solver

Chances are that your first Sudoku solver uses some bruteforce approach, while your manual solution certainly does not.

When you run millions of iterations on some crazy quantum solver, it's hard to spot if you're wrong.

Then the intuitions that you get from many manual games can drive the design of your solver.

Otherwise my experience is that asking for a general purpose solver is, for now, hopeless.

I've been working on a symbolic solver lately and have had to implement a lot of techniques manually.

Dumb question maybe, but is knowledge like this often used in symbolic solver software?

Is there a reason why you're looking for Levenberg-Marquardt over a trust-region Newton solver?

We aim at strengthening the mobile Android team with a creative and innovative problem-solver, who is able to cope with a challenging environment.

This is typically handled by choosing a solver that maintains such invariants by construction, e.g., symplectic methods for Hamiltonian systems conserve symplectic 2-form.

I was learning algorithms then and a sudoku solver was both fun and a good learning exercise.

Just wanted to mention that the or-tools constraint solver is absolutely top notch.

What's better for the brain, solving Sudokus, or writing a Sudoku solver?

> I guess it's extremely difficult to implement generic optimization solver, A "solver"?

Lots of people talk about giving a problem to a "solver".

If you're using TDD to write a sudoku solver, surely the first thing you should write is a test for your "solve_sudoku" function; then your design is supposed to evolve out of the code you write to pass that test (and the further tests you write for parts of that solution, iteratively).

It feels like no matter what someone writes --- an emulator, a TCP/IP stack, a Montgomery curve implementation, the world's fastest SMT solver, whatever --- there is always somebody, who always writes like they sound like "that creepy booger-nosed kid" from King of the Hill, to tell us how unimpressive the accomplishment is.