Jonathan Lalou's Blog

Posts Tagged ‘Optimization’

In the intricate ballet of software stewardship, where intuition waltzes with empiricism, resides the multi-armed bandit—a probabilistic oracle guiding choices amid uncertainty. Ben Halpern, co-founder of Forem and dev.to’s visionary steward, dissected this gem at dotJS 2024. A full-stack polymath blending code with community curation, Ben recounted its infusions across his odyssey—from parody O’Reilly covers viralizing memes to mutton-busting triumphs—framing bandits as bridges between artistic whimsy and scientific rigor, aligning devs with stakeholders in pursuit of optimal paths.

Ben’s prologue evoked dev.to’s genesis: Twitter-era jests birthing a creative agora, bandit logic A/B-testing post formats for engagement zeniths. The archetype—casino levers, pulls maximizing payouts—mirrors dev dilemmas: UI variants, feature rollouts, content cadences. Exploration probes unknowns; exploitation harvests proven yields. Ben advocated epsilon-greedy: baseline exploitation (1-ε pulls best arm), exploratory ventures (ε samples alternatives), ε tuning via Thompson sampling for contextual nuance.

Practical infusions abounded. Load balancing: bandit selects origins, favoring responsive backends. Feature flags: variants vie, metrics crown victors. Smoke tests: endpoint probes, failures demote. ML pipelines: hyperparameter hunts, models ascend via validation. Ben’s dev.to saga: title A/Bs, bandit-orchestrated, surfacing resonant headlines sans bias. Organizational strata: nascent projects revel in exploration—ideation fests yielding prototypes; maturity mandates exploitation—scaling victors, pruning pretenders. This lexicon fosters accord: explorers and scalers, once at odds, synchronize via phases, preempting pivots’ friction.

Caution tempered zeal: bandits thrive on voluminous outcomes, not trivial toggles; overzealous testing paralyzes. As AI cheapens variants—code gen’s bounty—feedback scaffolds intensify, bandits as arbiters ensuring quality amid abundance. Ben’s coda: wield judiciously, blending craft’s flair with datum’s discipline for endeavors audacious yet assured.

Algorithmic Essence and Variants

Ben unpacked epsilon-greedy’s equilibrium: 90% best-arm fealty, 10% novelty nudges; Thompson’s Bayesian ballet contextualizes. UCB (Upper Confidence Bound) optimism tempers regret, ideal for sparse signals—dev.to’s post tweaks, engagement echoes guiding refinements.

Embeddings in Dev Workflows

Balancing clusters bandit-route requests; flags unleash cohorts, telemetry triumphs. ML’s parameter quests, smoke’s sentinel sweeps—all bandit-bolstered. Ben’s ethos: binary pass-fails sideline; array assays exalt, infrastructure for insight paramount.

Strategic Alignment and Prudence

Projects arc: explore’s ideation inferno yields scale’s forge. Ben bridged divides—stakeholder symposia in bandit vernacular—averting misalignment. Overreach warns: grand stakes summon science; mundane mandates art’s alacrity, future’s variant deluge demanding deft discernment.

Links:

• Ben Halpern on LinkedIn
• Forem

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