Python code for computation and experimentation with cut-generating functions, in particular the Gomory-Johnson infinite group problem. By M. Köppe, Y. Zhou, C.Y. Hong, J. Wang with contributions by undergrad programmers
Branch-and-cut solver for the Capacitated Profitable Tour Problem (CPTP) and open s–t path variants
EvoCut: Automatic generation of acceleration cuts for integer programs via evolutionary search and LLMs.
An implementation for the Simplex algorithm for solving linear optimization problems. This approach covers both Primal and Dual Simplex.
The Cutting Plane Game v2 - My CS thesis project
MATLAB implementation of a branch-and-cut algorithm for the Asymmetric Travelling Salesman Problem (ATSP), using the Gurobi solver with degree constraints and subtour-elimination constraints separated via an integer-programming (ILP) separation problem. Operations research / integer programming.
Cutting-plane methods for JuMP
A from-scratch branch-and-cut MIP solver in C++23 with Python bindings (early development)
Learned Rounded Capacity Cut Separation (NeurIPS 2026)
A really simple implementation of simplex algorithm
An exact cutting plane method for the Euclidean max-sum diversity problem
✂️ Cutting planes separation in Julia with JuMP
Interactive C++/OpenGL tool for exploded-view visualization of NIfTI volume data using Marching Cubes, cutting planes, and edge-enhanced rendering.
SAGE: Automatic discovery of symbolic acceleration inequality families for MILP via evolutionary search and LLMs.
endolift (code name: chandelure): branch-and-cut for two-stage facility location with decision-dependent scenario probabilities. Linked lifted cuts, scenario decomposition with closed-form blocks, C++ solver (HiGHS, MPI), full experiments and the paper.
Computations supporting Chapters 2 and 3 of Jiawei Wang's dissertation "Subadditivity of Piecewise Linear Functions", UC Davis, Ph.D. program in Mathematics, 2020
This project has been developed for academic purpose. Basically, it consists in a Python algorithm able to solve a multidimensional Knapsack problem using only Gomory cuts. Every new cut shows its utility in terms of how much it reduces the gap between the relaxed PL solution and the optimal PLI one.
Rod cutting optimization problem with greedy 🔨 🔧
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