This book as well as Kochenderfer's earlier book "Decision Making Under Uncertainty"[0] are some of my favorite technical books (and I wouldn't be surprised to find his newest book, "Algorithms for Decision Making", also fell into this category).
The algorithm descriptions are clear, the visualizations are great, and, as someone who does a lot of ML work, they cover a lot of (important) topics beyond just what is covered in your standard ML book. This is especially refreshing if you're looking for thinking around optimization that is not just gradient descent (which has been basically the only mainstream approach to optimization in ML for two decades now).
I've known a few people to complain that the code examples are in Julia, but, as someone who doesn't know Julia, if you have experience doing quantitative programming at all it should be trivial convert the Julia examples to your favorite language for implementation (and frankly, I'm a bit horrified that so many people "smart" people interested in these sorts of topics seem trapped into reasoning in one specific language).
Optimization is such a rich field and should be of interest to any computer scientist who would describe themselves as "interested in solving hard problems" rather than just applying a well known technique to a specific class of hard problems.
> Optimization is such a rich field and should be of interest to any computer scientist who would describe themselves as "interested in solving hard problems" rather than just applying a well known technique to a specific class of hard problems.
Yes, but even if you are only interested in pragmatically solving problems, off-the-shelf solvers for various optimisation problems are a great toolkit to bring to bear.
Reformulating your specific problem as eg a mixed integer linear programming problem can often give you a quick baseline of performance. Similar for SMT. It also teaches you not to be afraid of NP. And it teaches you a valuable lesson in separation of specification (= your formulation of the problem) and how to compute the solution (= whatever the solver does), which can be applicable in other domains, too.
The algorithm descriptions are clear, the visualizations are great, and, as someone who does a lot of ML work, they cover a lot of (important) topics beyond just what is covered in your standard ML book. This is especially refreshing if you're looking for thinking around optimization that is not just gradient descent (which has been basically the only mainstream approach to optimization in ML for two decades now).
I've known a few people to complain that the code examples are in Julia, but, as someone who doesn't know Julia, if you have experience doing quantitative programming at all it should be trivial convert the Julia examples to your favorite language for implementation (and frankly, I'm a bit horrified that so many people "smart" people interested in these sorts of topics seem trapped into reasoning in one specific language).
Optimization is such a rich field and should be of interest to any computer scientist who would describe themselves as "interested in solving hard problems" rather than just applying a well known technique to a specific class of hard problems.
0. https://web.stanford.edu/group/sisl/public/dmu.pdf