Algorithms for Optimization
Algorithms for Optimization by Kochenderfer and Wheelerby Kochenderfer and Wheeler
The most recommended of 20 mathematics textbooks
Recommended as a detailed introduction to optimization algorithms, with clear figures, motivation and implementations. Critics say it gives a high-level overview with little practical usage.
Recommended as a detailed introduction to optimization algorithms, with clear figures, motivation and implementations. Critics say it gives a high-level overview with little practical usage.
11 people gave an opinion
- recommend it
- 10
- criticize it
- 1
- approval share
- 91%
Recommended in 1 of 13 months
- November 2025: not recommended
- December 2025: recommended
- January 2026: not recommended
- February 2026: not recommended
- March 2026: not recommended
- April 2026: not recommended
- May 2026: not recommended
- June 2026: not recommended
- July 2026: not recommended
- August 2026: not recommended
- September 2026: not recommended
- October 2026: not recommended
- November 2026: not recommended (month in progress)
Why it doesn’t carry the badge
- 10 people recommend itNeeds 10 or more, met.
- 10 recommenders for every criticNeeds 2 to 1 or better, met.
- Recommended in 1 of 13 monthsNeeds 4 or more, not met.
The same three rules apply to every product, and the badge can’t be bought. How the badge works
What people say about it
The reasons people give, with the number of comments that give each one. Open a reason to read what they said.
Why people recommend it
Each mark is one comment
Detailed introduction with motivation and derivations
“Alg4Opt covers more topics, providing the motivation behind the algorithm, sometimes a basic derivation, and a concrete implementation.”
Comment from 1 Dec 2025 in “Algorithms for Optimization [pdf]” “Gives the right amount of depth to serve as a detailed introduction”
Comment from 1 Dec 2025 in “Algorithms for Optimization [pdf]” Strong convexity and multiobjective chapters
“especially the advanced methods for establishing convexity one”
Comment from 1 Dec 2025 in “Algorithms for Optimization [pdf]” “excellent chapter on multiobjective optimization”
Comment from 1 Dec 2025 in “Algorithms for Optimization [pdf]” Good depth and useful field overview
“Gives the right amount of depth to serve as a detailed introduction”
Comment from 1 Dec 2025 in “Algorithms for Optimization [pdf]” “looks like a useful overview of the field”
Comment from 1 Dec 2025 in “Algorithms for Optimization [pdf]”
What people warn about
Each mark is one comment
High-level overview with little practical usage
“This book provides a high level overview of many methods without really hinting at the practical usage”
Comment from 1 Dec 2025 in “Algorithms for Optimization [pdf]”
Other mathematics textbooks people recommend
The four most recommended other mathematics textbooks, with the same counts for each, so you can compare like for like. The tick on each bar marks the 2 to 1 line the badge needs.
| Product | Recommend | Criticize | Approval | Months | Owned | Badge |
|---|---|---|---|---|---|---|
| Algorithms for Optimizationby Kochenderfer and WheelerThis page | 10 | 1 | 91% | 1 | — | — |
| Mathematics for Computer Scienceby Lehman, Leighton and Meyer | 8 | 4 | 67% | 1 | — | — |
| Numerical Recipesby Press et al. | 7 | 1 | 88% | 5 | — | — |
| Concrete Mathematicsby Graham, Knuth and Patashnik | 7 | 0 | 100% | 4 | — | — |
| Start Doing Graph Theoryby Sam Burns | 7 | 1 | 88% | 1 | — | — |
What people said
11 of the 12 comments that mention it, newest first. Each links to the original comment.
“The books [2],[3] are great marketing for Stanford.”
“It's a bit like the old Numerical Recipes book in that regard.”
“Alg4Opt covers more topics, providing the motivation behind the algorithm, sometimes a basic derivation, and a concrete implementation.”
“especially the advanced methods for establishing convexity one”
“the book has dedicated sections for the metaheuristics Firefly and Cuckoo Search”
“looks like a useful overview of the field”
“excellent chapter on multiobjective optimization”
“Gives the right amount of depth to serve as a detailed introduction”
“It starts out with modern gradient-based algorithms rooted in automatic differentiation, including recent things like Adam”
“This book provides a high level overview of many methods without really hinting at the practical usage”
“One thing I love about the book is it's full of really nice figures.”
Counts come from 1,795,640 Hacker News comments, November 2025 to November 2026. How we count. Report a mistake.