Optimizers in Action

Advanced Techniques for Machine Learning and Computational Challenges
1. Auflage 2027
ISBN: 978-1-041-01928-2
Verlag: Taylor & Francis Ltd

Buch, Englisch, 184 Seiten, Format (B × H): 156 mm x 234 mm

Optimization quietly powers much of modern decision-making—from logistics and finance to intelligent systems and engineering design. This book offers an accessible yet rigorous journey into how optimization methods are built and applied, showing how abstract ideas translate into practical solutions that shape efficient, real-world outcomes across diverse domains.

The book develops a structured understanding of optimization methods, beginning with foundational mathematical principles and progressing toward widely used algorithms for solving constrained and unconstrained problems. It explores classical techniques such as gradient-based methods, linear and nonlinear programming, and combinatorial optimization, alongside modern perspectives on large-scale and data-driven problem solving. Special emphasis is placed on how these methods are implemented in practice, including convergence behavior, computational efficiency, and stability considerations. Through carefully chosen examples and case-based discussions, the book illustrates how optimization frameworks are applied in real-world scenarios such as scheduling, resource allocation, and decision support systems, bridging theoretical formulations with practical implementation challenges.

Zielgruppe

Academic, Postgraduate, Professional Practice & Development, Professional Reference, and Undergraduate Advanced

Autoren/Hrsg.

Tripathi, Shashi

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Naturwissenschaften Biowissenschaften Angewandte Biologie Biophysik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Optimierung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik Mathematik Operations Research
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik

Inhaltsverzeichnis

Chapter 1: Introduction to Optimization. Chapter 2: The Genesis of Gradient Descent: A Journey to the Minimum. Chapter 3: Advanced Optimizers: Beyond SGD's Horizon. Chapter 4: Concave Optimization Techniques. Chapter 5: Case Studies and Real-World Applications of Optimizers. Chapter 6: Key Recent Optimization Trends and Methods for Large Language Models. Chapter 7: The Architecture of Efficiency: A Unified Synthesis of Optimization in Modern Intelligent Systems. Epilogue: The Global Minimum Reached.

Über Autor(innen)

Dr. Shashi Prakash Tripathi is an NLP practitioner with over ten years of experience in natural language processing, predictive analytics, and graph-based modeling. His work bridges research and real-world applications, with contributions to open-source projects and a focus on building scalable, intelligent systems.