that we can compute explicitly. Then,
we show that given a it is in principle always possible to
compute such a . This property makes it possible to estimate the
supremum norm of the absolute error at each iteration without any additional
hypothesis on , even when is so large that computing the product
is feasible, but estimating the supremum norm of
is not
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
Generalizing, Decoding, and Optimizing Support Vector Machine Classification
The classification of complex data usually requires the composition of processing steps. Here, a major challenge is the selection of optimal algorithms for preprocessing and classification. Nowadays, parts of the optimization process are automized but expert knowledge and manual work are still required. We present three steps to face this process and ease the optimization. Namely, we take a theoretical view on classical classifiers, provide an approach to interpret the classifier together with the preprocessing, and integrate both into one framework which enables a semiautomatic optimization of the processing chain and which interfaces numerous algorithms