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Optimization Series

Solve Harder Problems with
Smarter Models

Four volumes. Zero hand-waving. The MILP Optimization Handbook Series is your practical, no-nonsense guide to building optimization models that actually work in production — from foundational theory to advanced techniques.

The Series

4 books. 2 released. 2 on the way. Each volume builds on the last — but they also stand alone for practitioners who need to solve a specific class of problem right now.

📗 Volume 1 — Available

The MILP Optimization Handbook

Core Theory & Practical Foundations

  • Core modeling theory & practical framing
  • Variables, constraints & objectives
  • Solver behavior & tuning
  • Feasibility diagnostics & debugging
📘 Volume 2 — Available

The Linearization Handbook

Taming Nonlinearities in MILP

  • Absolute values, min/max & ratios
  • Soft constraints & penalty functions
  • Binary logic — IF/THEN, AND, OR, XOR
  • SOS sets & piecewise linear functions
📙 Volume 3 — Coming Soon

The Scalability Handbook

Release TBD

  • Best practices for large-scale models
  • Decomposition — Benders & Column Generation
  • Metaheuristics & hybrid approaches
Coming Soon
📓 Volume 4 — Coming Soon

Stochastic Optimization with MILP

Release TBD

  • Stochastic & robust optimization
  • Sequential decision analytics
  • Simulation + optimization integration
Coming Soon

What Readers Say

Practitioners, professors, and engineers weigh in on the Handbook Series.

“I've used this book more in a week than my old textbooks in a year. Straight to the point, full of tricks that actually work.”

Anonymous

Lead Data Scientist, Logistics Tech Company

“Finally, a book that explains indicator constraints and SOS sets without assuming I have a PhD in convex analysis.”

Anonymous

Optimization Engineer, Energy Sector

“The BitBros team gets it. Their content is pragmatic, modern, and way more useful than anything else I've seen on MILP.”

Anonymous

Professor of Operations Research, Tier 1 University

Build Models That
Actually Work

Whether you're just getting started with MILP or you're debugging infeasibility on a 50,000-variable model — there's a volume for you.