What kind of function can be minimized by going "downhill"

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The discussion focuses on identifying a specific property of functions that allows for continuous steepest descent to reach a global minimum. While smoothness is necessary, it is not sufficient for this property. The example of the 2-D Rosenbrock function illustrates that it is non-convex yet possesses the desired characteristic, where any numerical algorithm can lead to the global minimum despite potential challenges in navigating the valley. The property being sought is that of a function with a global minimum but no local minima. This type of function is often referred to as a "global minimum function."
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Hi all:
I'm looking for the name of a property that a function (of arbitrary dimension) has when you can continuously follow the steepest descent to get to the global minimum. Being "smooth" is necessary but not sufficient.

For example, for just 1-D, this property is equivalent to being "convex", however the 2-D Rosenbrock function is a non-convex function, but still does have the property I'm looking for: any numerical algorithm will bring you quickly to the valley --it will then struggle to follow the valley to get to the global minimum, but at least in principle there is always a small-but-finite gradient that, if followed, will lead you to the global minimum which is the only stationary point. Therefore, the Rosenbrock function is a ... function.

Can anyone tell me what word I should use to finish that sentence?
 
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I don't know a single word, but you can say that it is a function that has a global minimum but no local minima.
 
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