Tag: optimization

Advanced Analytics
Emily Lada 0
Simulate to validate

The primary objective of many discrete-event simulation projects is system investigation.  Output data from the simulation model are used to better understand the operation of the system (whether that system is real or theoretical), as well as to conduct various "what-if"-type analyses.   However, I recently worked on another model

Advanced Analytics
Matthew Galati 0
The kidney exchange problem

Suppose someone needs a kidney transplant and a family member is willing to donate one. If the donor and recipient are incompatible (because of blood types, tissue mismatch, and so on), the transplant cannot happen. Now suppose two donor-recipient pairs A and B are in this situation, but donor A

Rick Wicklin 0
How to find an initial guess for an optimization

Nonlinear optimization routines enable you to find the values of variables that optimize an objective function of those variables. When you use a numerical optimization routine, you need to provide an initial guess, often called a "starting point" for the algorithm. Optimization routines iteratively improve the initial guess in an

Data Management
Alyssa Farrell 0
A must-read for petroleum professionals

Oil companies are being forced to explore in geologically complex and remote areas to exploit more unconventional hydrocarbon deposits.  New engineering technology has pushed the envelope of previous upstream experience.  No guidebook existed on how computing methodologies can contribute to E&P performance at reduced risk.  Until now. A new book

Rick Wicklin 0
Optimizing a function of an integral

Last week I showed how to find parameters that maximize the integral of a certain probability density function (PDF). Because the function was a PDF, I could evaluate the integral by calling the CDF function in SAS. (Recall that the cumulative distribution function (CDF) is the integral of a PDF.)

Rick Wicklin 0
Optimizing a function that evaluates an integral

SAS programmers use the SAS/IML language for many different tasks. One important task is computing an integral. Another is optimizing functions, such as maximizing a likelihood function to find parameters that best fit a set of data. Last week I saw an interesting problem that combines these two important tasks.

Leo Sadovy 0
The value is in the network

Dateline: October 4, 2012 – Facebook reaches one billion users! One billion people connected on a single platform; one-seventh of the world’s population.  If you assume 40,000 BCE as the start of modern humans, it took the planet 41,804 years to reach a population level of one billion; it took

Mike Gilliland 0
Forecasting and analytics at Disney World

The April 2012 issue of ORMS Today contains a piece on "How analytics enhance the guest experience at Walt Disney World," by Pete Buczkowski and Hai Chu. While many of us are used to forecasting just one or two things (such as unit sales or revenue), Pete and Hai illustrate

Rick Wicklin 0
Optimizing? Two hints for specifying derivatives

I previously wrote about using SAS/IML for nonlinear optimization, and demonstrated optimization by maximizing a likelihood function. Many well-known optimization algorithms require derivative information during the optimization, including the conjugate gradient method (implemented in the NLPCG subroutine) and the Newton-Raphson method (implemented in the NLPNRA method). You should specify analytic

Rick Wicklin 0
Maximum likelihood estimation in SAS/IML

A popular use of SAS/IML software is to optimize functions of several variables. One statistical application of optimization is estimating parameters that optimize the maximum likelihood function. This post gives a simple example for maximum likelihood estimation (MLE): fitting a parametric density estimate to data. Which density curve fits the

Diane Lennox 0
James Taylor's take on why analytics matters

Decision management expert James Taylor wins the prize for most prolific blogger from The Series. James gives us thorough summaries of great presentations on: Balancing Intuition and Analytics in Decision Making. Analytics & Innovation, Analytics in the Executive Suite. SAS Media Day customer panels on fraud detection. and optimization. By