Matrix diagonalization and singular value decomposition: Static SageMath
and dynamic ChatGPT juxtaposed
- URL: http://arxiv.org/abs/2303.17163v1
- Date: Thu, 30 Mar 2023 05:51:27 GMT
- Title: Matrix diagonalization and singular value decomposition: Static SageMath
and dynamic ChatGPT juxtaposed
- Authors: N. Karjanto
- Abstract summary: In particular, we focused on (orthogonal) diagonalization and singular value decomposition (SVD)
We also offered the possibility of exploring these topics using SageMath, a Python-based free open software computer algebra system (CAS)
By consolidating essential concepts in linear algebra and improving computational skills through effective practice, mastering these topics would become easier and mistakes could be minimized.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We investigated some difficulties that students often face when studying
linear algebra at the undergraduate level, and identified some common mistakes
and difficulties they often encountered when dealing with topics that require
algorithmic thinking skills such as matrix factorization. In particular, we
focused on (orthogonal) diagonalization and singular value decomposition (SVD).
We also offered the possibility of exploring these topics using SageMath, a
Python-based free open software computer algebra system (CAS) that has been
identified to be useful for assisting many students in the computational
process even though its output is static by nature. We then explored dynamic
ChatGPT by inquiring the chatbot about the topic, either by asking to provide
an example or to solve a problem, that is by constructing an (orthogonal)
diagonalization or SVD from a particular matrix. By consolidating essential
concepts in linear algebra and improving computational skills through effective
practice, mastering these topics would become easier and mistakes could be
minimized. Static SageMath, in particular, is a great aid for calculation
confirmation and handling tedious computations. Although dynamic ChatGPT is
relatively unreliable for solving problems in linear algebra, the mistakes it
produces could become a valuable tool for improving critical thinking skills.
Related papers
- Large Language Models for Mathematical Analysis [3.7325315394927023]
This work addresses critical gaps in mathematical reasoning and contributes to advancing trustworthy AI.
We developed the DEMI-MathAnalysis dataset, comprising proof-based problems from mathematical analysis topics.
We also designed a guiding framework to rigorously enhance LLMs' ability to solve these problems.
arXiv Detail & Related papers (2024-12-28T20:37:55Z) - Mathify: Evaluating Large Language Models on Mathematical Problem Solving Tasks [34.09857430966818]
We introduce an extensive mathematics dataset called "MathQuest" sourced from the 11th and 12th standard Mathematics NCERT textbooks.
We conduct fine-tuning experiments with three prominent large language models: LLaMA-2, WizardMath, and MAmmoTH.
Our experiments reveal that among the three models, MAmmoTH-13B emerges as the most proficient, achieving the highest level of competence in solving the presented mathematical problems.
arXiv Detail & Related papers (2024-04-19T08:45:42Z) - Symbolic Equation Solving via Reinforcement Learning [9.361474110798143]
We propose a novel deep-learning interface involving a reinforcement-learning agent that operates a symbolic stack calculator.
By construction, this system is capable of exact transformations and immune to hallucination.
arXiv Detail & Related papers (2024-01-24T13:42:24Z) - ChatGPT for Computational Topology [10.770019251470583]
ChatGPT represents a significant milestone in the field of artificial intelligence.
This work endeavors to bridge the gap between theoretical topological concepts and their practical implementation in computational topology.
arXiv Detail & Related papers (2023-10-11T15:10:07Z) - CoLA: Exploiting Compositional Structure for Automatic and Efficient
Numerical Linear Algebra [62.37017125812101]
We propose a simple but general framework for large-scale linear algebra problems in machine learning, named CoLA.
By combining a linear operator abstraction with compositional dispatch rules, CoLA automatically constructs memory and runtime efficient numerical algorithms.
We showcase its efficacy across a broad range of applications, including partial differential equations, Gaussian processes, equivariant model construction, and unsupervised learning.
arXiv Detail & Related papers (2023-09-06T14:59:38Z) - ChatGPT for Programming Numerical Methods [2.741266294612776]
ChatGPT is a large language model recently released by the OpenAI company.
We explore for the first time the capability of ChatGPT for programming numerical algorithms.
arXiv Detail & Related papers (2023-03-21T12:18:17Z) - Towards a Holistic Understanding of Mathematical Questions with
Contrastive Pre-training [65.10741459705739]
We propose a novel contrastive pre-training approach for mathematical question representations, namely QuesCo.
We first design two-level question augmentations, including content-level and structure-level, which generate literally diverse question pairs with similar purposes.
Then, to fully exploit hierarchical information of knowledge concepts, we propose a knowledge hierarchy-aware rank strategy.
arXiv Detail & Related papers (2023-01-18T14:23:29Z) - JiuZhang: A Chinese Pre-trained Language Model for Mathematical Problem
Understanding [74.12405417718054]
This paper aims to advance the mathematical intelligence of machines by presenting the first Chinese mathematical pre-trained language model(PLM)
Unlike other standard NLP tasks, mathematical texts are difficult to understand, since they involve mathematical terminology, symbols and formulas in the problem statement.
We design a novel curriculum pre-training approach for improving the learning of mathematical PLMs, consisting of both basic and advanced courses.
arXiv Detail & Related papers (2022-06-13T17:03:52Z) - GeoQA: A Geometric Question Answering Benchmark Towards Multimodal
Numerical Reasoning [172.36214872466707]
We focus on solving geometric problems, which requires a comprehensive understanding of textual descriptions, visual diagrams, and theorem knowledge.
We propose a Geometric Question Answering dataset GeoQA, containing 5,010 geometric problems with corresponding annotated programs.
arXiv Detail & Related papers (2021-05-30T12:34:17Z) - Measuring Mathematical Problem Solving With the MATH Dataset [55.4376028963537]
We introduce MATH, a dataset of 12,500 challenging competition mathematics problems.
Each problem has a full step-by-step solution which can be used to teach models to generate answer derivations and explanations.
We also contribute a large auxiliary pretraining dataset which helps teach models the fundamentals of mathematics.
arXiv Detail & Related papers (2021-03-05T18:59:39Z) - Machine Number Sense: A Dataset of Visual Arithmetic Problems for
Abstract and Relational Reasoning [95.18337034090648]
We propose a dataset, Machine Number Sense (MNS), consisting of visual arithmetic problems automatically generated using a grammar model--And-Or Graph (AOG)
These visual arithmetic problems are in the form of geometric figures.
We benchmark the MNS dataset using four predominant neural network models as baselines in this visual reasoning task.
arXiv Detail & Related papers (2020-04-25T17:14:58Z)
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.