solvOR

Solvor all your optimization needs.

solvOR is a pure Python optimization library. No numpy, no scipy, no compiled extensions. Each solver fits in one file you can actually read.

Quick Start

uv add solvor

from solvor import solve_lp, dijkstra, solve_hungarian

# Linear programming
result = solve_lp(c=[1, 2], A=[[1, 1]], b=[4])
print(result.solution)

# Shortest path
graph = {'A': [('B', 1), ('C', 4)], 'B': [('C', 2)], 'C': []}
result = dijkstra('A', 'C', lambda n: graph.get(n, []))
print(result.solution)  # ['A', 'B', 'C']

# Assignment
costs = [[10, 5], [3, 9]]
result = solve_hungarian(costs)
print(result.solution)  # [1, 0]

What's in the box?

Category	Solvers	Use Case
Linear/Integer	solve_lp, solve_milp	Resource allocation, scheduling
Constraint	solve_sat, Model	Sudoku, puzzles, config problems
Combinatorial	solve_knapsack, solve_bin_pack, solve_job_shop, solve_vrptw	Packing, scheduling, routing
Local Search	anneal, tabu_search, lns, alns	TSP, combinatorial optimization
Population	evolve, differential_evolution, particle_swarm	Global search
Gradient	gradient_descent, momentum, rmsprop, adam	ML, curve fitting
Quasi-Newton	bfgs, lbfgs	Fast convergence, smooth functions
Derivative-Free	nelder_mead, powell, bayesian_opt	Black-box, expensive functions
Pathfinding	bfs, dfs, dijkstra, astar, bellman_ford, floyd_warshall	Shortest paths
Graph	max_flow, min_cost_flow, kruskal, prim	Flow, MST
Assignment	solve_hungarian, solve_assignment	Matching

When to use what?

I need the optimal solution and...

• My constraints are linear → solve_lp
• Some variables must be integers → solve_milp
• It's all boolean logic → solve_sat
• I have complex constraints → Model (CP-SAT)

Good enough is fine...

• I have a decent starting point → tabu_search or anneal
• Continuous, no gradients → particle_swarm or nelder_mead
• My function is smooth → adam, bfgs
• Each evaluation is expensive → bayesian_opt

I need a path through a graph...

• Unweighted → bfs or dfs
• Weighted, non-negative → dijkstra or astar
• Negative edge weights → bellman_ford

Philosophy

1. Pure Python - no external dependencies, runs anywhere
2. Readable - each solver fits in one file you can understand
3. Consistent - same Result format, same conventions
4. Practical - solves real problems (and AoC puzzles)

Get Started Examples