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New lower bounds for the Schur-Siegel-Smyth trace problem

Authors: Bryce Orloski; Naser Talebizadeh Sardari; Alexander Smith 0008;

New lower bounds for the Schur-Siegel-Smyth trace problem

Abstract

We derive and implement a new way to find lower bounds on the smallest limiting trace-to-degree ratio of totally positive algebraic integers and improve the previously best known bound to 1.80203. Our method adds new constraints to Smyth’s linear programming method to decrease the number of variables required in the new problem of interest. This allows for faster convergence recovering Schur’s bound in the simplest case and Siegel’s bound in the second simplest case of our new family of bounds. We also prove the existence of a unique optimal solution to our newly phrased problem and express the optimal solution in terms of polynomials. Lastly, we solve this new problem numerically with a gradient descent algorithm to attain the new bound 1.80203.

Keywords

General theory of numerical methods in complex analysis (potential theory, etc.), Evaluation of number-theoretic constants, Mathematics - Number Theory, Applications of functional analysis in optimization, convex analysis, mathematical programming, economics, Probability (math.PR), FOS: Mathematics, Number Theory (math.NT), Mathematics - Probability

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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