cvxpy-or 0.1.0

Operations Research-style modeling for CVXPY

cvxpy-or

Operations Research-style modeling for CVXPY.

This package provides AMPL/Pyomo-style set-based indexing for CVXPY, enabling natural modeling of transportation, scheduling, and other OR problems.

Installation

pip install cvxpy-or

# Optional: xarray support for matrix-style data
pip install cvxpy-or[xarray]

With uv:

uv add cvxpy-or

pandas and rich are included by default for DataFrame I/O and pretty printing.

Quick Start

import pandas as pd
from cvxpy_or import (
    Model, sum_by,
    set_from_dataframe, parameter_from_dataframe, parameter_from_series,
)

# Define data as DataFrames
cost_df = pd.DataFrame([
    {"warehouse": "Seattle", "customer": "NYC", "cost": 2.5},
    {"warehouse": "Seattle", "customer": "LA", "cost": 1.0},
    {"warehouse": "Seattle", "customer": "Houston", "cost": 1.8},
    {"warehouse": "Denver", "customer": "NYC", "cost": 2.0},
    {"warehouse": "Denver", "customer": "LA", "cost": 1.5},
    {"warehouse": "Denver", "customer": "Houston", "cost": 1.2},
    {"warehouse": "Chicago", "customer": "NYC", "cost": 1.0},
    {"warehouse": "Chicago", "customer": "LA", "cost": 2.5},
    {"warehouse": "Chicago", "customer": "Houston", "cost": 1.5},
])

supply = pd.Series({"Seattle": 100, "Denver": 80, "Chicago": 120}, name="supply")
demand = pd.Series({"NYC": 80, "LA": 70, "Houston": 50}, name="demand")

# Build model from DataFrames
routes = set_from_dataframe(cost_df, ["warehouse", "customer"])
cost = parameter_from_dataframe(cost_df, ["warehouse", "customer"], "cost", name="cost")
supply_param = parameter_from_series(supply, name="supply")
demand_param = parameter_from_series(demand, name="demand")

m = Model(name="transportation")
ship = m.add_variable(routes, nonneg=True, name="ship")

# Constraints
m.add_constraint("supply", sum_by(ship, "warehouse") <= supply_param)
m.add_constraint("demand", sum_by(ship, "customer") >= demand_param)

# Solve
m.minimize(cost @ ship)
m.solve()
m.print_summary()
m.print_solution(show_zero=False)

# Export results to DataFrame
result_df = m.to_dataframe("ship")
print(result_df[result_df["value"] > 0])

Key Features

• Native CVXPY: Variable and Parameter inherit from CVXPY - all operations work
• Model wrapper: Clean interface for building problems
• Set operations: Union, intersection, difference, filtering
• Named aggregation: sum_by, mean_by, min_by, max_by
• Constraint helpers: at_most_k, exactly_k, implies, mutex, one_of
• Validation: Helpful error messages with typo suggestions
• pandas I/O: Load data from DataFrames, export solutions
• Pretty printing: Rich tables for variables and solutions

API Reference

Core Classes

Set(elements, name=None, names=None)

An ordered set of elements for indexing.

warehouses = Set(['W1', 'W2', 'W3'], name='warehouses')
customers = Set(['C1', 'C2', 'C3'], name='customers')

# Cross product
routes = Set.cross(warehouses, customers)

# Set operations
A | B  # Union
A & B  # Intersection
A - B  # Difference
A ^ B  # Symmetric difference
A <= B # Subset

# Filtering and transformation
evens = numbers.filter(lambda x: x % 2 == 0)
doubled = numbers.map(lambda x: x * 2)
sorted_set = numbers.sorted()

# Access
s.first(), s.last()
len(s), 'W1' in s

Variable(index, nonneg=False, name=None, **kwargs)

A CVXPY Variable indexed by a Set.

ship = Variable(routes, nonneg=True, name='ship')
ship[('W1', 'C1')]          # Access by key
ship.get_value(('W1', 'C1')) # Get solved value

Parameter(index, data=None, name=None, **kwargs)

A CVXPY Parameter indexed by a Set.

cost = Parameter(routes, data={('W1', 'C1'): 10, ...})
cost.set_data(new_data)     # Update values
cost.expand(larger_index, positions)  # Broadcast

Model(name=None)

Wrapper for building optimization problems.

m = Model(name='my_problem')

# Create components
x = m.add_variable(index, name='x', nonneg=True)
p = m.add_parameter(index, data={...}, name='p')

# Add constraints
m.add_constraint('bounds', x <= 100)

# Set objective
m.minimize(cost @ x)  # or m.maximize(...)

# Solve
status = m.solve()
m.print_summary()
m.print_solution()

# Access results
m.status, m.value
m.get_variable('x'), m.get_parameter('p')
df = m.to_dataframe()

Aggregation Functions

from cvxpy_or import sum_by, mean_by, min_by, max_by, count_by, group_keys

# Sum by position (most common)
sum_by(ship, 'warehouses')  # Sum over customers for each warehouse
sum_by(ship, ['origin', 'period'])  # Keep multiple dimensions

# Mean by position
mean_by(cost, 'warehouses')  # Average cost per warehouse

# Min/max by position (returns variable + constraints)
max_ship, constraints = max_by(ship, 'warehouses')
min_ship, constraints = min_by(ship, 'customers')

# Utilities
counts = count_by(routes, 'warehouses')  # Elements per group
keys = group_keys(routes, 'warehouses')  # Unique group keys

Filtering

from cvxpy_or import where

# Filter expression elements
where(ship, lambda r: r[0] == 'W1')  # Callable
where(ship, origin='W1')             # Keyword
where(ship, origin=['W1', 'W2'])     # Multiple values

Constraint Helpers

from cvxpy_or import at_most_k, at_least_k, exactly_k, implies, mutex, one_of, bounds

# Cardinality constraints (returns list of constraints)
constraints = at_most_k(x, k=3)   # At most 3 nonzero
constraints = exactly_k(x, k=3)  # Exactly 3 nonzero
constraints = at_least_k(x, k=2) # At least 2 nonzero

# Logical constraints for binary variables
implies(a, b)  # a=1 => b=1
mutex(a, b, c) # At most one is 1
one_of(a, b, c) # Exactly one is 1

# Bounds from parameters
constraints = bounds(ship, lower=0, upper=capacity)

Validation

from cvxpy_or import validate_keys, validate_numeric, validate_bounds, ValidationError

# Validate data keys match index (with helpful error messages)
validate_keys(data, index)  # Raises ValidationError with suggestions

# Validate data types and bounds
validate_numeric(data)
validate_bounds(data, lower=0, upper=100)

pandas I/O

from cvxpy_or import (
    set_from_series, set_from_dataframe, set_from_index,
    parameter_from_dataframe, parameter_from_series,
    variable_to_dataframe, parameter_to_dataframe
)

# Create Set from DataFrame
customers = set_from_series(df['customer_id'])
routes = set_from_dataframe(df, columns=['origin', 'dest'])

# Create Parameter from DataFrame
cost = parameter_from_dataframe(df, index_cols=['origin', 'dest'], value_col='cost')
supply = parameter_from_series(df.set_index('warehouse')['supply'])

# Export solutions to DataFrame
df = variable_to_dataframe(ship)
df = m.to_dataframe('ship')  # From Model

Display

from cvxpy_or import print_variable, print_solution, variable_table

# Print variable values (after solving)
print_variable(ship, show_zero=False)

# Print solution summary
print_solution([ship, inventory], objective_value=m.value, status=m.status)

# Get table as string
table_str = variable_table(ship, precision=2)

Examples

See the examples/ directory for complete examples:

• assignment_problem.py - Worker-task assignment
• blending_problem.py - Blend optimization
• diet_problem.py - Classic diet problem
• facility_location.py - Facility location (UFLP)
• multi_period_transportation.py - Multi-period with inventory

Comparison: cvxpy-or vs Raw CVXPY

Raw CVXPY:

import cvxpy as cp
import numpy as np

n_warehouses, n_customers = 3, 4
cost = np.array([[1, 2, 3, 4], [2, 1, 2, 3], [3, 2, 1, 2]])
supply = np.array([100, 80, 120])
demand = np.array([60, 70, 50, 40])

ship = cp.Variable((n_warehouses, n_customers), nonneg=True)
prob = cp.Problem(
    cp.Minimize(cp.sum(cp.multiply(cost, ship))),
    [
        cp.sum(ship, axis=1) <= supply,
        cp.sum(ship, axis=0) >= demand,
    ]
)
prob.solve()

# Accessing results requires remembering indices
print(f"Ship from W1 to C2: {ship.value[0, 1]}")

cvxpy-or:

from cvxpy_or import Model, Set, sum_by

m = Model()
warehouses = Set(['Seattle', 'Denver', 'Chicago'], name='warehouses')
customers = Set(['NYC', 'LA', 'Houston', 'Miami'], name='customers')
routes = Set.cross(warehouses, customers)

cost = m.add_parameter(routes, data={('Seattle', 'NYC'): 1, ...})
supply = m.add_parameter(warehouses, data={'Seattle': 100, ...})
demand = m.add_parameter(customers, data={'NYC': 60, ...})
ship = m.add_variable(routes, nonneg=True, name='ship')

m.add_constraint('supply', sum_by(ship, 'warehouses') <= supply)
m.add_constraint('demand', sum_by(ship, 'customers') >= demand)
m.minimize(cost @ ship)
m.solve()

# Named access to results
print(f"Ship from Seattle to LA: {ship.get_value(('Seattle', 'LA'))}")
m.print_solution()

License

Apache-2.0