Linked e-resources
Details
Table of Contents
Intro
Halftitle Page
Series Page
Title Page
Copyright
Contents
About the Authors
1 Convexity and convex optimisation problems
1.1 Convex sets
1.2 Convex functions
1.3 Convex optimisation problems
2 Recognition and classification of convex programming
2.1 Relation to definition
2.2 Relation to derivatives
2.2.1 First-order conditions
2.2.2 Second-order conditions
2.3 Relation to convexity propositions
2.4 Relation to classes of convex programming
2.4.1 Linear programming
2.4.2 Quadratic programming
2.4.3 Second-order cone programming
2.4.4 Geometric programming
2.4.5 Semidefinite programming
2.5 Relation to equality and inequality
3 Convex optimisation for signal processing and wireless communication
3.1 Convex optimisation for signal estimation
3.2 Convex optimisation for resource allocation problems
3.3 Convex optimisation for the problems of scheduling and deployment in wireless networks
3.4 Convex optimisation for emerging wireless network technologies
3.5 Convex optimisation for smart wireless networks
4 Introduction to real-Ưtime embedded optimisation programming
4.1 Concepts of real-time systems
4.1.1 Modelling real-time systems
4.1.2 Real-time dynamic scheduling
4.1.3 Real-time communication
4.1.4 Real-time performance analysis
4.2 Real-time computing
4.3 Real-time embedded systems
4.4 Real-time embedded convex optimisation
4.4.1 Disciplined convex programming
4.4.2 Code generation
5 Introduction to practical optimisation problems
5.1 Stochastic optimisation
5.1.1 Analysis of stochastic optimisation
5.1.2 Characteristics of stochastic optimisation
5.1.3 Popular stochastic algorithms
5.1.4 Stochastic optimisation in wireless communication systems
5.2 Large-scale optimisation
5.2.1 Large-scale unconstrained optimisation
5.2.2 Large-scale constrained optimisation
5.2.3 Large-scale optimisation in the wake of big data
5.2.4 Examples of large-scale optimisation
5.3 Multi-objective optimisation
5.3.1 Definition of multi-objective optimisation
5.3.2 Example of multi-objective optimisation
5.4 Integer programming and combinatorial optimisation
5.4.1 Branch-and-bound methods
5.4.2 Dynamic programming
5.5 Real-time optimisation problems
5.6 Introduction to methodologies of real-time optimisation
6 First-Ưorder methods for real-Ưtime optimisation
6.1 An overview of first-order methods
6.2 Accelerated first-order approaches
6.3 Proximal methods for non-smooth problems
6.4 Stochastic gradient methods
6.5 Applications of first-order optimisation in 5G IoT
7 Distributed and parallel computing for real-Ưtime optimisation
7.1 Introduction to parallel computing
7.2 The role of parallel computing in optimisation
7.3 Parallel first-order optimisation approaches
7.4 Alternating direction method of multipliers
Halftitle Page
Series Page
Title Page
Copyright
Contents
About the Authors
1 Convexity and convex optimisation problems
1.1 Convex sets
1.2 Convex functions
1.3 Convex optimisation problems
2 Recognition and classification of convex programming
2.1 Relation to definition
2.2 Relation to derivatives
2.2.1 First-order conditions
2.2.2 Second-order conditions
2.3 Relation to convexity propositions
2.4 Relation to classes of convex programming
2.4.1 Linear programming
2.4.2 Quadratic programming
2.4.3 Second-order cone programming
2.4.4 Geometric programming
2.4.5 Semidefinite programming
2.5 Relation to equality and inequality
3 Convex optimisation for signal processing and wireless communication
3.1 Convex optimisation for signal estimation
3.2 Convex optimisation for resource allocation problems
3.3 Convex optimisation for the problems of scheduling and deployment in wireless networks
3.4 Convex optimisation for emerging wireless network technologies
3.5 Convex optimisation for smart wireless networks
4 Introduction to real-Ưtime embedded optimisation programming
4.1 Concepts of real-time systems
4.1.1 Modelling real-time systems
4.1.2 Real-time dynamic scheduling
4.1.3 Real-time communication
4.1.4 Real-time performance analysis
4.2 Real-time computing
4.3 Real-time embedded systems
4.4 Real-time embedded convex optimisation
4.4.1 Disciplined convex programming
4.4.2 Code generation
5 Introduction to practical optimisation problems
5.1 Stochastic optimisation
5.1.1 Analysis of stochastic optimisation
5.1.2 Characteristics of stochastic optimisation
5.1.3 Popular stochastic algorithms
5.1.4 Stochastic optimisation in wireless communication systems
5.2 Large-scale optimisation
5.2.1 Large-scale unconstrained optimisation
5.2.2 Large-scale constrained optimisation
5.2.3 Large-scale optimisation in the wake of big data
5.2.4 Examples of large-scale optimisation
5.3 Multi-objective optimisation
5.3.1 Definition of multi-objective optimisation
5.3.2 Example of multi-objective optimisation
5.4 Integer programming and combinatorial optimisation
5.4.1 Branch-and-bound methods
5.4.2 Dynamic programming
5.5 Real-time optimisation problems
5.6 Introduction to methodologies of real-time optimisation
6 First-Ưorder methods for real-Ưtime optimisation
6.1 An overview of first-order methods
6.2 Accelerated first-order approaches
6.3 Proximal methods for non-smooth problems
6.4 Stochastic gradient methods
6.5 Applications of first-order optimisation in 5G IoT
7 Distributed and parallel computing for real-Ưtime optimisation
7.1 Introduction to parallel computing
7.2 The role of parallel computing in optimisation
7.3 Parallel first-order optimisation approaches
7.4 Alternating direction method of multipliers