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MCH
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Gradient without function, just testing and training data

I am new to optimization in particular so I think what I am looking for is vocabulary to describe a particular area of problems. In many (all?) optimization algorithms you need to take the gradient of the objective function at various points in the algorithm.

Many examples I find assume you already know the objective function, but, I assume, in most interesting problems you only have resultsinputs and outputs of the objective function (trainingtesting and training data set). So how do you go from just the results of the function to the gradient of a function? I guess you could use regression analysis or slope of data points, but what are the common techniques (pros and cons) in optimization?

Gradient without function, just training data

I am new to optimization in particular so I think what I am looking for is vocabulary to describe a particular area of problems. In many (all?) optimization algorithms you need to take the gradient of the objective function at various points in the algorithm.

Many examples I find assume you already know the objective function, but, I assume, in most interesting problems you only have results of the objective function (training set). So how do you go from just the results of the function to the gradient of a function? I guess you could use regression analysis or slope of data points, but what are the common techniques (pros and cons) in optimization?

Gradient without function, just testing and training data

I am new to optimization in particular so I think what I am looking for is vocabulary to describe a particular area of problems. In many (all?) optimization algorithms you need to take the gradient of the objective function at various points in the algorithm.

Many examples I find assume you already know the objective function, but, I assume, in most interesting problems you only have inputs and outputs of the objective function (testing and training data set). So how do you go from just the results of the function to the gradient of a function? I guess you could use regression analysis or slope of data points, but what are the common techniques (pros and cons) in optimization?

Source Link
MCH
MCH
  • 103
  • 3

Gradient without function, just training data

I am new to optimization in particular so I think what I am looking for is vocabulary to describe a particular area of problems. In many (all?) optimization algorithms you need to take the gradient of the objective function at various points in the algorithm.

Many examples I find assume you already know the objective function, but, I assume, in most interesting problems you only have results of the objective function (training set). So how do you go from just the results of the function to the gradient of a function? I guess you could use regression analysis or slope of data points, but what are the common techniques (pros and cons) in optimization?