Continuous Optimization for Data Science Author: Moshe Haviv

Continuous Optimization for Data Science by Moshe Haviv is a definitive resource for those aiming to understand and apply mathematical optimization techniques within the realm of data science. With clarity and rigor, Haviv bridges the gap between theory and real-world implementation—making it especially valuable for students, data scientists, and applied mathematicians.

The book walks readers through foundational methods such as:

Gradient descent and its variations

Newton’s method and quasi-Newton techniques

Conjugate gradient methods

Convex optimization principles

Alongside these techniques, Haviv highlights their relevance to key data science processes such as:

Model training for machine learning

Feature selection strategies

Hyperparameter tuning and performance enhancement

With visual representations, case studies, and problem sets, the book transforms abstract mathematics into actionable insight. It emphasizes how continuous optimization lies at the heart of predictive modeling, data fitting, and algorithm efficiency—making it essential for navigating the increasingly quantitative landscape of analytics and artificial intelligence.

The cover’s geometric and colorful design echoes the multidimensional nature of optimization landscapes—reinforcing the book’s dynamic subject matter.