NDWasmOptimize

Namespace

NDWasmOptimize

Description:

  • Namespace for Optimization functions using Go WASM.

Source:

Methods

(static) linearRegression(x, y) → {Object}

Description:

  • Fits a simple linear regression model: Y = alpha + beta*X.

Source:
Parameters:
Name Type Description
x NDArray

The independent variable (1D NDArray of float64).
y NDArray

The dependent variable (1D NDArray of float64).
Throws:

If WASM runtime is not loaded or inputs are invalid.

Type
Error
Returns:
  • An object containing the intercept (alpha) and slope (beta) of the fitted line.
Type
Object

(static) linprog(c, G, h, A, b, bounds) → {Object}

Description:

  • Provides Optimization capabilities by wrapping Go WASM functions. minimize cᵀ * x s.t G * x <= h A * x = b lower <= x <= upper

Source:
Parameters:
Name Type Description
c NDArray

Coefficient vector for the objective function (1D NDArray of float64).
G NDArray | null

Coefficient matrix for inequality constraints (2D NDArray of float64).
h NDArray | null

Right-hand side vector for inequality constraints (1D NDArray of float64).
A NDArray | null

Coefficient matrix for equality constraints (2D NDArray of float64).
b NDArray | null

Right-hand side vector for equality constraints (1D NDArray of float64).
bounds Array

Optional variable bounds as an array of [lower, upper] pairs. Use null for unbounded. [0, null] for all for default.
Throws:

If WASM runtime is not loaded or inputs are invalid.

Type
Error
Returns:
  • The optimization result.
Type
Object

(static) minimize(func, x0, optionsopt) → {Object}

Description:

  • Finds the minimum of a scalar function of one or more variables using an L-BFGS optimizer.

Source:
Parameters:
Name Type Attributes Description
func function

The objective function to be minimized. It must take a 1D Float64Array x (current point) and return a single number (the function value at x).
x0 NDArray

The initial guess for the optimization (1D NDArray of float64).
options Object <optional>

Optional parameters.

Properties
Name Type Attributes Description
grad function <optional>

The gradient of the objective function. Must take x (a 1D Float64Array) and write the result into the second argument grad_out (a 1D Float64Array). This function should not return a value.
Returns:

The optimization result.

Type
Object