Optimizing Across Noisy Domain

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In summary, the conversation discusses various methods for optimizing across a 2D surface with noise. The options mentioned include using a median filter, defining optimality criteria, considering constraints and space, and using the SVD. However, more details are needed to determine the best approach for optimizing a generic, noisy function quickly and accurately.
  • #1
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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  • #3
'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
 
  • #4
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.
 
  • #5
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
 
  • #6
Would the SVD be helpful? It would presumably still require you to compute the entire surface, though.
 

FAQ: Optimizing Across Noisy Domain

What is meant by "noisy domain" in optimization?

A noisy domain refers to a system or process in which the output is subject to random or unpredictable variations. In optimization, this means that the function being optimized may produce different results even when the input values are kept constant due to inherent randomness or external influences.

Why is optimizing across noisy domain challenging?

Optimizing across a noisy domain is challenging because the random or unpredictable variations in the output can make it difficult to accurately determine the optimal solution. The noise can also make it harder to differentiate between different solutions and can lead to misleading results.

How can noise be addressed in optimization?

There are several approaches that can be used to address noise in optimization. One method is to use robust optimization techniques that can account for uncertainty and variations in the output. Another approach is to collect more data and use statistical methods to filter out the noise and identify patterns in the data.

What are some common applications of optimizing across noisy domain?

Optimizing across noisy domains is often used in various fields such as finance, engineering, and machine learning. For example, it can be used to optimize investment portfolios in the stock market, improve the performance of complex systems, and train machine learning models with noisy data.

What are some potential drawbacks of optimizing across noisy domain?

One potential drawback of optimizing across noisy domains is that it can be computationally expensive and time-consuming. It may also require a large amount of data and resources to effectively address the noise. Additionally, there is always a risk of overfitting the data and producing suboptimal results if the noise is not properly accounted for.

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