Posts Tagged ‘ Optimization ’

Symbolic derivatives in SAS

September 11, 2017
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Symbolic derivatives in SAS

Did you know that you can get SAS to compute symbolic (analytical) derivatives of simple functions, including applying the product rule, quotient rule, and chain rule? SAS can form the symbolic derivatives of single-variable functions and partial derivatives of multivariable functions. Furthermore, the derivatives are output in a form that

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How to find a feasible point for a constrained optimization in SAS

June 19, 2017
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How to find a feasible point for a constrained optimization in SAS

Most numerical optimization routines require that the user provide an initial guess for the solution. I have previously described a method for choosing an initial guess for an optimization, which works well for low-dimensional optimization problems. Recently a SAS programmer asked how to find an initial guess when there are

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Two simple ways to construct a log-likelihood function in SAS

June 12, 2017
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Two simple ways to construct a log-likelihood function in SAS

Maximum likelihood estimation (MLE) is a powerful statistical technique that uses optimization techniques to fit parametric models. The technique finds the parameters that are "most likely" to have produced the observed data. SAS provides many tools for nonlinear optimization, so often the hardest part of maximum likelihood is writing down

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Split data into groups that have the same mean and variance

May 1, 2017
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Split data into groups that have the same mean and variance

A frequently asked question on SAS discussion forums concerns randomly assigning units (often patients in a study) to various experimental groups so that each group has approximately the same number of units. This basic problem is easily solved in SAS by using PROC SURVEYSELECT or a DATA step program. A

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Quadratic optimization in SAS

April 12, 2017
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Quadratic optimization in SAS

At SAS Global Forum last week, I saw a poster that used SAS/IML to optimized a quadratic objective function that arises in financial portfolio management (Xia, Eberhardt, and Kastin, 2017). The authors used the Newton-Raphson optimizer (NLPNRA routine) in SAS/IML to optimize a hypothetical portfolio of assets. The Newton-Raphson algorithm

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Solve mixed-integer linear programming problems in SAS

January 18, 2017
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Solve mixed-integer linear programming problems in SAS

This article shows how to solve mixed-integer linear programming (MILP) problems in SAS. In a mixed-integer problem, some of the variables in the problem are integer-valued whereas others are continuous. The objective function is a linear function of the variables and the variables can be subject to linear constraints. Last

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Solve linear programming problems in SAS

December 19, 2016
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Solve linear programming problems in SAS

In some applications, you need to optimize a linear objective function of many variables, subject to linear constraints. Solving this problem is called linear programming or linear optimization. This article shows two ways to solve linear programming problems in SAS: You can use the OPTMODEL procedure in SAS/OR software or

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Ten tips before you run an optimization

May 11, 2016
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Optimization is a primary tool of computational statistics. SAS/IML software provides a suite of nonlinear optimizers that makes it easy to find an optimum for a user-defined objective function. You can perform unconstrained optimization, or define linear or nonlinear constraints for constrained optimization. Over the years I have seen many

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