solvor 0.3.3

Solvor your optimization needs.

Solvor

Solvor your optimization needs..

What's in the box?

Category	Solvers	Use Case
Linear/Integer	solve_lp, solve_milp	Resource allocation, scheduling
Constraint	solve_sat	Sudoku, configuration, puzzles
Local Search	anneal, tabu_search	TSP, combinatorial optimization
Population	evolve	When you want nature to do the work
Continuous	gradient_descent, momentum, adam	ML, curve fitting
Black-box	bayesian_opt	Hyperparameter tuning, expensive functions
Graph	max_flow, min_cost_flow, solve_assignment	Matching, transportation
Exact Cover	solve_exact_cover	Sudoku, N-Queens, tiling puzzles

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Quickstart

uv add solvor

from solvor import solve_lp, solve_tsp, anneal, Model

# Linear Programming
result = solve_lp(c=[1, 2], A=[[1, 1], [2, 1]], b=[4, 5])
print(result.solution)  # optimal x

# TSP with tabu search
distances = [[0, 10, 15], [10, 0, 20], [15, 20, 0]]
result = solve_tsp(distances)
print(result.solution)  # best tour found

# Constraint satisfaction
m = Model()
x = m.int_var(1, 9, 'x')
y = m.int_var(1, 9, 'y')
m.add(m.all_different([x, y]))
m.add(m.sum_eq([x, y], 10))
result = m.solve()
print(result.solution)  # {'x': 3, 'y': 7}

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Solvers

Linear & Integer Programming

solve_lp

For resource allocation, blending, production planning. Finds the exact optimum for linear objectives with linear constraints.

# minimize 2x + 3y subject to x + y >= 4, x <= 3
result = solve_lp(
    c=[2, 3],
    A=[[-1, -1], [1, 0]],  # constraints as Ax <= b
    b=[-4, 3]
)

solve_milp

When some variables must be integers. Diet problems, scheduling with discrete slots, set covering.

# same as above, but x must be integer
result = solve_milp(c=[2, 3], A=[[-1, -1], [1, 0]], b=[-4, 3], integers=[0])

Constraint Programming

solve_sat

For "is this configuration valid?" problems. Dependencies, exclusions, implications - anything that boils down to boolean constraints.

# (x1 OR x2) AND (NOT x1 OR x3) AND (NOT x2 OR NOT x3)
result = solve_sat([[1, 2], [-1, 3], [-2, -3]])
print(result.solution)  # {1: True, 2: False, 3: True}

Model (CP-SAT)

For puzzles and scheduling with "all different", arithmetic, and logical constraints. Sudoku, N-Queens, timetabling.

m = Model()
cells = [[m.int_var(1, 9, f'c{i}{j}') for j in range(9)] for i in range(9)]

# All different in each row
for row in cells:
    m.add(m.all_different(row))

result = m.solve()

Metaheuristics

anneal

Simulated annealing, accepts worse solutions probabilistically.

result = anneal(
    initial=initial_solution,
    objective_fn=cost_function,
    neighbors=random_neighbor,
    temperature=1000,
    cooling=0.9995
)

tabu_search

Greedy local search with memory. Prevents cycling back to recent solutions, forcing exploration of new territory. More deterministic than anneal.

result = tabu_search(
    initial=initial_solution,
    objective_fn=cost_function,
    neighbors=get_neighbors,  # returns [(move, solution), ...]
    cooldown=10
)

evolve

Population-based search. More overhead than anneal/tabu, but better diversity and parallelizable.

result = evolve(
    objective_fn=fitness,
    population=initial_pop,
    crossover=my_crossover,
    mutate=my_mutate,
    max_gen=100
)

Continuous Optimization

gradient_descent / momentum / adam

Follow the slope downhill. Great for polishing solutions from other methods if your objective is differentiable. Adam adapts learning rates per parameter - usually the default choice.

def grad_fn(x):
    return [2 * x[0], 2 * x[1]]  # gradient of x^2 + y^2

result = adam(grad_fn, x0=[5.0, 5.0])
print(result.solution)  # [~0, ~0]

bayesian_opt

When each evaluation is expensive (think hyperparameter tuning, simulations). Builds a surrogate model to guess where to sample next instead of brute-forcing.

def expensive_fn(x):
    # imagine this takes 10 minutes to evaluate
    return (x[0] - 0.3)**2 + (x[1] - 0.7)**2

result = bayesian_opt(expensive_fn, bounds=[(0, 1), (0, 1)], max_iter=30)

Network Flow

max_flow

"How much can I push through this network?" Assigning workers to tasks, finding bottlenecks. The max-flow min-cut theorem gives you bottleneck analysis for free.

graph = {
    's': [('a', 10, 0), ('b', 5, 0)],
    'a': [('b', 15, 0), ('t', 10, 0)],
    'b': [('t', 10, 0)],
    't': []
}
result = max_flow(graph, 's', 't')
print(result.objective)  # total flow
print(result.solution)   # edge flows dict

min_cost_flow / solve_assignment

"What's the cheapest way to route X units?" Transportation, logistics, matching with costs.

# Assignment problem: 3 workers, 3 tasks
costs = [
    [10, 5, 13],
    [3, 9, 18],
    [10, 6, 12]
]
result = solve_assignment(costs)
# result.solution[i] = task assigned to worker i
# result.objective = total cost

Exact Cover

solve_exact_cover

For "place these pieces without overlap" or "fill this grid with exactly one of each" problems. Sudoku, pentomino tiling, scheduling where every slot must be filled exactly once.

# Tiling problem: cover all columns with non-overlapping rows
matrix = [
    [1, 1, 0, 0],  # row 0 covers columns 0, 1
    [0, 1, 1, 0],  # row 1 covers columns 1, 2
    [0, 0, 1, 1],  # row 2 covers columns 2, 3
    [1, 0, 0, 1],  # row 3 covers columns 0, 3
]
result = solve_exact_cover(matrix)
# result.solution = (0, 2) or (1, 3) - rows that cover all columns exactly once

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Result Format

All solvers return a consistent Result namedtuple:

Result(
    solution,     # best solution found
    objective,    # objective value
    iterations,   # solver iterations (pivots, generations, etc.)
    evaluations,  # function evaluations
    status        # OPTIMAL, FEASIBLE, INFEASIBLE, UNBOUNDED, MAX_ITER
)

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When to use what?

Problem	Solver
Linear constraints, continuous variables	solve_lp
Linear constraints, some integers	solve_milp
Boolean satisfiability	solve_sat
Discrete variables, complex constraints	Model
Combinatorial, good initial solution	tabu_search, anneal
Combinatorial, no clue where to start	evolve
Smooth, differentiable	adam
Expensive black-box	bayesian_opt
Assignment, matching, flow	max_flow, solve_assignment
Exact cover, tiling, N-Queens	solve_exact_cover

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Philosophy

1. Pure Python - no numpy, no scipy, no compiled extensions
2. Readable - each solvor fits in one file you can actually read
3. Consistent - same Result format, same minimize/maximize convention
4. Practical - solves real problems, or AoC puzzles

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Contributing

See CONTRIBUTING.md for development setup and guidelines.

License

Apache 2.0 License - free for personal and commercial use.