Parsimonious Optimization

The word parsimonious means thrifty, economical, frugal, and sometimes even stingy. It is an unusual word to use when describing optimization, but it is meaningful here in two ways.Time is moneyFirst, the purpose of optimization is to minimize a function. The intent is not a meager reduction, but absolute minimization. This seems pretty stingy, but in a useful way.

Second, and of greater interest here, the process of optimization must be efficient, economical, and thrifty. That is, finding an optimized solution to a problem should take as little of your time and resources as possible. Unfortunately, in many real-world applications, time is the largest barrier to realizing the true value of automated optimization.

In theory, you should be able to find an optimized solution whenever you have a good system analysis model and an appropriate search algorithm.  But if the model requires hours or even days of CPU time for each design evaluation, and the algorithm requires a large number of evaluations, then the total time required to reach that optimized solution may turn out to be completely impractical.

Let’s consider the cost factors involved. The total CPU time needed to find an optimized solution is defined this way:

where NSOL is the number of optimization solutions performed, and the expression inside the brackets represents the total search time per iteration of the optimization solution.

Based on this formula, there are only four ways you can reduce the solution time for an optimization study. You can

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The Upside

baseball playerHigh school tryouts. College recruiting. Pro draft day. At every level of athletics, coaches face a tough pre-season decision. Select player A, a better than average athlete who has worked diligently for many years to maximize his potential under the tutelage of top coaches. Or choose player B, whose present skill level is not quite as high but who has greater raw athletic ability, is coachable and has real potential to be a superstar – a trait that coaches call “upside.”

A similar situation occurs in product development when selecting between two competing design concepts. A more optimized version of concept A may appear better than a version of concept B that has not been optimized. But concept B may have a lot more potential for improvement, a bigger upside.

The performance of a single example of a concept is not usually a good measure of the concept itself.

How optimized is concept A? What level of performance could be attained by concept B? We seldom know the answers to these questions prior to performing an optimization study.   Continue reading

When More Is Not Better

Suppose that your favorite finite element software boasted the following claims:

Directions“Over a dozen equation solvers are available to approximate the solution of your problem, and each solver contains a rich set of parameters that you can define to tune the solver’s performance. To maximize the accuracy of your solution and the efficiency of the solution process, simply choose the solver that is intended for your problem type, and then tune it properly. Though it is often not possible to classify your problem type beforehand, usually the right solver can be identified within 3-5 attempts. Then, you can use an iterative tuning process to make the solution even more accurate and efficient.”

If the above statements were true, then each finite element solution would require a full-blown research project to find the right equation solver. The added time and cost of numerous solution iterations would offset many of the benefits of the finite element method within the design process.  Continue reading

Edisonian Innovation in the 21st Century

Edison's light bulbsThomas Edison demonstrated the first long-lasting, high-quality light bulb in 1879. His successful design resulted from a long and laborious trial-and-error search for the best filament material, a process we now call the Edisonian approach.

Edison’s determined and tireless pursuit of innovation is also evident in some of his famous quotes:

“When I have fully decided that a result is worth getting I go ahead of it and make trial after trial until it comes.”

“I have not failed. I’ve just found 10,000 ways that won’t work.”

While Edison had to create physical prototypes to test each new design, modern advances in computing power and computer-aided engineering (CAE) software now make it possible to create virtual prototypes based on mathematical models. As a result, design trials are easier, faster and cheaper than ever before.    Continue reading