CG, most elegant of quadratic methods. Now let’s talk about why you wouldn’t use it. 1. In the form given, you need to know the terms of the quadratic. 2. If yo

CG, most elegant of quadratic methods. Now let’s talk about why you wouldn’t use it. 1. In the form given, you need to know the terms of the quadratic. 2. If you know the terms, you can compute a stable point using QR, which is more efficient. 3. IIRC CG has better numerical stability than QR, but only under strict quadratic assumptions. 4. If you don’t have the form of the objective, but you know it’s quadratic, and you can compute a gradient, then it also works, but errors in the gradient compound. 5. If it’s not quadratic, then you have the same issue. You can try to account for this using resets or more complicated methods, but then, 6. For so many problems, the stability proof becomes too onerous or the evaluations become less efficient than simply doing more steps of gradient descent, which is why this is still the dominant method in neural networks, despite so much effort on ‘our’ part to find more efficient solvers.