Hi SciPy-Dev, I have been using scipy.optimize.fmin_bfgs on some derivative-free benchmark problems, and noticed that whenever the Wolfe line search requires a directional derivative, the current implementation estimates the entire gradient via finite differencing, and then computes the directional derivative by taking the inner product of the gradient and the search direction. In my experiments, replacing this full gradient estimation with a single extra function evaluation to estimate the directional derivative directly, is faster. The key places this happens in the code are: https://github.com/scipy/scipy/blob/701ffcc8a6f04509d115aac5e5681c538b5265a2... <https://github.com/scipy/scipy/blob/701ffcc8a6f04509d115aac5e5681c538b5265a2...> https://github.com/scipy/scipy/blob/701ffcc8a6f04509d115aac5e5681c538b5265a2... <https://github.com/scipy/scipy/blob/701ffcc8a6f04509d115aac5e5681c538b5265a2...> Both of the Wolfe line search implementations take an input function fprime that computes/estimates the full gradient, even though only derphi, the directional derivative, is used. What I’d like to do is have an option for fprime to be either provided or not provided to the Wolfe line search. If the objective has a nice/cheap gradient then the current behavior is fine (passing the gradient function as fprime, and computing directional derivatives with an inner product), but if the objective is derivative-free then derphi should be computed with finite differencing along the search direction (just one extra function evaluation) instead of using fprime. Do people agree this would be a good change? If so I can make a pull request. Best, Sara