|
Showing 1 - 3 of
3 matches in All Departments
This volume presents extensive research devoted to a broad spectrum
of mathematics with emphasis on interdisciplinary aspects of
Optimization and Probability. Chapters also emphasize applications
to Data Science, a timely field with a high impact in our modern
society. The discussion presents modern, state-of-the-art, research
results and advances in areas including non-convex optimization,
decentralized distributed convex optimization, topics on
surrogate-based reduced dimension global optimization in process
systems engineering, the projection of a point onto a convex set,
optimal sampling for learning sparse approximations in high
dimensions, the split feasibility problem, higher order embeddings,
codifferentials and quasidifferentials of the expectation of
nonsmooth random integrands, adjoint circuit chains associated with
a random walk, analysis of the trade-off between sample size and
precision in truncated ordinary least squares, spatial deep
learning, efficient location-based tracking for IoT devices using
compressive sensing and machine learning techniques, and nonsmooth
mathematical programs with vanishing constraints in Banach spaces.
The book is a valuable source for graduate students as well as
researchers working on Optimization, Probability and their various
interconnections with a variety of other areas. Chapter 12 is
available open access under a Creative Commons Attribution 4.0
International License via link.springer.com.
Advances in discrete mathematics are presented in this book with
applications in theoretical mathematics and interdisciplinary
research. Each chapter presents new methods and techniques by
leading experts. Unifying interdisciplinary applications, problems,
and approaches of discrete mathematics, this book connects topics
in graph theory, combinatorics, number theory, cryptography,
dynamical systems, finance, optimization, and game theory. Graduate
students and researchers in optimization, mathematics, computer
science, economics, and physics will find the wide range of
interdisciplinary topics, methods, and applications covered in this
book engaging and useful.
This volume presents extensive research devoted to a broad spectrum
of mathematics with emphasis on interdisciplinary aspects of
Optimization and Probability. Chapters also emphasize applications
to Data Science, a timely field with a high impact in our modern
society. The discussion presents modern, state-of-the-art, research
results and advances in areas including non-convex optimization,
decentralized distributed convex optimization, topics on
surrogate-based reduced dimension global optimization in process
systems engineering, the projection of a point onto a convex set,
optimal sampling for learning sparse approximations in high
dimensions, the split feasibility problem, higher order embeddings,
codifferentials and quasidifferentials of the expectation of
nonsmooth random integrands, adjoint circuit chains associated with
a random walk, analysis of the trade-off between sample size and
precision in truncated ordinary least squares, spatial deep
learning, efficient location-based tracking for IoT devices using
compressive sensing and machine learning techniques, and nonsmooth
mathematical programs with vanishing constraints in Banach spaces.
The book is a valuable source for graduate students as well as
researchers working on Optimization, Probability and their various
interconnections with a variety of other areas. Chapter 12 is
available open access under a Creative Commons Attribution 4.0
International License via link.springer.com.
|
You may like...
Loot
Nadine Gordimer
Paperback
(2)
R398
R330
Discovery Miles 3 300
|