https://rise4fun.com/ for all sorts of examples of what these sorts of algorithms can solve.
https://www.coursera.org/learn/discrete-optimization is a good introduction to constraint optimization, local search, linear programming, and mixed integer programming.
The most useful and accessible symbolic execution package I’ve found so far is KLEE https://klee.github.io/
If anyone else has recommendations for tools or beginner material I’m all ears!
Nielson & Nielson have the standard textbook in static program analysis that is used everywhere. But it's quite unfriendly as it's written using abstract algebra. They've recently released two textbooks that are much gentler. Actually, I'd say they are easy going and fun but still retain all the mathematical rigor.
They use program graphs, which are a bit less general but a lot easier to digest. They cover all major techniques, including theorem proving, static analysis, model checking, abstract interpretation, type and effect systems, etc:
- Formal Methods: An Appetizer https://www.springer.com/gp/book/9783030051556
- Program Analysis: An Appetizer https://arxiv.org/abs/2012.10086
There's also a companion website with some F# code. The second book, which seems still unfinished discusses how to implement program analyses using datalog. This speeds up development quite a lot. Otherwise, developing your own static analyzer is a lot of work.
My dream is to implement some kind of framework that enables quick DSL creation along with lightweight formal methods support to verify programs written in each DSL. I think restricted semantics is the key to make formal methods practical. Quoting Alan Perlis, "Beware of the Turing tar-pit in which everything is possible but nothing of interest is easy."
There's work in this area using monads. Specifically, Darais (from Galois) et al show in "Abstracting Definitional Interpreters" how given a definitional interpreter you can easily create all sorts of abstractions using a stack of monad transformers. The best part of it all is that your particular chosen stack remains valid when moved between interpreters of different languages.
Your dream of varied static analysis can be achieved using monad transformers, definitional interpreters written in the required style, and Racket's DSL-creation system.
- rise4fun https://rise4fun.com/z3/tutorialcontent/guide
- https://ericpony.github.io/z3py-tutorial/guide-examples.htm
- Programming Z3 -https://theory.stanford.edu/~nikolaj/programmingz3.html
- Nikolaj Bjorner's tutorial https://youtu.be/nGwyNmsxX6I
- Hakank's examples http://www.hakank.org/z3/
- Yurichev's book "SMT by Example" https://yurichev.com/writings/SAT_SMT_by_example.pdf
- http://hackage.haskell.org/package/sbv
- https://www.youtube.com/watch?v=ruNFcH-KibY Tikhon Jelvis - Analyzing Programs with Z3
- https://www.youtube.com/watch?v=rvPWDgJc0O4&ab_channel=ACMSI... - Nadia Polykarpova on Z3
- SAT SMT school https://sat-smt.in/
- Emina Torlak's course https://courses.cs.washington.edu/courses/cse507/19au/calend...
- Lindsey Kuper - SMT Solving and Solver-Aided Systems http://composition.al/CSE290Q-2019-09/