Simply put: Optimizing a function f(x,y,z), we simply take the gradients and follow them downhill/uphill to optimize the function. However, when any of the variable(s) has a constraint on it (i.e it should be less or greater than some value(s) or some g(x) under some range), then how do we achieve the optimization process? I have been trying for sometime, but not able to get the idea clearly.
Can some body explain on the similar lines?. Will be much thankful.