A C++ monorepo for discrete and continuous optimization. Batteries included!
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Monolith is a monorepo with several optimization projects. Some of the code was originally written for research or as hobby projects in other repositories (e.g. spii and easy-IP).

One of the highlights is a state-of-the-art scheduler using column generation, which significantly outperforms all other optimizers at schedulingbenchmarks.org.

Why a monorepo?

  • C++ does not have an ABI. Every compiler, or worse, every flag configuration of every compiler generates potentially incompatible code. I want to use many compilers (MCVC, GCC, Clang) and many settings (debug, release, asan, fuzzers etc.). I also use Emscripten to compile programs to WASM (example here).
  • Refactoring code becomes much easier if all code with all dependencies is available in one IDE at the same time.


Petter Strandmark, petter.strandmark@gmail.com


Column generation

The minimum::linear::colgen module contains code for solving scheduling problems. It significantly outperforms all other optimizers at schedulingbenchmarks.org.

Some of the reasons it is fast:

  • It uses a first-order LP solver based on papers by Chambolle and Pock.
  • The Ryan-Foster rule is used to iteratively work towards an integer solution. There is no time to branch and bound for big problems.
  • The pricing problem uses highly optimized dynamic programming in a DAG (in minimum::algorithms).


The minimum::ai module contains code originally from my Monte-Carlo tree search repository. It is very fast – some years ago it evaluated almost 2 million complete games per second when playing 4 in a row.


The minimum::linear module contains code originally from my easy-IP repository. It is a modelling interface (DSL) for integer programming and supports converting IPs to SAT. It also uses two free solvers: Cbc and Glpk.

minimum::linear also contains an implementation of a first-order LP solver based on papers by Chambolle and Pock. Using a first-order LP solver is very important when solving really big scheduling problems.


The minimum::nonlinear module contains code originally from my spii repository. When a function is defined, it will be automatically differentiated with an autodiff library. This makes the resulting code very fast and numerically stable. The library contains minimization routines like L-BFGS and Newton’s method.

For example, a function of one vector variable is simply defined as:

auto lambda = [](auto* x) {
    auto d0 = x[1] - x[0] * x[0];
    auto d1 = 1 - x[0];
    return 100 * d0 * d0 + d1 * d1;

Having auto here instead of e.g. double is crucial. Efficient code for first and second-order derivatives is generated with

auto term = make_differentiable<2>(lambda);


Using both minimum::linear and minimum::nonlinear, this module implements constrained optimization via successive linear programming.


The module minimum::curvature contains code originally written at curve_extraction. It computes shortest paths in regular graphs taking curvature into account.


Everything needed to build the library is included. With CMake and a modern C++ compiler, you can do

$ cmake /path/to/source
$ make


The license can be found in the LICENSE file. Contact me if you need another license.

The software in the third-party folder is not written exclusively by me and their respective license files apply.