My 28-Week Rust Learning Journey: Optimization, Heuristics, Simulation, and ML

This post serves two purposes:

1. For myself – A central place to track my progress, resources, and weekly deliverables.

2. For you – If you’re walking a similar path or have insights to offer, you’re welcome to use this as a guide or share feedback.

Why Rust for OR, Simulation, and ML?

Rust gives me:

• Performance like C/C++ without the memory safety nightmares.

• Concurrency that’s easier to reason about than raw threads.

• A growing ecosystem for numerical computing, data science, and AI. It’s not as mature in scientific computing as Python, but the benefits in performance and reliability are worth the investment.

Learning Objectives

Over 28 weeks, my aim is to:

• Master Rust fundamentals (ownership, borrowing, lifetimes, traits).

• Build competence in numerical computing (linear algebra, statistics).

• Implement and experiment with optimization and heuristic algorithms.

• Create discrete-event and agent-based simulations.

• Integrate machine learning—both classical and deep learning—into simulation and optimization workflows.

• Scale computations with concurrency, async, and HPC methods.

Weekly Roadmap (Overview)

Stage 1 – Fundamentals (Weeks 1–4)
Get fluent in Rust syntax, memory model, traits, and module organization. Deliverables include small CLI tools, parsers, and basic data structures.

Stage 2 – Numerical Computing (Weeks 5–8)
Learn ndarray, nalgebra, ndarray-linalg, and statrs. Build linear solvers, perform Monte Carlo simulations, and apply statistical methods.

Stage 3 – Optimization & Heuristics (Weeks 9–12)
Implement simulated annealing, hill climbing, genetic algorithms, and model small LP/MILP problems with good_lp and argmin.

Stage 4 – Simulation & Stochastic Modeling (Weeks 13–16)
Build discrete-event simulations, M/M/1 queue models, agent-based epidemic simulations, and domain-specific scheduling simulations.

Stage 5 – Machine Learning Integration (Weeks 17–20)
Use linfa and smartcore for clustering, classification, and regression; integrate deep learning models via tch-rs.

Stage 6 – Parallelism & HPC (Weeks 21–24)
Parallelize workloads with rayon, build async simulations with tokio, and scale with MPI (rsmpi).

Stage 7 – Capstone (Weeks 25–28)
Deliver a complete application integrating optimization, simulation, and ML—document it, benchmark it, and make it public.

Weekly Deliverables & Checkboxes

Each week has specific tasks, from reading chapters in The Rust Programming Language to building algorithms from scratch. I keep them in a simple checklist format so I can see progress visually.

Example for Week 10:

• ☐ Implement GA for TSP with selection, crossover, mutation

• ☐ Parallelize fitness evaluation

• ☐ Export tour lengths over generations

Tools and Crates

Some of the core crates I’ll be using:

• Numerics: ndarray, nalgebra, ndarray-linalg

• Optimization: good_lp, argmin

• Simulation: rand, simrs

• ML: linfa, smartcore, tch-rs

• HPC: rayon, tokio, rsmpi

Recommended Books

Rust

• Programming Rust – Blandy, Orendorff, Tindall

• Rust for Rustaceans – Jon Gjengset

• Rust Atomics and Locks – Mara Bos

Optimization & Heuristics

• Numerical Optimization – Nocedal & Wright

• Integer and Combinatorial Optimization – Nemhauser & Wolsey

• Handbook of Metaheuristics – Gendreau & Potvin

Simulation

• Simulation Modeling and Analysis – Averill M. Law

• Discrete-Event System Simulation – Banks et al.

Machine Learning

• Pattern Recognition and Machine Learning – Bishop

• The Elements of Statistical Learning – Hastie, Tibshirani, Friedman

• Deep Learning – Goodfellow, Bengio, Courville

• Reinforcement Learning: An Introduction – Sutton & Barto

Weekly Time Commitment

• Baseline: 6–8 hrs/week

• Aggressive: 10–12 hrs/week

• Immersive: 15+ hrs/week

I expect to spend more time coding than reading after the fundamentals stage. Debugging and profiling will eat into time during optimization and HPC weeks.

Downloadable PDF Format

If this resource interests you and you would like the full format. Follow this link to download this five-page pdf.

Closing

This journey is as much about building capability in Rust as it is about applying it to real-world OR, simulation, and ML problems.

I’ll update this post periodically as I check off deliverables, add notes, or swap in better resources. If you’re on a similar path—or have suggestions for better crates, libraries, or approaches—drop a comment or reach out.

I'm starting this path around the middle of August. If all goes well, I'm expecting to have demonstrated a strong understanding of Rust as winter is wrapping up. Here's to a very productive fall and winter!