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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
Invited Topics

  1. Nonsmooth Optimization and Subgradient Methods for Modern Losses
  2. Optimization on Riemannian Manifolds for Low-Rank and Geometric Models
  3. Variational Inequalities and Equilibrium Computation Methods
  4. Fixed-Point Iterations and Contraction Mapping Techniques in Algorithms
  5. Convex Relaxations for Discrete and Nonconvex Problems
  6. Semidefinite Programming and Spectral Relaxations in Analytics
  7. Duality, Sensitivity, and Stability in Optimization Models
  8. Saddle-Point Problems and Primal–Dual Algorithms for Learning
  9. Optimal Transport: Theory, Computation, and Data Applications
  10. Entropic Regularization and Sinkhorn Algorithms for Scalability
  11. Graph Algorithms for Large-Scale Analytics and Network Science
  12. Spectral Algorithms for Clustering and Community Detection
  13. Randomized Algorithms and Probabilistic Guarantees in Computation
  14. Approximation Algorithms and Performance Bounds in Optimization
  15. Streaming Algorithms for Real-Time Analytics and Summarization
  16. Sketching and Dimensionality Reduction for Faster Optimization
  17. Complexity Theory Trends for Optimization and Learning Problems
  18. Smoothed Analysis and Beyond-Worst-Case Algorithm Understanding
  19. Algorithmic Game Theory for Strategic Data and Platform Systems
  20. Mechanism Design Foundations for Incentive-Aware Analytics
  21. Auction Algorithms and Optimization for Modern Marketplaces
  22. Fairness Constraints and Mathematical Formulations in Decision Systems
  23. Optimization Methods for Fair Classification and Ranking
  24. Differential Privacy: Mathematical Guarantees and Optimization Trade-Offs
  25. Private Learning via Convex Optimization and Noise Mechanisms
  26. Causal Discovery Using Graphical Models and Optimization Criteria
  27. Graphical Models, Message Passing, and Variational Methods
  28. Bayesian Optimization for Expensive Black-Box Functions
  29. Hyperparameter Optimization as a Mathematical Search Problem
  30. Kernel Methods and RKHS Optimization for Nonlinear Modeling
  31. Convex Geometry and High-Dimensional Phenomena in Analytics
  32. Concentration Inequalities Supporting Algorithmic Generalization
  33. Regularization Paths and Model Selection via Optimization
  34. Sparsity-Promoting Penalties and Lasso-Type Algorithms
  35. Compressed Sensing and Sparse Recovery Optimization Methods
  36. Matrix Completion and Low-Rank Optimization in Recommender Systems
  37. Tensor Optimization for Multiway Data Analysis
  38. Graph Signal Processing and Optimization on Graph Domains
  39. Time Series Analytics Using Convex and Nonconvex Models
  40. Change-Point Detection via Optimization and Statistical Guarantees
  41. Anomaly Detection Models and Theoretical Thresholds
  42. Uncertainty Quantification and Robust Analytics Pipelines
  43. Risk Measures, CVaR Optimization, and Decision Robustness
  44. Optimization for Reinforcement Learning and Control Problems
  45. Dynamic Programming, Approximate Methods, and Complexity Limits
  46. Bandit Algorithms and Regret-Optimal Decision Making
  47. Online Learning, Mirror Descent, and Adaptive Algorithms
  48. Constrained Optimization in Deep Learning Training
  49. Optimization Landscapes and Implicit Bias in Gradient Methods
  50. Acceleration Techniques in Modern First-Order Methods
  51. Adaptive Gradient Methods: Theory and Practical Behavior
  52. Variance Reduction for Stochastic Optimization at Scale
  53. Distributed Learning Under Communication Constraints
  54. Decentralized Consensus Optimization and Network Effects
  55. Federated Learning with Heterogeneous Data and Systems
  56. Optimization with Quantization and Compression for Edge Devices
  57. Primal–Dual Methods for Large-Scale Constrained Learning
  58. Operator Splitting Methods for Structured Optimization Problems
  59. Nonlinear Programming Advances for Real-World Analytics
  60. Global Optimization and Certified Bounds for Nonconvex Problems
  61. Submodular Optimization in Feature Selection and Summarization
  62. Graph Cuts, Energy Minimization, and Vision Analytics
  63. Optimal Transport for Domain Adaptation and Distribution Shift
  64. Robustness to Distribution Shift Through Mathematical Guarantees
  65. Explainability Through Optimization-Based Attribution Methods
  66. Interpretable Models via Sparsity and Constraint Design
  67. Benchmarking Optimization Algorithms: Metrics and Protocols
  68. Reproducibility Standards in Algorithmic Research
  69. Open-Source Optimization Libraries and Verification Practices
  70. Mathematical Approaches to Algorithm Selection and AutoML
  71. Meta-Learning and Optimization of Learning Algorithms
  72. Convexification Techniques for Hard Optimization Problems
  73. Hybrid Heuristics and Theory-Guided Optimization
  74. Real-Time Optimization for Operations and Supply Chains
  75. Network Optimization for Mobility, Logistics, and Smart Cities
  76. Energy Systems Optimization and Grid Analytics
  77. Healthcare Analytics Optimization: Scheduling and Resource Allocation
  78. Finance Optimization: Portfolio Theory, Risk, and Computation
  79. Future Trends in Optimization, Algorithms, and Analytics
  80. Research Roadmap for Mathematics-Driven Analytics Systems
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