Optimizing Across Noisy Domain

  • Thread starter Thread starter tangodirt
  • Start date Start date
  • Tags Tags
    Domain
AI Thread Summary
Established methods for optimizing across a noisy 2D surface include filtering techniques like the median filter, but the effectiveness depends on specific criteria for optimality, the nature of the expression being optimized, and additional details such as constraints and the need for speed in solving. The discussion emphasizes the importance of defining what "optimal" means in the context of a noisy function, as noise can significantly affect the perceived maximum or minimum. Suggestions include considering the characteristics of the noise and potentially using techniques like Singular Value Decomposition (SVD), although this may still require computing the entire surface. Without more details about the problem, it's challenging to provide targeted advice. Clarifying the optimization criteria and noise characteristics is essential for effective solutions.
tangodirt
Messages
51
Reaction score
1
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?
 
Mathematics news on Phys.org
'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.
 
Insights auto threads is broken atm, so I'm manually creating these for new Insight articles. In Dirac’s Principles of Quantum Mechanics published in 1930 he introduced a “convenient notation” he referred to as a “delta function” which he treated as a continuum analog to the discrete Kronecker delta. The Kronecker delta is simply the indexed components of the identity operator in matrix algebra Source: https://www.physicsforums.com/insights/what-exactly-is-diracs-delta-function/ by...
Fermat's Last Theorem has long been one of the most famous mathematical problems, and is now one of the most famous theorems. It simply states that the equation $$ a^n+b^n=c^n $$ has no solutions with positive integers if ##n>2.## It was named after Pierre de Fermat (1607-1665). The problem itself stems from the book Arithmetica by Diophantus of Alexandria. It gained popularity because Fermat noted in his copy "Cubum autem in duos cubos, aut quadratoquadratum in duos quadratoquadratos, et...
Thread 'Imaginary Pythagorus'
I posted this in the Lame Math thread, but it's got me thinking. Is there any validity to this? Or is it really just a mathematical trick? Naively, I see that i2 + plus 12 does equal zero2. But does this have a meaning? I know one can treat the imaginary number line as just another axis like the reals, but does that mean this does represent a triangle in the complex plane with a hypotenuse of length zero? Ibix offered a rendering of the diagram using what I assume is matrix* notation...
Back
Top