| /* |
| * Copyright 2018 Google Inc. |
| * |
| * Use of this source code is governed by a BSD-style license that can be |
| * found in the LICENSE file. |
| */ |
| |
| #pragma once |
| |
| #include <stdbool.h> |
| |
| // One Gauss-Newton step, tuning up to 3 parameters P to minimize [ r(x,ctx) ]^2. |
| // |
| // rg: residual function r(x,P) to minimize, and gradient at x in dfdP |
| // ctx: arbitrary context argument passed to rg |
| // P: in-out, both your initial guess for parameters of r(), and our updated values |
| // x0,x1,N: N x-values to test in [x0,x1] (both inclusive) with even spacing |
| // |
| // If you have fewer than 3 parameters, set the unused P to zero, don't touch their dfdP. |
| // |
| // Returns true and updates P on success, or returns false on failure. |
| bool skcms_gauss_newton_step(float (*rg)(float x, const void*, const float P[3], float dfdP[3]), |
| const void* ctx, |
| float P[3], |
| float x0, float x1, int N); |
