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International Journal of Applied Mathematics and Computing.
Published by Asosiasi Riset Ilmu Matematika dan Sains Indonesia
ISSN : 30481988     EISSN : 3047146X     DOI : 10.62951
Core Subject : Science, Education,
Computer Science & IT Mathematics
This Journal accepts manuscripts based on empirical research, both quantitative and qualitative. This journal is a peer-reviewed and open access journal of Mathematics and Computing
Arjuna Subject : Matematika - Matematika Komputer
Articles 3 Documents
 1 
Search results for , issue "Vol. 2 No. 2 (2025): April: International Journal of Applied Mathematics and Computing" : 3 Documents clear
Determining the Price of Asian Type Call Option Contracts Using the Monte Carlo Stratified Sampling Method Susanti Marito Barus; Komang Dharmawan; Luh Putu Ida Harini
International Journal of Applied Mathematics and Computing Vol. 2 No. 2 (2025): April: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i2.188

Abstract

Determining the price of option contracts is a crucial aspect of financial markets, particularly for investors aiming to manage risk and make informed investment decisions. In this study, the price of an Asian call option is calculated using the Monte Carlo Stratified Sampling method based on the stock price data of Tesla, Inc. (TSLA) from January 2021 to December 2023. This method has been proven to reduce variance compared to the Standard Monte Carlo simulation, leading to faster price convergence and more efficient results. The parameters used in the simulation include the initial stock price (S_0), number of simulations (N), maturity time (T)dividend = 0, risk-free rate (r), strike price ( K), and volatility
Adaptive Algorithmic Simulation for Nonlinear Eigenvalue Problems in Mathematical Physics Abid Nurhuda; Ali Anhar Syi’bul Huda; Syeda Azwa Asif
International Journal of Applied Mathematics and Computing Vol. 2 No. 2 (2025): April: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i2.265

Abstract

Nonlinear eigenvalue problems (NEPs) pose significant challenges in mathematical physics and other computational applications due to their nonlinear nature, which makes analytical solutions difficult to obtain. NEPs are encountered in various scientific and engineering fields, including signal processing, electronic structure calculations, and structural optimization. This study aims to explore the application of adaptive algorithms in solving nonlinear eigenvalue problems, with a primary focus on improving accuracy and computational efficiency. The proposed method combines an iterative solver with adaptive step-size adjustment, where the step size is dynamically adjusted during the iteration based on error estimates calculated at each step. This approach enables faster convergence and significant reductions in computational time without compromising accuracy. In experiments conducted on large-scale problems, the adaptive algorithm reduced computational time by 40% faster compared to fixed-step iterative methods. The comparison between the adaptive algorithm and traditional methods showed that the adaptive algorithm is not only more efficient but also more robust when dealing with high-complexity problems. Additionally, the adaptive algorithm provides more accurate error estimates, allowing better error control throughout the iteration process. Overall, this study concludes that adaptive algorithms offer a more effective and efficient solution for complex nonlinear eigenvalue problems and can be adapted to various types of problems in scientific and engineering applications. Further research could focus on optimizing the implementation of this algorithm for larger and more complex scales.
Computational Modeling and Simulation of Nonlinear Dynamical System Stability in Applied Mathematics Aji Priyambodo; Hariyono Rakhmad; Muhammad Shakir
International Journal of Applied Mathematics and Computing Vol. 2 No. 2 (2025): April: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i2.271

Abstract

Nonlinear dynamical systems represent a fundamental area of study in applied mathematics due to their relevance across various disciplines, including physics, biology, and engineering. Their inherent complexity, characterized by phenomena such as bifurcation, chaos, and sensitivity to parameter variations, often limits the effectiveness of traditional manual analysis, particularly when addressing high-dimensional or computationally intensive models. This study aims to address these challenges by applying computational modeling and numerical simulation techniques to analyze the stability of nonlinear dynamical systems. The research employs analytical methods, including equilibrium point identification and linearization, which are then validated and extended through the fourth-order Runge-Kutta numerical method. Simulations were conducted to visualize equilibrium points, phase portraits, and parameter-driven bifurcation phenomena. The findings demonstrate a strong correspondence between analytical and numerical approaches, with minimal error margins (≤1%) observed in equilibrium point estimation, thus confirming the reliability of computational methods. Moreover, the bifurcation analysis revealed critical transitions such as pitchfork and Hopf bifurcations, which indicate sudden shifts from stability to instability behaviors that are difficult to capture through manual calculations alone. The integration of computational approaches provides clear advantages, offering systematic exploration of parameter spaces and detailed visualizations of system dynamics, thereby expanding the scope of stability analysis. In conclusion, this study emphasizes that computational modeling is not only an effective complement to analytical methods but also a necessary strategy for advancing the understanding of nonlinear dynamical systems in applied mathematics.

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