Search arXivSearch

arXiv · 2601.14940

Nonclassical symmetries of polynomial equations and test problems with parameters for computer algebra systems

Abstract

Nonclassical symmetries and reductions of polynomial equations and systems of polynomial equations are considered. It is shown that specific polynomial equations having "hidden" symmetries can be reduced to classical symmetric systems of polynomial equations by introducing a new additional variable. It has been established that symmetric systems of polynomial equations of mixed type, consisting of symmetric and anti-symmetric polynomials, can be transformed into simpler systems. A method is presented for solving nonclassical symmetric systems of two polynomial equations that change places when the unknowns are permuted. We study polynomial equations containing the second iteration of a given polynomial, which are reduced to nonclassical symmetric systems of equations. New higher-degree polynomial equations containing free parameters that admit solutions in radicals are found. Three such equations of the sixth and ninth degrees are further used as test problems with parameters for analyzing the capabilities of two leading computer algebra systems. It is shown that currently, the Maple and Mathematica systems do not allow us to efficiently find analytical solutions (in radicals) of polynomial equations with free parameters, but they allow us to obtain numerical solutions of equations for fixed numerical values of the parameters. The results of this work and the proposed test problems with parameters can be used to further improve existing computer algebra systems.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Inna K. Shingareva, Andrei D. Polyanin. 2026-01-21. Nonclassical symmetries of polynomial equations and test problems with parameters for computer algebra systems. https://arxiv.org/abs/2601.14940

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

How many continuous measurements are needed to learn a vector?

One can recover vectors from $\mathbb{R}^m$ with arbitrary precision, using only $\lceil \log_2(m)\rceil +1$ continuous measurements that are chosen adaptively. This surprising result is explained and discussed, and we present applications to infinite-dimensional approximation problems.

math.NA

IterativeCUR: Large Rank-Adaptive Approximation From a Small Recycled Sketch

The computation of accurate low-rank matrix approximations is central to improving the scalability of various techniques in machine learning, uncertainty quantification, and control. Traditionally, low-rank approximations are constructed using SVD-based approaches such as truncated SVD or Randomized SVD. Although these SVD approaches---especially Randomized SVD---have proven to be very computationally efficient, other low-rank approximation methods can offer even greater performance. One such approach is the CUR decomposition, which forms a low-rank approximation using direct row and column subsets of a matrix. Because CUR uses direct matrix subsets, it is also often better able to preserve native matrix structures like sparsity or non-negativity than SVD-based approaches and can facilitate data interpretation in many contexts. This paper introduces IterativeCUR, which draws on previous work in randomized numerical linear algebra to build a new algorithm that is highly competitive compared to prior work. IterativeCUR is adaptive in the sense that it takes as an input parameter the desired tolerance $ε$ and outputs (with arbitrarily high probability) an approximation of error bounded by $ε$, rather than requiring an a priori guess of the numerical rank. IterativeCUR typically runs significantly faster than both existing CUR algorithms and techniques such as Randomized SVD. Its asymptotic complexity is $\mathcal{O}(mn + (m+n)r^2)$ for an $m\times n$ matrix of output rank $r$. IterativeCUR relies on a single small sketch from the matrix that is successively downdated as the algorithm proceeds. We demonstrate through extensive experiments that IterativeCUR achieves up to $4\times$ speed-up over state-of-the-art pivoting-on-sketch approaches with no loss of accuracy, and up to $40\times$ speed-up over rank-adaptive randomized SVD approaches.

math.NA

Multigrid with Linear Storage Complexity

As the discretization error for the solution of a partial differential equation (PDE) decreases, the precision required to store the corresponding coefficients naturally increases. Storing the solution's finite element coefficients explicitly requires $\mathcal O(n \log n)$ bits of storage, where $n$ is the number of degrees of freedom (DoFs). This paper presents a full multigrid method to compute the solution in a compressed format that reduces the storage complexity of the solution and intermediate vectors to $\mathcal O(n)$ bits. This reduction allows a matrix-free implementation to solve elliptic PDEs with an overall linear space complexity. For problems limited by the memory capacity of current supercomputers, we expect a memory footprint reduction of about an order of magnitude compared to state-of-the-art mixed-precision methods. We demonstrate the applicability of our algorithm by solving two model problems. Depending on the PDE and polynomial degree, but irrespective of the problem size, the solution vector on the finest grid requires between 4 and 12 bits per DoF, and the residual and correction require 3 to 6 bits each. Additional data is stored on the coarse grids with modestly increasing bit widths toward coarser grids.

math.NA