Posts Tagged ‘MathematicalModeling’
[KotlinConf2018] Mathematical Modeling in Kotlin: Optimization, Machine Learning, and Data Science Applications
Lecturer
Thomas Nield is a Business Consultant at Southwest Airlines, balancing technology with operations research in airline scheduling and optimization. He is an author with O’Reilly Media, having written “Getting Started with SQL” and “Learning RxJava,” and contributes to open-source projects like RxJavaFX and RxKotlin. Relevant links: O’Reilly Profile (publications); LinkedIn Profile (professional page).
Abstract
This article explores mathematical modeling in Kotlin, addressing complex problems through discrete optimization, Bayesian techniques, and neural networks. It analyzes methodologies for scheduling, regression, and classification, contextualized in data science and operations research. Implications for production deployment, library selection, and problem-solving efficiency are discussed, emphasizing Kotlin’s refactorable features.
Introduction and Context
Mathematical modeling solves non-deterministic problems beyond brute force, such as scheduling 190 classes or optimizing train costs. Kotlin’s pragmatic features enable clear, evolvable models for production.
Context: Models underpin data science, machine learning, and operations research. Examples include constraint programming for puzzles (Sudoku) and real-world applications (airline schedules).
Methodological Approaches
Discrete optimization uses libraries like OjAlgo for linear programming (e.g., minimizing train costs with constraints). Bayesian classifiers (e.g., Naive Bayes) model probabilities for spam detection.
Neural networks: Custom implementations train on MNIST for digit recognition, using activation functions (sigmoid) and backpropagation. Kotlin’s extensions and lambdas facilitate intuitive expressions.
Graph optimization: Dijkstra’s algorithm for shortest paths, applicable to logistics.
Analysis of Techniques and Examples
Optimization: Linear models minimize objectives under constraints; graph models solve routing (e.g., traveling salesman via genetic algorithms).
Bayesian: Probabilistic inference for sentiment/email classification, leveraging word frequencies.
Neural networks: Multi-layer perceptrons for fuzzy problems (image recognition); Kotlin demystifies black boxes through custom builds.
Innovations: Kotlin’s type safety and conciseness aid refactoring; libraries like Deeplearning4j for production.
Implications and Consequences
Models enable efficient solutions; choose based on data/problem nature (optimization for constraints, networks for fuzzy data).
Consequences: Custom implementations build intuition but libraries optimize; Kotlin enhances maintainability for production.
Conclusion
Kotlin empowers mathematical modeling, bridging optimization and machine learning for practical problem-solving.
Links
- Lecture video: https://www.youtube.com/watch?v=-zTqtEcnM7A
- Lecturer’s X/Twitter: @thomasnield
- Lecturer’s LinkedIn: Thomas Nield
- Organization’s X/Twitter: @SouthwestAir
- Organization’s LinkedIn: Southwest Airlines