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Publish a Book Chapter in "Emerging Trends in Mathematics for Optimization, Algorithms and Analytics (Volume - 1)"

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978-1-80433-870-4

Mathematics Edited Book | Edited Book on Mathematics


This edited book on mathematics titled "Emerging Trends in Mathematics for Optimization, Algorithms and Analytics" mainly focuses on various topics such as convex optimization trends, nonconvex optimization, stochastic optimization etc., and the rest are given below in the Scope of the book. This mathematics edited book will be published with ISBN numbers after following a proper double blind peer reviewed process. All the chapters of this mathematics edited book will be published in a proper style, so that reader can easily understand and learn.

Author can download this mathematics edited book titled "Emerging Trends in Mathematics for Optimization, Algorithms and Analytics" authorship responsibility and copyright form: Click Here

Indexed In


Indexed in Crossref Indexed in Dimensions Indexed in Bowker
ISBN978-1-80433-870-4

Invited Topics

  1. Emerging Mathematical Directions in Optimization and Algorithmic Analytics
  2. Convex Optimization Frontiers for Large-Scale Learning Problems
  3. Nonconvex Optimization Landscapes and Convergence Guarantees
  4. Stochastic Gradient Methods and Variance Reduction Techniques
  5. Online Optimization and Regret Analysis in Data Streams
  6. Distributed and Decentralized Optimization in Networked Systems
  7. Federated Optimization: Mathematical Models and Convergence Theory
  8. Multiobjective Optimization for Trade-Off Aware Decision Making
  9. Robust Optimization Under Model Uncertainty and Data Noise
  10. Chance-Constrained Optimization and Risk-Limited Planning
  11. Integer Optimization Advances for Complex Discrete Decision Problems
  12. Mixed-Integer Programming for Modern Analytics Applications
  13. Combinatorial Optimization Methods for Graph and Network Problems
  14. Submodular Optimization and Greedy Methods with Guarantees
  15. Proximal Gradient Algorithms for Composite Objectives
  16. ADMM Variants and Operator Splitting for Large-Scale Problems
  17. Mirror Descent and Bregman Geometry in Optimization
  18. Coordinate Descent and Randomized Block Updates in Practice
  19. Second-Order Methods, Newton Sketching and Curvature Exploitation
  20. Quasi-Newton Methods and Limited-Memory Updates at Scale



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ISBN

ISBN: 978-1-80433-870-4

Book Scope

  • Convex Optimization Trends
  • Nonconvex Optimization
  • Stochastic Optimization
  • Online Optimization
  • Distributed Optimization
  • Federated Optimization
  • Multiobjective Optimization
  • Robust Optimization
  • Chance Constraints
  • Integer Optimization
  • Mixed-Integer Programming
  • Combinatorial Optimization
  • Submodular Optimization
  • Gradient Methods
  • Accelerated Methods
  • Proximal Algorithms
  • ADMM Variants
  • Mirror Descent
  • Coordinate Descent
  • Second-Order Methods
  • Quasi-Newton Methods
  • Optimization on Manifolds
  • Nonsmooth Optimization
  • Variational Inequalities
  • Fixed-Point Algorithms
  • Graph Algorithms
  • Spectral Algorithms
  • Randomized Algorithms
  • Approximation Algorithms
  • Streaming Algorithms
  • Sketching Methods
  • Complexity Bounds
  • Smoothed Analysis
  • Algorithmic Game Theory
  • Mechanism Design Basics
  • Fairness Constraints
  • Differential Privacy
  • Causal Discovery Tools
  • Graphical Models
  • Bayesian Optimization
  • Hyperparameter Search
  • Kernel Methods
  • Convex Relaxations
  • Semidefinite Programming
  • Optimal Transport
  • Topological Data Methods
  • Time Series Analytics
  • Anomaly Detection Math
  • Uncertainty Quantification
  • Benchmarking and Reproducibility


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Deadline

31 Jan 2026