Linear And Integer Programming Made Easy
by Andrew B. Kahng /
2016 / English / PDF
2.5 MB Download
This textbook provides concise coverage of the basics of linear and
integer programming which, with megatrends toward optimization,
machine learning, big data, etc., are becoming fundamental toolkits
for data and information science and technology. The authors’
approach is accessible to students from almost all fields of
engineering, including operations research, statistics, machine
learning, control system design, scheduling, formal verification
and computer vision. The presentations enables the basis for
numerous approaches to solving hard combinatorial optimization
problems through randomization and approximation.
This textbook provides concise coverage of the basics of linear and
integer programming which, with megatrends toward optimization,
machine learning, big data, etc., are becoming fundamental toolkits
for data and information science and technology. The authors’
approach is accessible to students from almost all fields of
engineering, including operations research, statistics, machine
learning, control system design, scheduling, formal verification
and computer vision. The presentations enables the basis for
numerous approaches to solving hard combinatorial optimization
problems through randomization and approximation.
Readers will learn to cast various problems that may arise in
their research as optimization problems, understand the cases
where the optimization problem will be linear, choose appropriate
solution methods and interpret results appropriately.
Readers will learn to cast various problems that may arise in
their research as optimization problems, understand the cases
where the optimization problem will be linear, choose appropriate
solution methods and interpret results appropriately.
