optyx 1.3.0

Intuitive symbolic interface for constrained optimization problems

Optyx

Optimization that reads like Python.

📚 Documentation · 🚀 Quickstart · 💡 Examples

With Optyx	With SciPy
from optyx import Variable, Problem x = Variable("x", lb=0) y = Variable("y", lb=0) solution = ( Problem() .minimize(x**2 + y**2) .subject_to(x + y >= 1) .solve() ) # x=0.5, y=0.5	from scipy.optimize import minimize import numpy as np def objective(v): return v[0]**2 + v[1]**2 def gradient(v): # manual! return np.array([2*v[0], 2*v[1]]) result = minimize( objective, x0=[1, 1], jac=gradient, method='SLSQP', bounds=[(0, None), (0, None)], constraints={'type': 'ineq', 'fun': lambda v: v[0]+v[1]-1} )

Your optimization code should read like your math. With Optyx, x + y >= 1 is exactly that—not a lambda buried in a constraint dictionary.

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Why Optyx?

Python has excellent optimization libraries. SciPy provides algorithms. CVXPY handles convex problems. Pyomo scales to industrial applications.

Optyx takes a different path: radical simplicity.

• Write problems as you think them — x**2 + y**2 not lambda v: v[0]**2 + v[1]**2
• Never compute gradients by hand — symbolic autodiff handles derivatives
• Skip solver configuration — sensible defaults, automatic solver selection

Being Honest

Optyx is young and opinionated. It's not a replacement for specialized tools:

Need	Use Instead
Large-scale MILP with custom branching	Pyomo, OR-Tools, Gurobi
Convex guarantees	CVXPY
Maximum performance	Raw solver APIs

Optyx does support MILP (via HiGHS), sparse LPs with 100k+ variables, and solver callbacks—but if you need industrial-grade MIP with cutting planes, a dedicated solver is the right choice.

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Installation

pip install optyx

Requires Python 3.12+, NumPy ≥2.0, SciPy ≥1.7.

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Quick Examples

Constrained Quadratic

from optyx import Variable, Problem

x = Variable("x", lb=0)
y = Variable("y", lb=0)

solution = (
    Problem()
    .minimize(x**2 + y**2)
    .subject_to(x + y >= 1)
    .solve()
)
# x=0.5, y=0.5, objective=0.5

Portfolio Optimization

from optyx import Variable, Problem

# Asset weights
tech = Variable("tech", lb=0, ub=1)
energy = Variable("energy", lb=0, ub=1)
finance = Variable("finance", lb=0, ub=1)

# Expected returns and risk (simplified)
returns = 0.12*tech + 0.08*energy + 0.10*finance
risk = tech**2 + energy**2 + finance**2  # variance proxy

solution = (
    Problem()
    .minimize(risk)
    .subject_to(returns >= 0.09)              # minimum return
    .subject_to((tech + energy + finance).eq(1))  # fully invested
    .solve()
)

Autodiff Just Works

from optyx import Variable
from optyx.core.autodiff import gradient

x = Variable("x")
f = x**3 + 2*x**2 - 5*x + 3

df = gradient(f, x)  # Symbolic: 3x² + 4x - 5
print(df.evaluate({"x": 2.0}))  # 15.0

Mixed-Integer Programming

from optyx import BinaryVariable, VectorVariable, Problem
import numpy as np

# Binary knapsack: select items to maximize value within weight limit
n = 5
x = VectorVariable("x", n, domain="binary")
values = np.array([10, 20, 15, 25, 30])
weights = np.array([5, 10, 8, 12, 15])

solution = (
    Problem()
    .maximize(x.dot(values))
    .subject_to(x.dot(weights) <= 30)
    .solve()
)
# Automatically routes to HiGHS MILP solver

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Features at a Glance

Feature	Description
Natural syntax	x + y >= 1 instead of constraint dictionaries
Automatic gradients	Symbolic differentiation—no manual derivatives
Smart solver selection	HiGHS for LP/MILP, SLSQP/BFGS for NLP
Mixed-integer programming	BinaryVariable, IntegerVariable, automatic MILP routing
Vector & matrix variables	VectorVariable, MatrixVariable, VariableDict for scalable models
Sparse LP support	subject_to(A @ x <= b) with `as_matrix(..., storage="auto"
Solver callbacks	Monitor progress, enforce time limits, early termination
LP format export	Problem.write("model.lp") for interop with other solvers
Solution serialization	to_json() / from_json() for logging and auditing
Fast re-solve	Cached compilation + warm starts, up to 900x speedup
Debuggable	Inspect expression trees, understand your model

See the documentation for the full API reference, tutorials, and real-world examples.

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What's Next

Optyx is actively evolving:

• MIQP / MINLP support — Quadratic and nonlinear MIP via native HiGHS or Gurobi
• MPS format I/O — Import and export MPS files for solver interop
• More solvers — IPOPT integration for large-scale NLP
• Better debugging — Infeasibility diagnostics and model inspection

See the roadmap for details.

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Contributing

git clone https://github.com/optyx-dev/optyx.git
cd optyx
uv sync
uv run pytest

Contributions welcome! See our contributing guide.

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License

MIT