Unconstrained optimization finds a variable (or a set of variables) that minimize or maximize an objective function without restrictions on their values. Mathematically, it says, find x∗ such that function f(x∗) takes the smallest value.
x may be a scalar (in ℝ1) or a vector (in ℝn). f(x) is always a scalar (in ℝ1). Unconstrained optimization has applications in many branches of science. In finance, constructing a yield curve involves finding a set of parameters to minimize the difference ...
