| 31. | Where the objective function is the Lagrange dual function.
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| 32. | Given an incompressible objective function, there is no basis for choosing one algorithm over another.
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| 33. | F ( \ mathbf { x } ) denotes the objective function to be minimized.
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| 34. | A common objective function, at least for regression / function estimation, is the least squares function:
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| 35. | The objective function for the group lasso is a natural generalization of the standard lasso objective
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| 36. | Using these in the objective function f gives f = 1 and f = 1.
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| 37. | When the objective function is convex, then any local minimum will also be a global minimum.
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| 38. | And implicitly, since stockholder and bondholders have different objective functions, in the analysis of agency problems.
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| 39. | Labor costs of exactly $ 10 will cause the objective function value to remain the same.
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| 40. | Most often only the diagonal elements are known, in which case the objective function simplifies to
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