Theoretical physicist. Postdoc at the Mathematical Institute, University of Oxford, and research associate at St. Peter's College. I work with Feynman graphs, combinatorics of power series, resurgence theory, and do a lot of C++ programming. My goal is to understand the physical properties of renormalized Green functions in quantum field theory. Has anyone seen an instanton around here lately? # quantum # physics # math
Theoretical physicist. Postdoc at the Mathematical Institute, University of Oxford, and research associate at St. Peter's College. I work with Feynman graphs, combinatorics of power series, resurgence theory, and do a lot of C++ programming. My goal is to understand the physical properties of renormalized Green functions in quantum field theory. Has anyone seen an instanton around here lately? # quantum # physics # math
This #paperOfTheDay is #computerScience : "A polynomial time, numerically stable integer relation algorithm" from 1992. This is the article that introduces the PSLQ algorithm.
Given a n-component vector of real numbers x=(x_1, ..., x_n), an integer relation is a vector of integers (m_1, ..., m_n) such that m_1*x_1 + ... +m_n*x_n=0. Phrased differently, this means that one of the entries of x is a linear combination of the other entries, with rational coefficients. The task is to either find this integer vector m, or to establish that no such vector exists with entries below a certain size (since the numbers x are given to finite floating-point precision, it is always possible to find some vector m with gigantic numeric values: Say they have 100 digits, then multiply x by 10^100, and the resulting numbers will be integers within the given precision).
Before PSLQ, there had been algorithms, most notably LLL and HJLS, for the same task. The great innovation of PSLQ is the "numerically stable": It does not require much more digits for internal computations than then input data has, and it is reliable in determining that no relation exists below a given threshold. By this, it is effectively also faster than previous algorithms, because it can run at lower floating point accuracy.
PSLQ has important applications in perturbative #QuantumFieldTheory and broader #physics : Often one has complicated integrals, where one knows that the result is some rational linear combination of a finite number of transcendentals (e.g. pi, e, Values of the zeta function, sqrt(2), log(2), etc). Then, one can solve the integral numerically, and use PSLQ to find the linear combination. https://www.davidhbailey.com/dhbpapers/pslq.pdf