Lecture 4/8 - Optimality Conditions and Algorithms in Nonlinear Optimization
Short Course given by Prof. Gabriel Haeser (IME-USP) at Universidad Santiago de Compostela - October/2014. Máster en Matemática Industrial. Lecture 4/8 ...
Mario Martinez
Lecture 24 (part 1): Conditional gradient method
Ryan T
Lecture 2/8 - Optimality Conditions and Algorithms in Nonlinear Optimization
Short Course given by Prof. Gabriel Haeser (IME-USP) at Universidad Santiago de Compostela - October/2014. Máster en Matemática Industrial. Lecture 2/8 ...
Mario Martinez
Prof. Zahr: Integrated Computational Physics and Numerical Optimization
CICS at Notre Dame
Algorithmic Tools for Smooth Nonconvex Optimization
Steve Wright, University of Wisconsin-Madison https://simons.berkeley.edu/talks/steve-wright-10-03-17 Fast Iterative Methods in Optimization.
Simons Institute
ML Lunch (Feb 17): L1 optimization beyond quadratic loss
Title: L1 optimization beyond quadratic loss: algorithms and applications in graphical models, control, and energy systems Speaker: Zico Kolter School of ...
Carnegie Mellon
Worst-case complexity and optimality of methods for smooth optimization – P. Toint – ICM2018
Control Theory and Optimization Invited Lecture 16.5 Worst-case evaluation complexity and optimality of second-order methods for nonconvex smooth ...
Rio ICM2018
4 Sundials C.Woodward
13th DOE ACTS Collection Workshop.
CITRIS
An Introduction to Physics-based Animation Pt.1
ACMSIGGRAPH
Algorithmic Adaptations to Extreme-Scale Computing ǀ David Keyes, KAUST
Presented at the Argonne Training Program on Extreme-Scale Computing 2018. Slides for this presentation are available here: ...
ANL Training
Second Order Machine Learning
A major challenge for large-scale machine learning, and one that will only increase in importance as we develop models that are more and more ...
Rodrigo Silveira
Algorithmic Adaptations to Extreme Scale, David Keyes (King Abdullah University , Saudi Arabia)
Recording of a plenary presentation during the PASC15 Conference. www.pasc15.org Abstract Algorithmic adaptations are required to use anticipated exascale ...
cscsch
Shai Shalev-Schwartz - Efficiently Training Sum-Product Neural Networks - invited talk
Slides: https://sites.google.com/site/nips13greedyfrankwolfe/slides-shalevshwartz.pdf Invited talk at the NIPS 2013 Workshop on Greedy Algorithms, Frank-Wolfe ...
Frank-Wolfe and Greedy Algorithms (NIPS 2013 Workshop)
PyLith Tutorial 2015 Session Ill: Linear Solvers and Preconditioners
2015 Crustal Deformation Modeling Session III Title: Optimizing Solver Parameters Instructors: Brad Aagaard, Charles Williams, Matthew Knepley Download ...
CIG Geodynamics
Floating point
In computing, floating point describes a method of representing an approximation of a real number in a way that can support a wide range of values.
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