Can small changes in fundamental constants affect the properties of water?

In summary: Yeah, when ε=0 the spectrum is continuum and otherwise its discrete...So for an Hamiltonian H(ε) = H0 + ε H' we have to find necessary and sufficient conditions for a smooth limit ε → 0 for spectrum and eigenstates.
  • #1
hilbert2
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Suppose we have a matrix A that has eigenvalues λ1, λ2, λ3,... Matrix B is a matrix that has "very small" matrix elements. Then we could expect that the eigenvalues of sum matrix A + B would be very close to the eigenvalues λi. But this is not the case. The eigenvalues of a matrix are not necessarily stable with respect to small changes in the matrix elements, and a relatively small change in them could displace the eigenvalues considerably in the complex plane. This is because the eigenvalues are the zeroes of characteristic polynomial, and the roots of a polynomial are not always stable with respect to small changes in the coefficients of its terms.

For hermitian matrices one can prove (AFAIK) a theorem that states that a small perturbation that keeps the matrix hermitian causes a correspondingly small change in the eigenvalues.

Does this property also always hold in the infinite-dimensional case, i.e. finding the eigenvalues of a hermitian operator acting in Hilbert space? Suppose we have a hamiltonian operator for a macroscopic system of very many (like NA) particles interacting by the Lennard-Jones potential for example. Does a small change in the LJ parameters necessarily result in a small change in the predicted thermodynamic properties of the system?

I was reading about the anthropic principle and an article listed the lower density of ice as compared to the density of liquid water as an "anthropic coincidence". Is it possible that a very small change in values of fundamental constants (Planck constant, elementary charge,...) could remove this special property of water?
 
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  • #2
I think the problem is how to define "small perturbation" for an operator. This cannot be done by looking at a small parameter only. In the finite dimensional example one could write A+B = A+εb with small ε and study the solutions λ(ε) of |A+εb - λ1| = 0 as functions of ε for small ε≈0.

In the infinite dimensional example I don't see a general approach. Look the Hamiltonians H = p2 and H(ε) = p2 + εx2. They do not even exist in the same Hilbert space, and there is no smooth limit ε → 0 for the spectrum.
 
  • #3
tom.stoer said:
Look the Hamiltonians H = p2 and H(ε) = p2 + εx2. They do not even exist in the same Hilbert space, and there is no smooth limit ε → 0 for the spectrum.

Yeah, when ε=0 the spectrum is continuum and otherwise its discrete...
 
  • #4
So for an Hamiltonian H(ε) = H0 + ε H' we have to find necessary and sufficient conditions for a smooth limit ε → 0 for spectrum and eigenstates.
 

What is the definition of eigenvalues?

Eigenvalues are a set of numbers associated with a linear transformation or a matrix that describes the scale factor of the transformation in a particular direction.

Why is the stability of eigenvalues important?

The stability of eigenvalues is important because it determines the behavior and characteristics of a system. It helps in understanding the long-term behavior of a system and predicting any changes that may occur.

What factors affect the stability of eigenvalues?

The stability of eigenvalues can be affected by changes in the coefficients of the matrix, the initial conditions, and the type of system. It can also be influenced by external factors such as noise or disturbances.

How is the stability of eigenvalues analyzed?

The stability of eigenvalues is analyzed by computing the eigenvalues and eigenvectors of a system and determining their relationships. This can be done through various methods such as diagonalization, Jordan canonical form, or using numerical techniques.

What are the practical applications of understanding the stability of eigenvalues?

Understanding the stability of eigenvalues is important in various fields such as physics, engineering, and economics. It is used in analyzing systems such as electrical circuits, control systems, and economic models. It also helps in designing stable and robust systems.

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