Convex set for similarity constraint

In summary, the conversation discusses the search for a projector onto a convex set defined in a non-explicit way, for a seismic processing application. The set membership requires signals in a Hilbert Space H to correlate with each other above a scalar \rho, given that a known signal \textbf{w} is in the set. The set is symbolically represented as \textit{C} and requires further clarification on how it references itself in its definition. Help in finding an equivalent formulation for \textit{C} to make finding the projector easier is also requested.
  • #1
Squatchmichae
12
0
I am trying to ultimately find the projector onto a convex set defined in a non-explicit way, for a seismic processing application.

The signals in question are members of some Hilbert Space H and the set membership requires that they must correlate with each other above some scalar [itex]\rho[/itex], given that the known signal [itex]\textbf{w}[/itex] is in the set. Symbolically, I want to find a projector [itex]\textit{P}[/itex] onto the convex set [itex]\textit{C}[/itex]:

\begin{equation}

C = \left\{\mathbf{u}(t) : \left\langle \hat{\mathbf{u}}(t),\hat{\mathbf{v}}(t) \right\rangle \geq\rho_{0}, \forall \mathbf{v}(t) \in C, \quad where \quad \mathbf{w}(t) \in C \right\},

\end{equation}

Any intermediate help is appreciated, i.e., is there an equivalent way to formulate this set, that make finding the projector easier?
 
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  • #2
I'm a bit confused by how C references itself in its definition.
 
  • #3
Office_Shredder said:
I'm a bit confused by how C references itself in its definition.

I understand the confusion--that is what makes the defining characteristic a little awkward. The basic idea is this: each element in the convex set [itex]\textit{C}[/itex] must correlate with every other element above [itex]\rho[/itex]. But we also know that a given (known) element [itex]\textbf{w}(t)[/itex] is contained in [itex]\textit{C}[/itex]. Is that less confusing of a statement?
 

Related to Convex set for similarity constraint

What is a convex set?

A convex set is a set of points where any line segment connecting any two points in the set lies entirely within the set. In other words, a convex set is a set that does not have any indentations or holes.

What is the importance of convex sets in similarity constraint?

Convex sets are important in similarity constraint because they allow for a clear and concise way to define the boundaries and constraints of a problem. This allows for more efficient and effective solutions to be found.

How are convex sets used in data analysis?

Convex sets are commonly used in data analysis to represent and analyze data that follows a particular pattern or distribution. They can be used to define boundaries for classification problems, or to identify outliers in a dataset.

Can a convex set have more than one dimension?

Yes, a convex set can have more than one dimension. In fact, most real-world applications of convex sets involve multiple dimensions, such as in machine learning and optimization problems.

What are some common examples of convex sets?

Some common examples of convex sets include circles, spheres, cones, and polygons. In data analysis, convex sets can also represent clusters of data points that follow a certain pattern or distribution.

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