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/*
* 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,dx,N: N x-values to test with even dx spacing, [x0, x0+dx, x0+2dx, ...]
//
// 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 dx, int N);
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