What is the difference between Constrained Maximum Likelihood and Maximum Likelihood or Constrained Optimization and Optimization?
A. The constrained versions of these programs can, of course, solve unconstrained problems as well as constrained ones. However, the methods the constrained programs must use are more complicated and time consuming – constrained problems are more difficult to solve and require additional, complicated methods not required for unconstrained problems. For that reason, you can expect that it will take the constrained programs more time and resources to solve an unconstrained problem than it would take the unconstrained programs to solve them. The essential calculation in Maximum Likelihood and Optimization is the solution of a linear equation, Hd = g, where H is the Hessian, or an approximation to the Hessian, g is the gradient vector, and d is the direction vector that we are solving for. Constrained Maximum Likelihood and Constrained Optimization, on the other hand, must solve d’Hd + d’G subject to equality and inequality constraints, which is a much more difficult problem. Thus, you sho
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