2. Core APIs¶

This chapter covers NLSQ’s core API classes for direct control over optimization.

2.5. Chapter Overview¶

CurveFit Class (15 min): Reusable curve fitting with JIT caching.
LeastSquares (15 min): Direct optimizer control.
TRF Optimizer (10 min): Trust Region Reflective algorithm details.
Result Types (5 min): OptimizeResult and OptimizeWarning.

2.6. API Level Comparison¶

API	Control Level	Use Case
`fit()`	Automatic	Simple, workflow-based fitting
`CurveFit`	Medium	Reusable fitting, batch processing
`LeastSquares`	High	Custom residual functions, diagnostics
`TRF`	Full	Algorithm customization, research

2.7. Quick Examples¶

from nlsq import fit, CurveFit
from nlsq.core.least_squares import LeastSquares

# Simple fit
popt, pcov = fit(model, x, y, p0=[...])

# Reusable CurveFit
fitter = CurveFit()
popt1, pcov1 = fitter.curve_fit(model, x1, y1, p0=[...])
popt2, pcov2 = fitter.curve_fit(model, x2, y2, p0=[...])

# Direct LeastSquares
optimizer = LeastSquares()
result = optimizer.least_squares(
    fun=residual_func, x0=initial_params, bounds=(-np.inf, np.inf)
)