Cauchy: Jurnal Matematika Murni dan Aplikasi

Aims & Scope

CAUCHY publishes manuscripts that demonstrate clear mathematical novelty and methodological rigor. The journal prioritizes contributions that present new mathematical results, develop or analyze mathematical models with sound theoretical justification, or propose computational and algorithmic methods supported by rigorous mathematical analysis such as correctness, convergence, stability, complexity, or reproducibility. The scope of the journal covers, but is not limited to, the following areas: Analysis (real analysis, functional analysis, applied analysis) Algebra and number theory Geometry and topology Discrete mathematics and graph theory Optimization and operations research Probability and mathematical statistics Dynamical systems and control Mathematical modeling with rigorous formulation and validation Computational mathematics and scientific computing Fuzzy systems and uncertainty modeling Actuarial and financial mathematics Out of Scope CAUCHY maintains a strong mathematical emphasis. Manuscripts will be considered out of scope and may be desk-rejected if they primarily fall into one or more of the following categories: Application-only case studies or implementations that do not present a clear mathematical model, theoretical analysis, or mathematically justified methodology. Machine learning / data-driven papers that mainly report benchmark results or metric comparisons (e.g., accuracy, F1-score, AUC) without explicit mathematical novelty, analytical justification, or substantive methodological contribution. Routine use of standard techniques with minimal originality (e.g., applying commonly used models to a new dataset) and without a meaningful mathematical development, analysis, or insight. Software or system papers that focus on implementation details, engineering features, or interfaces without rigorous mathematical content (e.g., correctness proof, convergence/stability analysis, complexity analysis, or error bounds). Surveys or review manuscripts that are primarily narrative/tutorial, lack a systematic review protocol, provide limited critical synthesis, or do not clearly articulate research gaps and future directions. Manuscripts with insufficient rigor or transparency to support verification and reproducibility (e.g., incomplete method description, unclear assumptions, unverifiable claims).

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Subject Classification