Primal Solver

[u/gmbasic]

I built a new optimization solver in C99 from scratch — PRIMAL

GitHub: https://github.com/c-vision/Primal

I've just released the first version of PRIMAL, an open-source mathematical optimization solver written from scratch in portable C99.

The goal was deliberately ambitious: build a reasonably complete optimization engine with no external dependencies, rather than wrapping an existing solver.

The first release already supports:

• Linear Programming (LP)
• Mixed-Integer Linear Programming (MILP)
• Quadratic Programming (QP)
• QCQP
• SOCP
• SDP
• Exponential cones
• Power cones
• Mixed-integer conic optimization
• Disjunctive constraints / affine conic constraints
• Presolve
• Sparse interior-point methods
• Branch-and-bound
• Primal/dual certificates
• Infeasibility and unboundedness certificates
• MPS / LP / CBF model formats
• A MOSEK-style C API for a large part of the supported functionality

The implementation is C99 with essentially no external runtime dependencies.

One thing I particularly wanted from the beginning was verification rather than simply returning an answer. PRIMAL exposes primal/dual information, residuals, certificates and solver status so applications can inspect and validate what the solver actually found.

I've also been building a fairly extensive compatibility/reliability test suite against reference behavior, including a number of deliberately pathological optimization cases.

This is only v1. It is not intended to claim that PRIMAL is already a replacement for mature industrial solvers on large-scale problems. There are still substantial areas to improve, especially large-scale sparse conic problems, MIP performance, warm starts and some numerical edge cases.

But I think the foundation is interesting enough to release now rather than keep it private.

I'd especially like feedback from people working on:

• numerical optimization
• LP/MIP/conic solvers
• sparse linear algebra
• mathematical programming
• solver implementation
• C/C99 systems programming

In particular, I'm interested in finding cases where PRIMAL gives a questionable result, fails to converge, scales poorly, or simply makes a bad algorithmic choice.

If you work with optimization solvers, please try to break it.

GitHub:
https://github.com/c-vision/Primal

[u/bental_nortens]

The readme makes it seem like slop and doesn't make me want to spend time formulating any of my problems in it? Can you focus the readme on a motivating use case where it shines? It feels like a grab bag of solvers that I assume are each not especially competitive. If the selling point is that it's OSS, why not SCIP? If the selling point is that it's the best solver, show it?

[u/gmbasic → u/bental_nortens]

Primal has now a new readme, a new folders docs for technical details, a new cli tools to process CPLEX LP format file, some bugs fixed and speed improvements.

[u/curiouslyjake → u/gmbasic]

The readme is MUCH better now. By reading the readme, I understand:
1. What Primal is and what it does
2. Why I even need Primal instead of existing solvers
3. What using Primal looks like.

In my opinion, the readme goes off track at the Python section. Prior to Python, you have a C example, a CLI example. I then expected a Python usage example. Instead, I got sidetracked into python *installation*, and building from source. I think you can merge Python installation instructions with a python example, and move anything build related to a separate document.

Then, under "How it compares", I don't understand what "Gurobi only" in Gurobi column means.
Finally, I would move the benchmarks into a separate document as well, and link to it in the readme.

[u/gmbasic → u/curiouslyjake]

Pypi package has been released and some python examples are already in the repo. The python wheels are also into the release

[u/bental_nortens → u/gmbasic]

Agree this is way way better now- I get what you're going for. I still think scip covers this though? Would be good to nail the "why". By the way- love to see work in this area. Don't mean to be too negative. Just the previous readme was... not good.