Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userInvertible field transformations with derivatives: necessary and sufficient conditions
114
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
We formulate explicitly the necessary and sufficient conditions for the local invertibility of a field transformation involving derivative terms. Our approach is to apply the method of characteristics of differential equations, by treating such a transformation as differential equations that give new variables in terms of original ones. The obtained results generalise the well-known and widely used inverse function theorem. Taking into account that field transformations are ubiquitous in modern physics and mathematics, our criteria for invertibility will find many useful applications.
rate research
Read More
We discuss a field transformation from fields $psi_a$ to other fields $phi_i$ that involves derivatives, $phi_i = bar phi_i(psi_a, partial_alpha psi_a, ldots ;x^mu)$, and derive conditions for this transformation to be invertible, primarily focusing on the simplest case that the transformation maps between a pair of two fields and involves up to their first derivatives. General field transformation of this type changes number of degrees of freedom, hence for the transformation to be invertible, it must satisfy certain degeneracy conditions so that additional degrees of freedom do not appear. Our derivation of necessary and sufficient conditions for invertible transformation is based on the method of characteristics, which is used to count the number of independent solutions of a given differential equation. As applications of the invertibility conditions, we show some non-trivial examples of the invertible field transformations with derivatives, and also give a rigorous proof that a simple extension of the disformal transformation involving a second derivative of the scalar field is not invertible.
Quantum supermaps are a higher-order generalization of quantum maps, taking quantum maps to quantum maps. It is known that any completely positive, trace non-increasing (CPTNI) map can be performed as part of a quantum measurement. By providing an explicit counterexample we show that, instead, not every quantum supermap sending a quantum channel to a CPTNI map can be realized in a measurement on quantum channels. We find that the supermaps that can be implemented in this way are exactly those transforming quantum channels into CPTNI maps even when tensored with the identity supermap. We link this result to the fact that the principle of causality fails in the theory of quantum supermaps.
Matrix regularity is a key to various problems in applied mathematics. The sufficient conditions, used for checking regularity of interval parametric matrices, usually fail in case of large parameter intervals. We present necessary and sufficient conditions for regularity of interval parametric matrices in terms of boundary parametric hypersurfaces, parametric solution sets, determinants, real spectral radiuses. The initial n-dimensional problem involving K interval parameters is replaced by numerous problems involving 1<= t <= min(n-1, K) interval parameters, in particular t=1 is most attractive. The advantages of the proposed methodology are discussed along with its application for finding the interval hull solution to interval parametric linear system and for determining the regularity radius of an interval parametric matrix.
Convergence of the gradient descent algorithm has been attracting renewed interest due to its utility in deep learning applications. Even as multiple variants of gradient descent were proposed, the assumption that the gradient of the objective is Lipschitz continuous remained an integral part of the analysis until recently. In this work, we look at convergence analysis by focusing on a property that we term as concavifiability, instead of Lipschitz continuity of gradients. We show that concavifiability is a necessary and sufficient condition to satisfy the upper quadratic approximation which is key in proving that the objective function decreases after every gradient descent update. We also show that any gradient Lipschitz function satisfies concavifiability. A constant known as the concavifier analogous to the gradient Lipschitz constant is derived which is indicative of the optimal step size. As an application, we demonstrate the utility of finding the concavifier the in convergence of gradient descent through an example inspired by neural networks. We derive bounds on the concavifier to obtain a fixed step size for a single hidden layer ReLU network.
In a previous work, we introduced the Collatz polynomials; these are the polynomials $left[P_N(z)right]_{Ninmathbb{N}}$ such that $left[z^0right]P_N = N$ and $left[z^{k+1}right]P_N = cleft(left[z^kright]P_Nright)$, where $c:mathbb{N}rightarrow mathbb{N}$ is the Collatz function $1rightarrow 0$, $2nrightarrow n$, $2n+1rightarrow 3n+2$ (for example, $P_5(z) = 5 + 8z + 4z^2 + 2z^3 + z^4$). In this article, we prove that all zeros of $P_N$ (which we call Collatz zeros) lie in an annulus centered at the origin, with outer radius 2 and inner radius a function of the largest odd iterate of $N$. Moreover, using an extension of the Enestrom-Kakeya Theorem, we prove that $|z| = 2$ for a root of $P_N$ if and only if the Collatz trajectory of $N$ has a certain form; as a corollary, the set of $N$ for which our upper bound is an equality is sparse in $mathbb{N}$. Inspired by these results, we close with some questions for further study.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
