gridoptim 2.1.1

Fast multivariate grid search optimizer with C++ core

gridoptim

gridoptim is a deterministic multivariate grid-search optimizer for Python with a compiled C++ backend.

It evaluates a mathematical expression across every point in a user-defined parameter grid and returns the best result for either minimization or maximization.

Features

• Deterministic brute-force optimization
• Fast native C++ core exposed through Python
• Simple string-based expression interface
• Support for multi-variable parameter sweeps
• Reproducible results across runs
• Small public API with minimal setup

Installation

Install from PyPI:

pip install gridoptim

Requirements:

• Python 3.10+
• A supported build environment if installing from source

Quick Start

from gridoptim import GridSearchOptimiser

opt = GridSearchOptimiser()

value, params = (
    opt
    .function("x^2 + y^2")
    .set_range("x", -10, 10, 0.5)
    .set_range("y", -10, 10, 0.5)
    .optimise("min")
)

print("Best value:", value)
print("Best parameters:", params)

Expected output:

Best value: 0.0
Best parameters: {'x': 0.0, 'y': 0.0}

How It Works

gridoptim performs an exhaustive search over all combinations of values produced by the ranges you define.

For example, if you search:

• x from -10 to 10 with step 1
• y from -10 to 10 with step 1

the optimizer evaluates 21 x 21 = 441 points.

This guarantees the best result within the grid you specified, unlike probabilistic or heuristic optimizers that may trade certainty for speed.

Basic Usage

Optimization follows three steps:

1. Create an optimizer.
2. Define the expression to evaluate.
3. Define a range for each variable, then run optimise("min") or optimise("max").

from gridoptim import GridSearchOptimiser

opt = GridSearchOptimiser()
opt.function("sin(x) + cos(y) + x^2")
opt.set_range("x", -3.14, 3.14, 0.1)
opt.set_range("y", -3.14, 3.14, 0.1)

value, params = opt.optimise("min")

API

GridSearchOptimiser

Main optimization class.

GridSearchOptimiser(expr: str | None = None)

Creates a new optimizer. You may optionally provide the expression at construction time.

function(expr: str) -> GridSearchOptimiser

Sets the mathematical expression to optimize.

opt.function("x^2 + y^2")

set_range(var: str, min_val: float, max_val: float, step: float) -> GridSearchOptimiser

Defines the search range for a variable.

opt.set_range("x", -10, 10, 0.1)

Arguments:

• var: variable name used in the expression
• min_val: lower bound
• max_val: upper bound
• step: step size, must be greater than 0

optimise(mode: str = "min") -> tuple[float, dict[str, float]]

Runs the grid search and returns:

• best_value
• best_parameter_dict

value, params = opt.optimise("max")

Valid modes:

• "min" for minimization
• "max" for maximization

Expression Syntax

Expressions are passed as strings and evaluated by the native backend using a lightweight mathematical expression parser.

Supported operators include:

• +
• -
• *
• /
• ^

Common mathematical functions such as sin, cos, tan, log, sqrt, exp, and abs are supported.

Example:

opt.function("sin(x) * cos(y) + x^2")

Examples

Minimize a quadratic

from gridoptim import GridSearchOptimiser

opt = GridSearchOptimiser()

value, params = (
    opt
    .function("x^2 + y^2")
    .set_range("x", -5, 5, 0.1)
    .set_range("y", -5, 5, 0.1)
    .optimise("min")
)

print(value)
print(params)

Maximize a trigonometric expression

from gridoptim import GridSearchOptimiser

opt = GridSearchOptimiser()

value, params = (
    opt
    .function("sin(x) * cos(y)")
    .set_range("x", -3.14, 3.14, 0.01)
    .set_range("y", -3.14, 3.14, 0.01)
    .optimise("max")
)

print(value)
print(params)

Search across three variables

from gridoptim import GridSearchOptimiser

opt = GridSearchOptimiser()

value, params = (
    opt
    .function("x^2 + y^2 + z^2")
    .set_range("x", -10, 10, 1)
    .set_range("y", -10, 10, 1)
    .set_range("z", -10, 10, 1)
    .optimise("min")
)

print(value)
print(params)

Performance Notes

Grid search grows exponentially with the number of variables and the number of steps per variable.

For example:

• 3 variables
• 100 steps per variable

results in 100 x 100 x 100 = 1,000,000 evaluations.

Because the heavy computation is performed in C++, gridoptim can handle much larger search spaces than a pure Python implementation, but exhaustive search still becomes expensive as the grid grows.

License

See the LICENSE file included in this repository for licensing terms.

Author

Akhil Shimna Kumar