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