Optimizing Across Noisy Domain

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SUMMARY

This discussion focuses on optimizing a noisy 2D surface to find a maximum without computing the entire surface. Key techniques mentioned include the median filter for noise reduction and considerations for defining optimality based on the function's characteristics. The conversation emphasizes the importance of understanding the criteria for optimization, the nature of the function being optimized, and the constraints involved. Jason highlights that without additional details, providing effective solutions is challenging.

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  • Understanding of optimization techniques in noisy environments.
  • Familiarity with median filtering for noise reduction.
  • Knowledge of function characteristics in optimization problems.
  • Experience with constraints in optimization scenarios.
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  • Research advanced optimization algorithms for noisy functions.
  • Explore the application of median filters in data preprocessing.
  • Learn about defining optimality criteria in optimization problems.
  • Investigate the use of Singular Value Decomposition (SVD) in function analysis.
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Data scientists, mathematicians, and engineers involved in optimization tasks, particularly those dealing with noisy data and requiring efficient solutions for complex functions.

tangodirt
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Are there any established methods for optimizing across a 2D surface with noise? I am trying to find the maximum across a 2D surface, but the surface is extremely noisy. Ideally, I would numerically optimize a function without resorting to computing the entire surface, filtering the surface, and searching for a maximum, but I am not finding any established methods for this.

Any ideas?
 
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'Optimize' can mean almost anything. The right thing to do will strongly depend on

1. The criterion by which you are defining optimality ,

2. The form of the expression you are optimizing, and

3. Any other detauls that matter: constraints, continuous or discrete space, whether this is somethig that must be solved many time very quickly or if it just done once in awhile and can run a long time to converge, etc.

Unless you provide more details folks here cannot do much to help you.

Jason
 
jasonRF said:
'Optimize' can mean almost anything. The right thing to do will strongly depend on

1. The criterion by which you are defining optimality ,

2. The form of the expression you are optimizing, and

3. Any other detauls that matter: constraints, continuous or discrete space, whether this is somethig that must be solved many time very quickly or if it just done once in awhile and can run a long time to converge, etc.

Unless you provide more details folks here cannot do much to help you.

Jason

1. Maximize/minimize over a known domain.

2. It is a generic function. A black box with two inputs that returns an output that is noisy.

3. Something that needs to be solved many times, very quickly.

Think of this as Excel's "Solver", but with a noisy function.
 
I've never used excel's solver - you aren't describing what 'optimal' means. If your function is noisy, the maximum or minimum will likely be due to noise, not what you care about. So ... what does 'optimal' mean in this instance? What do you know about the problem (characteristics of noise, etc.)?

jason
 
Would the SVD be helpful? It would presumably still require you to compute the entire surface, though.
 

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