Formulating Linear Constraints on a Matrix

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Discussion Overview

The discussion revolves around formulating linear constraints on a symmetric matrix, specifically addressing how to represent these constraints in a matrix form while also considering the context of semidefinite programming (SDP). Participants explore the relationship between matrix entries and linear equations.

Discussion Character

  • Exploratory
  • Technical explanation
  • Mathematical reasoning

Main Points Raised

  • One participant presents a set of linear constraints for a symmetric matrix and seeks guidance on how to represent these constraints effectively.
  • The participant expresses confusion regarding the formulation of the matrix from a vector of its entries and the implications of the constraints.
  • Another participant suggests that by letting p_{12} = p, the value of p_{33} can be expressed as -1 - 2p, leading to a specific matrix representation.
  • There is a reflection on the simplicity of the solution after initially struggling with the problem, indicating a realization of algebraic dependence in the constraints.

Areas of Agreement / Disagreement

Participants do not explicitly express disagreement; however, the initial confusion and subsequent realization suggest a process of exploration rather than a consensus on a singular method of representation.

Contextual Notes

The discussion touches on the formulation of constraints in the context of semidefinite programming, but does not resolve the broader implications or limitations of these formulations.

JeSuisConf
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Hi everyone. I have this problem which I am trying to formulate. Basically, I have the following linear constraints:

<br /> p_{11} = 2<br />
<br /> p_{22} = 5<br />
<br /> p_{33}+2p_{12}=-1<br />
<br /> 2p_{13} =2<br />
<br /> 2p_{23} = 0<br />

And these are for the symmetric matrix

<br /> \mathbf{P} =<br /> \left( \begin{array}{ccc}<br /> p_{11} &amp; p_{12} &amp; p_{13} \\<br /> p_{12} &amp; p_{22} &amp; p_{23} \\<br /> p_{13} &amp; p_{23} &amp; p_{33} \end{array} \right)<br />

I would like to formulate a way to represent the linear constraints and \mathbf{P} as a matrix at the same time.

I can do this using \mathbf{P} or a vector of the entries of \mathbf{P}. The linear constraints are easy if I use a vector (\mathbf{Ap}=\mathbf{b}, but then I don't know how to represent \mathbf{P} as a matrix from the vector! And if I leave \mathbf{P} as a matrix, all the constraints are easy to formulate except p_{33}+2p_{12}=-1. Can anyone help me figure this out?

If anyone's curious, I'm trying to solve for \mathbf{P} over the cone of PSD matrices using SDP. But I am entirely new to SDP and I'm scratching my head formulating this problem. I feel stupid right now :'(
 
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I answered my own question... and the answer has to do with algebraic dependence in the structure of the problem, and how SDP problems are formulated. I just thought I was missing something obvious ... hence my convoluted search.
 
Well, I'm glad you answered it. It looks simple to me: I you let p_{12}= p then p_{33}= -1- 2p and all other values are essentially given:
\begin{pmatrix}2 &amp; p &amp; 1 \\ p &amp; 5 &amp; 0 \\1 &amp; 0 &amp; -1-2p\end{pmatrix}
 
HallsofIvy said:
Well, I'm glad you answered it. It looks simple to me: I you let p_{12}= p then p_{33}= -1- 2p and all other values are essentially given:
\begin{pmatrix}2 &amp; p &amp; 1 \\ p &amp; 5 &amp; 0 \\1 &amp; 0 &amp; -1-2p\end{pmatrix}

That's exactly what came out in the end. I'm not sure why I didn't see it at first... I always seem to manage to do things in the most roundabout way.
 

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