# Should we always search for the global minima in our optimization problem?

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I am trying to fit a custom equation to a series of data. When I change the startpoints the obtained fitting parameters and the fitted curve to data changes. I am wondering should we always search for the global minima in our fitting (optimization) problem by using e.g. multistart or global search?

Thank you in advance!

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John D'Errico
il 9 Giu 2022

Modificato: John D'Errico
il 9 Giu 2022

Yes. And, no. Amazing how the anwer is often, well, it depends...

You do want to find the true global min. Yes, that makes sense, as sort of a truism. But many problems are trivially solved without making the larger amount of effort to find a global min. Doing a global search takes more time. So do you ALWAYS want to waste time that need not be wasted? Of course not.

As well, in some cases, global solvers are not as good at zeroing into an accurate solution, whereas other tools will be rapidly convergent to the solution when they get near to it. So you may get a few less decimal places in the model parameters. And sometimes, those extra decimal places are really important.

It is often the case that merely having good starting values is sufficient to find the correct solution, without need for recourse to a global solver.

Looking at the converse, yes, a global solver can help. Sometimes. It makes things simpler for the novice. If you have no clue where to start a solver, a global solver typically just does not care.

A metaphor I often use for these questions is to think of the utilities in MATLAB, the various toolboxes and the tools in them, as if they were tools in your workshop. For example, in my shop, I have many screwdrivers, wrenches, hammers, saws. I know how to use all of them well, when to use one of the other. But supppose that tomorrow, XYZ comany introduces a magical tool that does everything for you. (They like to do that around the holidays.) So a tool that can turn any bolt, screw, cut any piece of wood for you, drive any nail. Not only that, but it is battery powered, with a lifetime battery!

What are the odds that the XYZ magic tool does its job only barely acceptably? Some of the time, it works nicely enough, but some of the times, it leaves a massive mess on your hands, destroying the part you are working on?

The point is, if you understand the tools in your workshop, if you understand the problem you are working on, then you will know which tool to use. You will not need to resort to the XYZ magic tool.

So understand the tools. Spend the time to learn how each optimizer works, what it does well, what it does poorly. What each one can do, and when to use it. Do the same time on the problem you want to solve. If you can, plot it. Look at how it works. What do the parameters mean? What are good starting values. PLOT YOUR DATA. Do your thinking in advance.

Of course, you don't need to do any of those things. But I will say that the person who wanders into my workshop and just starts grabbing random tools will probably soon find they are missing a hand, or at least some fingers. The same thing applies to the utter novice in MATLAB. That person may well find a global optimizer a good first choice, as it removes some of the need to understand the tools you are using. But I've seen as many novice MATLAB programmers having problems using the global optimizers as any other tool.

There is simply nothing better than understanding the tools you will use.

### Più risposte (3)

Matt J
il 6 Giu 2022

Modificato: Matt J
il 6 Giu 2022

I am wondering should we always search for the global minima in our fitting (optimization) problem by using e.g. multistart or global search?

If when you say "we", you mean "everybody", then no. There are plenty of people who work with models for which it is not challenging to find the globally best fit without GlobalSearch. For example, polynomial and spline fitting problems do not have non-global minima, so it isn't something you have to worry about in scenarios like that.

However, your particular model may be unusually difficult and therefor may warrant a global optimizer if you are finding it challenging to identify a good initial start point. To be sure that your difficulties are not self-inflicted, however, it might be advisable to present a mathematical description (i.e., equations, not code) of the curve fitting problem you are facing. The community may then be able to recommend initialization strategies.

Walter Roberson
il 6 Giu 2022

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Sam Chak
il 6 Giu 2022

Hi @Shaily_T

For academic reasons, maybe a 'Yes' if your team want to demonstrate the novelty in the optimization problem that no one has done this before.

In most commercial industrial practices, then it's a 'No' because your team must to find the optimal solution in the operating region of interest only, so that the company can make cost-effective products at a profit in the desired specified time frame.

##### 16 Commenti

Walter Roberson
il 9 Giu 2022

It might perhaps make sense in some situations to have a sum of Gaussian that each have the same a and c, but different b (b being the center position.)

If that is your situation, then would the center positions b happen to be at regular intervals? Known interval?

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