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This book is a valuable resource for the increasing body of
researchers and practitioners in the field of geospatial
technologies. Written by leading researchers and experts it is
designed in such a way that technical achievements and challenges
of geospatial computing applications are followed by various
applications developed for society. As such, they serve as a bridge
between technologists and solution developers, which is critical in
the context of developing countries. There have been significant
advances in geospatial technologies in India in the last decade,
including advances in spatial data infrastructures, geocomputation
and spatial databases, and innovative applications in natural
resource development. Ranging from LIDAR standards, to data
integration using ontologies, and mobile computing, such progress
enhances the utility of the technology for both urban and rural
development. This book discusses these achievements and considers
the way forward.
Motivation Stochastic Linear Programming with recourse represents
one of the more widely applicable models for incorporating
uncertainty within in which the SLP optimization models. There are
several arenas model is appropriate, and such models have found
applications in air line yield management, capacity planning,
electric power generation planning, financial planning, logistics,
telecommunications network planning, and many more. In some of
these applications, modelers represent uncertainty in terms of only
a few seenarios and formulate a large scale linear program which is
then solved using LP software. However, there are many
applications, such as the telecommunications planning problem
discussed in this book, where a handful of seenarios do not capture
variability well enough to provide a reasonable model of the actual
decision-making problem. Problems of this type easily exceed the
capabilities of LP software by several orders of magnitude. Their
solution requires the use of algorithmic methods that exploit the
structure of the SLP model in a manner that will accommodate large
scale applications."
Sunanda Sen offers an analysis of the ongoing malaise in the world economy, which include the financial and real instability as well as economic recession and lack of development. Rejecting the explanations advanced by the orthodoxy, she deplores the retrograde steps in the interest of high finance. This calls for a change in policies, away from the contractionary monetarist devices and in the direction of demand expansion which will prove mutually beneficial for both advanced and developing countries.
Sunanda Sen offers an analysis of the ongoing malaise in the world
economy, which include the financial and real instability as well
as economic recession and lack of development. Rejecting the
explanations advanced by the orthodoxy, she deplores the retrograde
steps in the interest of high finance. This calls for a change in
policies, away from the contractionary monetarist devices and in
the direction of demand expansion which will prove mutually
beneficial for both advanced and developing countries.
This book summarizes developments related to a class of methods
called Stochastic Decomposition (SD) algorithms, which represent an
important shift in the design of optimization algorithms. Unlike
traditional deterministic algorithms, SD combines sampling
approaches from the statistical literature with traditional
mathematical programming constructs (e.g. decomposition, cutting
planes etc.). This marriage of two highly computationally oriented
disciplines leads to a line of work that is most definitely driven
by computational considerations. Furthermore, the use of sampled
data in SD makes it extremely flexible in its ability to
accommodate various representations of uncertainty, including
situations in which outcomes/scenarios can only be generated by an
algorithm/simulation. The authors report computational results with
some of the largest stochastic programs arising in applications.
These results (mathematical as well as computational) are the tip
of the iceberg'. Further research will uncover extensions of SD to
a wider class of problems. Audience: Researchers in mathematical
optimization, including those working in telecommunications,
electric power generation, transportation planning, airlines and
production systems. Also suitable as a text for an advanced course
in stochastic optimization.
A comprehensive text addressing the high demand for network, cloud,
and content services through cutting-edge research on data pricing
and business strategies Smart Data Pricing tackles the timely issue
of surging demand for network, cloud, and content services and
corresponding innovations in pricing these services to benefit
consumers, operators, and content providers. The pricing of data
traffic and other services is central to the core challenges of
network monetization, growth sustainability, and bridging the
digital divide. In this book, experts from both academia and
industry discuss all aspects of smart data pricing research and
development, including economic analyses, system development, user
behavior evaluation, and business strategies. Smart Data Pricing:
Presents the analysis of leading researchers from industry and
academia surrounding the pricing of network services and content.
Discusses current trends in mobile and wired data usage and their
economic implications for content providers, network operators, end
users, government regulators, and other players in the Internet
ecosystem. Includes new concepts and background technical knowledge
that will help researchers and managers effectively monetize their
networks and improve user quality-of-experience. Provides
cutting-edge research on business strategies and initiatives
through a diverse collection of perspectives. Combines academic and
industry expertise from multiple disciplines and business
organizations. The ideas and background of the technologies and
economic principles discussed within these chapters are of real
value to practitioners, researchers, and managers in identifying
trends and deploying new pricing and network management
technologies, and will help support managers in identifying new
business directions and innovating solutions to challenging
business problems.
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