A quadratic programming model is an optimization model with **n** decision variables and **m** linear constraints, and of the form:

Minimize Z=12xTQx+cTx

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Subject to: Ax≥b

x≥0

Where **x** is the n by 1 column vector of decision variables and **x**T is its transpose, **Q** is an n by n symmetric matrix of the objective parameters, **c** is an n by 1 vector of additional objective parameters, **A** is an m by n matrix of constraints parameters, and **b** is an m by 1 vector of constraints’ right hand sides.

- Explain how quadratic programming is used in the real world. Provide a specific example from your own line of work, or a line of work that you find particularly interesting. Indicate explicitly and qualitatively what Z,
**x**,**Q**,**C**,**A**, and b are in your example. - Describe how you would handle solving a quadratic programming problem in which some of the problem parameters are random variables as opposed to being constant values. Make sure that you include a specific example of application in your response.