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Gradient ascent is based on the principle of locating the greatest point on a function and then moving in the direction of the gradient. In this method, the gradient function is the function of x and y differentiable values.
To add to this, the changes made with gradient descent are in the direction of 'steepest' improvement relative to the current point, whereas hill climbing accepts changes that make any improvement regardless of slope.
Summary. The gradient is the directional derivative of a function. The directional of steepest descent (or ascent) is the direction amongst all nearby directions that lowers or raises the value of f the most.
In fact, there are three types of gradients: linear, radial, and conic.
I am not able to find anything about gradient ascent. Any good link about gradient ascent demonstrating how it is different from gradient descent would help.
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