I will present my recent research in going beyond the quadratic cost in optimization, by replacing it with a general cost function and using a majorize-minimize framework. With Flavien Léger (INRIA Paris), we unveiled in arxiv.org/abs/2305.04917 a new class of gradient-type optimization methods that extends vanilla gradient descent, mirror descent, Riemannian gradient descent, and natural gradient descent, while keeping the same proof ideas and rates of convergence. Our approach involves constructing a surrogate for the objective function in a systematic manner, based on a chosen cost function. This surrogate is then minimized using an alternating minimization scheme. Using optimal transport theory we establish convergence rates based on generalized notions of L-smoothness and convexity. We provide local versions of these two notions when the cost satisfies a condition known as nonnegative cross-curvature. In particular our framework provides the first global rates for natural gradient descent and Newton's method. For a quick read of arxiv.org/abs/2305.04917 I recommend reading the summary p4-6 and then look for your favorite algorithm (mirror, Riemannian, etc) in Section 4.