FiniteDiff Opt fails with denoising, 2 dimensions

[PaulTalbot-INL]

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Issue Description

If a two-dimensional optimization is performed with at least 2 "numIterPerAve" denoising samples, the first run fails with a "too small dh" error. This appears to be because of an indexing error in choosing the gradient points.

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For Change Control Board: Issue Review

This review should occur before any development is performed as a response to this issue.

• 1. Is it tagged with a type: defect or improvement?
• 2. Is it tagged with a priority: critical, normal or minor?
• 3. If it will impact requirements or requirements tests, is it tagged with requirements?
• 4. If it is a defect, can it cause wrong results for users? If so an email needs to be sent to the users.
• 5. Is a rationale provided? (Such as explaining why the improvement is needed or why current code is wrong.)

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For Change Control Board: Issue Closure

This review should occur when the issue is imminently going to be closed.

• 1. If the issue is a defect, is the defect fixed?
• 2. If the issue is a defect, is the defect tested for in the regression test system? (If not explain why not.)
• 3. If the issue can impact users, has an email to the users group been written (the email should specify if the defect impacts stable or master)?
• 4. If the issue is a defect, does it impact the latest stable branch? If yes, is there any issue tagged with stable (create if needed)?
• 5. If the issue is being closed without a merge request, has an explanation of why it is being closed been provided?

[PaulTalbot-INL]

Does not provide bad results, since it crashes instead.

[alfoa]

The issue is a defect but can not cause bad results since it would end up in an error.
No email required...

Closure checklist passed