# Posts Tagged ‘ Optimization ’

## Visualize the feasible region for a constrained optimization

November 7, 2018
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When solving optimization problems, it is harder to specify a constrained optimization than an unconstrained one. A constrained optimization requires that you specify multiple constraints. One little typo or a missing minus sign can result in an infeasible problem or a solution that is unrelated to the true problem. This

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## Optimization with nonlinear constraints in SAS

August 15, 2018
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This article shows how to perform an optimization in SAS when the parameters are restricted by nonlinear constraints. In particular, it solves an optimization problem where the parameters are constrained to lie in the annular region between two circles. The end of the article shows the path of partial solutions

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## Fit a distribution from quantiles

March 7, 2018
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Data analysts often fit a probability distribution to data. When you have access to the data, a common technique is to use maximum likelihood estimation (MLE) to compute the parameters of a distribution that are "most likely" to have produced the observed data. However, how can you fit a distribution

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## The method of moments: A smart way to choose initial parameters for MLE

November 27, 2017
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When you run an optimization, it is often not clear how to provide the optimization algorithm with an initial guess for the parameters. A good guess converges quickly to the optimal solution whereas a bad guess might diverge or require many iterations to converge. Many people use a default value

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## Symbolic derivatives in SAS

September 11, 2017
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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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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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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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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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