Looking for tighter bound on symmetric PSD matrices products

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SUMMARY

The discussion focuses on obtaining a tighter upper bound for the expression involving symmetric positive semidefinite (PSD) matrices K and L of size N*N, with entries constrained to the interval [0,1]. The initial bound derived using triangular inequality is 12N - 13, which is deemed excessively loose. Empirical observations suggest that a more accurate constant coefficient is closer to 1N. Participants are encouraged to explore linear algebra properties that may connect the components of the equation to refine the bound further.

PREREQUISITES
  • Understanding of symmetric positive semidefinite (PSD) matrices
  • Familiarity with element-wise multiplication of matrices
  • Knowledge of triangular inequality in linear algebra
  • Proficiency in summation notation for matrices
NEXT STEPS
  • Investigate linear algebra properties relevant to symmetric PSD matrices
  • Explore tighter bounds in matrix inequalities
  • Learn about empirical methods for bounding matrix expressions
  • Review the implications of matrix entries constrained to [0,1]
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Mathematicians, data scientists, and researchers working with linear algebra, particularly those focused on matrix inequalities and optimization problems involving symmetric PSD matrices.

Yossi
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Homework Statement


Let K and L be symmetric PSD matrices of size N*N, with all entries in [0,1]. Let i be any number in 1...N and K’, L’ be two new symmetric PSD matrices, each with only row i and column i different from K and L. I would like to obtain an upper bound of the equation below: where .∗ is an element-wise multiply.

Homework Equations


|[sum(K' .∗ L') − sum(K .∗ L)] − (2/N)*[sum(K'L'') − sum(KL)] + (1/N*N)*[sum(K')sum(L') − sum(K)sum(L)]|

See attached for equation in LaTex/PDF.

The Attempt at a Solution


Using simple triangular inequality and bounding the three square brackets respectively,
I can bound the above with 12N − 13.
However, this is extremely loose.
Empirical experiments show that the constant coefficient should be much lower, probably closer to 1N.

Can you suggest any linear algebra properties connecting the equation's parts - which may help me get a tighter bound?
 

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Yossi said:

Homework Statement


Let K and L be symmetric PSD matrices of size N*N, with all entries in [0,1]. Let i be any number in 1...N and K’, L’ be two new symmetric PSD matrices, each with only row i and column i different from K and L. I would like to obtain an upper bound of the equation below: where .∗ is an element-wise multiply.

Homework Equations


|[sum(K' .∗ L') − sum(K .∗ L)] − (2/N)*[sum(K'L'') − sum(KL)] + (1/N*N)*[sum(K')sum(L') − sum(K)sum(L)]|

See attached for equation in LaTex/PDF.

The Attempt at a Solution


Using simple triangular inequality and bounding the three square brackets respectively,
I can bound the above with 12N − 13.
However, this is extremely loose.
Empirical experiments show that the constant coefficient should be much lower, probably closer to 1N.

Can you suggest any linear algebra properties connecting the equation's parts - which may help me get a tighter bound?

Does ##\sum(A)## mean ##\sum_i \sum_j a_{ij}## for matrix ##A = (a_{ij})##? When you say that each of ##K', L'##have only row ##i## and column ##i## different from ##K,L##, do you mean that ##K'## has both its row and column ##i## different from ##K## and that ##L'## has both its row and its column ##i## different from ##L##? I think that is what you mean, but it is worth checking. Are all the elements of ##K', L'## also in ##[0,1]##?
 

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