Simplicial techniques for operator solutions of linear constraint
systems
- URL: http://arxiv.org/abs/2305.07974v1
- Date: Sat, 13 May 2023 17:34:29 GMT
- Title: Simplicial techniques for operator solutions of linear constraint
systems
- Authors: Ho Yiu Chung, Cihan Okay, Igor Sikora
- Abstract summary: We use the theory of simplicial sets to develop a framework for studying operator solutions of linear systems.
Within our framework, we introduce a new class of linear systems that come from simplicial sets.
We provide significant evidence for a conjecture stating that for odd $d$ every linear system admitting a solution in a group admits a solution in $ZZ_d$.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: A linear constraint system is specified by linear equations over the group
$\ZZ_d$ of integers modulo $d$. Their operator solutions play an important role
in the study of quantum contextuality and non-local games. In this paper, we
use the theory of simplicial sets to develop a framework for studying operator
solutions of linear systems. Our approach refines the well-known
group-theoretical approach based on solution groups by identifying these groups
as algebraic invariants closely related to the fundamental group of a space. In
this respect, our approach also makes a connection to the earlier homotopical
approach based on cell complexes. Within our framework, we introduce a new
class of linear systems that come from simplicial sets and show that any linear
system can be reduced to one of that form. Then we specialize in linear systems
that are associated with groups. We provide significant evidence for a
conjecture stating that for odd $d$ every linear system admitting a solution in
a group admits a solution in $\ZZ_d$.
Related papers
- Algebraic method of group classification for semi-normalized classes of differential equations [0.0]
We prove the important theorems on factoring out symmetry groups and invariance algebras of systems from semi-normalized classes.
Nontrivial examples of classes that arise in real-world applications are provided.
arXiv Detail & Related papers (2024-08-29T20:42:04Z) - On Poles and Zeros of Linear Quantum Systems [2.479281189998068]
Non-commutative nature of quantum mechanics imposes fundamental constraints on system dynamics.
This paper investigates zeros and poses of linear quantum systems.
arXiv Detail & Related papers (2024-08-06T13:19:18Z) - An L-BFGS-B approach for linear and nonlinear system identification under $\ell_1$- and group-Lasso regularization [0.0]
We propose a very efficient numerical method for identifying linear and nonlinear discrete-time state-space models.
A Python implementation of the proposed identification method is available in the package jax-sysid.
arXiv Detail & Related papers (2024-03-06T16:17:34Z) - Reconstructing $S$-matrix Phases with Machine Learning [49.1574468325115]
We apply modern machine learning techniques to studying the unitarity constraint.
We find a new phase-ambiguous solution which pushes the known limit on such solutions significantly beyond the previous bound.
arXiv Detail & Related papers (2023-08-18T10:29:26Z) - On the solvability of weakly linear systems of fuzzy relation equations [0.0]
Systems of fuzzy relation equations and inequalities in which an unknown fuzzy relation is on the one side of the equation or inequality are linear systems.
This paper describes the set of fuzzy relations that solve weakly linear systems to a certain degree and provides ways to compute them.
arXiv Detail & Related papers (2022-05-25T16:59:48Z) - Universal and data-adaptive algorithms for model selection in linear
contextual bandits [52.47796554359261]
We consider the simplest non-trivial instance of model-selection: distinguishing a simple multi-armed bandit problem from a linear contextual bandit problem.
We introduce new algorithms that explore in a data-adaptive manner and provide guarantees of the form $mathcalO(dalpha T1- alpha)$.
Our approach extends to model selection among nested linear contextual bandits under some additional assumptions.
arXiv Detail & Related papers (2021-11-08T18:05:35Z) - Deep Learning Approximation of Diffeomorphisms via Linear-Control
Systems [91.3755431537592]
We consider a control system of the form $dot x = sum_i=1lF_i(x)u_i$, with linear dependence in the controls.
We use the corresponding flow to approximate the action of a diffeomorphism on a compact ensemble of points.
arXiv Detail & Related papers (2021-10-24T08:57:46Z) - A Practical Method for Constructing Equivariant Multilayer Perceptrons
for Arbitrary Matrix Groups [115.58550697886987]
We provide a completely general algorithm for solving for the equivariant layers of matrix groups.
In addition to recovering solutions from other works as special cases, we construct multilayer perceptrons equivariant to multiple groups that have never been tackled before.
Our approach outperforms non-equivariant baselines, with applications to particle physics and dynamical systems.
arXiv Detail & Related papers (2021-04-19T17:21:54Z) - Bilinear Classes: A Structural Framework for Provable Generalization in
RL [119.42509700822484]
Bilinear Classes is a new structural framework which permits generalization in reinforcement learning.
The framework incorporates nearly all existing models in which a sample complexity is achievable.
Our main result provides an RL algorithm which has sample complexity for Bilinear Classes.
arXiv Detail & Related papers (2021-03-19T16:34:20Z) - On the Adversarial Robustness of LASSO Based Feature Selection [72.54211869067979]
In the considered model, there is a malicious adversary who can observe the whole dataset, and then will carefully modify the response values or the feature matrix.
We formulate the modification strategy of the adversary as a bi-level optimization problem.
Numerical examples with synthetic and real data illustrate that our method is efficient and effective.
arXiv Detail & Related papers (2020-10-20T05:51:26Z) - Probabilistic Linear Solvers for Machine Learning [32.05287257207481]
We propose a class of linear solvers which jointly infer the matrix, its inverse and the solution from matrix-vector product observations.
We demonstrate how to incorporate prior spectral information in order to calibrate uncertainty and experimentally showcase the potential of such solvers for machine learning.
arXiv Detail & Related papers (2020-10-19T17:29:16Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.