• Communications of the Korean Mathematical Society
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• v.39 no.2
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• pp.313-329
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• 2024

An (additive) commutative monoid is called atomic if every given non-invertible element can be written as a sum of atoms (i.e., irreducible elements), in which case, such a sum is called a factorization of the given element. The number of atoms (counting repetitions) in the corresponding sum is called the length of the factorization. Following Geroldinger and Zhong, we say that an atomic monoid M is a length-finite factorization monoid if each b ∈ M has only finitely many factorizations of any prescribed length. An additive submonoid of ℝ_≥0 is called a positive monoid. Factorizations in positive monoids have been actively studied in recent years. The main purpose of this paper is to give a better understanding of the non-unique factorization phenomenon in positive monoids through the lens of the length-finite factorization property. To do so, we identify a large class of positive monoids which satisfy the length-finite factorization property. Then we compare the length-finite factorization property to the bounded and the finite factorization properties, which are two properties that have been systematically investigated for more than thirty years.

• School Mathematics
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• v.4 no.4
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• pp.581-599
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• 2002

This study investigated the factorization concept development level of 3rd grades in middle school, the success of factorization problem solving, and the completion of factorization analogy tasks and science concepts analogy tasks. This study's results are followings. 1. Based on Sfard' reification levels, we classified students' factorization concept development levels from level 0 to level 3. As the students' development level was high, they tended to succeed the factorization problems gradually. 2. Experiencing factorization tasks which made students arrange factorization expressions hating same characterization, students ' factorization problem solving was improved. And, as the students' development level was high, they tended to attend to internal structural relations in factorization analogy tasks. 3. Analogy in factorization wasn't interrelated with analogy in science concepts. It said that analogy depended on the knowledges with it.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.3
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• pp.437-447
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• 2003

We propose the methods to design the finite impulse response (FIR) and the infinite impulse response (IIR) lattice filters using Schur algorithm through the spectral factorization of the covariance matrix by circulant matrix factorization (CMF). Circulant matrix factorization is also very powerful tool used fur spectral factorization of the covariance polynomial in matrix domain to obtain the minimum phase polynomial without the polynomial root finding problem. Schur algorithm is the method for a fast Cholesky factorization of Toeplitz matrix, which easily determines the lattice filter parameters. Examples for the case of the FIR Inter and for the case of the IIR filter are included, and performance of our method check by comparing of our method and another methods (polynomial root finding and cepstral deconvolution).

• Journal of applied mathematics & informatics
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• v.26 no.3_4
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• pp.779-784
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• 2008

We show that $\mathbb{Z}[\sqrt{-p}]$ is not a unique factorization domain (UFD) but a factorization domain (FD) with a condition $1\;+\;a^2p\;=\;qr$, where a and p are positive integers and q and r are positive primes in $\mathbb{Z}$ with q < p. Using this result, we also construct several specific non-unique factorization domains which are factorization domains. Furthermore, we prove that an integral domain $\mathbb{Z}[\sqrt{-p}]$ is not a UFD but a FD for some positive integer p.

• Korean Journal of Mathematics
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• v.32 no.1
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• pp.97-107
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• 2024

Let A ⊆ B be an extension of integral domains with characteristic zero. Let H(A, B) and h(A, B) be rings of composite Hurwitz series and composite Hurwitz polynomials, respectively. We simply call H(A, B) and h(A, B) composite Hurwitz rings of A and B. In this paper, we study when H(A, B) and h(A, B) are unique factorization domains (resp., GCD-domains, finite factorization domains, bounded factorization domains).

• Journal of Digital Contents Society
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• v.19 no.4
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• pp.731-737
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• 2018

The recommendation system is actively researched for the purpose of suggesting information that users may be interested in in many fields such as contents, online commerce, social network, advertisement system, and the like. However, there are many recommendation systems that propose based on past preference data, and it is difficult to provide users with little or no data in the past. Therefore, interest in higher-order data analysis is increasing and Matrix Factorization is attracting attention. In this paper, we study and propose a comparison and replay of the Factorization Machines Leaning(FM) model which is attracting attention in the recommendation system and High-Order Factorization Machines Learning(HOFM) which is a high - dimensional data analysis.

• Korean Management Science Review
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• v.15 no.1
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• pp.87-95
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• 1998

The computational speed of interior point method depends on the speed of Cholesky factorization. The supernodal column Cholesky factorization is a fast method that performs Cholesky factorization of sparse matrices with exploiting computer's characteristics. Three steps are necessary to perform the supernodal column Cholesky factorization : symbolic factorization, creation of the elimination tree, ordering by a post-order of the elimination tree and creation of supernodes. We study performing sequences of these three steps and efficient implementation of them.

• Bulletin of the Korean Mathematical Society
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• v.37 no.3
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• pp.551-568
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• 2000

We propose new parallelizable block incomplete factorization preconditioners for a symmetric block-tridiagonal H-matrix. Theoretical properties of these block preconditioners are compared with those of block incomplete factorization preconditioners for the corresponding comparison matrix. Numerical results of the preconditioned CG(PCG) method using these block preconditioners are compared with those of PCG method using a standard incomplete factorization preconditioner to see the effectiveness of the block incomplete factorization preconditioners.

• Communications of the Korean Mathematical Society
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• v.37 no.3
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• pp.669-679
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• 2022

We provide a characterization of the positive monoids (i.e., additive submonoids of the nonnegative real numbers) that satisfy the finite factorization property. As a result, we establish that positive monoids with well-ordered generating sets satisfy the finite factorization property, while positive monoids with co-well-ordered generating sets satisfy this property if and only if they satisfy the bounded factorization property.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.1
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• pp.35-44
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• 2004

We Propose the methods to design the finite impulse response (FIR) and the infinite impulse response (IIR) lattice filters using Schur algorithm through the spectral factorization of the covariance matrix by circulant matrix factorization (CMF). Circulant matrix factorization is also very powerful tool used for spectral factorization of the covariance polynomial in matrix domain to obtain the minimum phase polynomial without the polynomial root finding problem. Schur algorithm is the method for a fast Cholesky factorization of Toeplitz matrix, which easily determines the lattice filter parameters. Examples for the case of the FIR filter and for the case of the In filter are included, and performance of our method check by comparing of our method and another methods (polynomial root finding and cepstral deconvolution).