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Computational Mathematics

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I apply computational mathematics to analyze algorithms, data growth, probability, and system behavior in real software. This work focuses on practical reasoning rather than formal proof, supporting clear decisions around performance, structure, and correctness.

Summary

Computational mathematics is the math that shows up naturally when writing and reasoning about software.

It includes understanding algorithms and their costs, working with vectors and numerical primitives, reasoning about probability and statistics, and thinking in terms of sets, relationships, and state. This also includes low-level representations such as binary and hexadecimal, where numerical structure directly affects behavior.

My work applies this mathematics pragmatically to guide design decisions, avoid hidden complexity, and ensure that software behaves predictably as it grows.

Details

Service Overview

Computational mathematics is the applied mathematics used to reason about software systems as they are built and executed. Rather than focusing on formal proofs or abstract theory, this practice centers on the mathematical thinking that naturally arises when working with algorithms, data, and computation.

This includes understanding how algorithms behave as input grows, how data accumulates over time, and how numerical representation affects performance and correctness. The emphasis is on usable insight rather than mathematical completeness.

Algorithms and Complexity

A core component of computational mathematics is reasoning about algorithmic cost and complexity. This includes understanding time and space tradeoffs, recognizing growth patterns, and identifying when an approach will become inefficient or unstable at scale.

Probability and Statistics

Probability and statistics are used pragmatically to reason about uncertainty, distribution, and error rather than to produce formal models. This supports decision-making around reliability, approximation, and system behavior under imperfect conditions.

Sets, Structures, and Primitives

Many software systems are fundamentally about sets, relationships, and state. I apply set-based reasoning, relational thinking, and numerical primitives to model data, constrain behavior, and reason about correctness.

Numerical Representation

Low-level numerical representation—such as binary and hexadecimal—plays a direct role in how systems behave. Understanding these representations helps avoid subtle errors and supports clearer reasoning about memory, encoding, and performance.

Mathematics as a Working Tool

This practice treats mathematics as a working tool embedded in software development rather than as a separate discipline. The goal is clarity, predictability, and informed judgment when designing and evolving computational systems.

Provider

Alex Stevovich

Alex Stevovich is an independent polymath guided by a self-directed perspective. His projects focus on original content and innovation developed through discovery-driven work grounded in first-principles thinking.

Studio Banners

Midnight Citylights

Midnight Citylights is my personal software development studio — the banner under which I work as a principal software engineer and independent developer. This is hands-on, first-principles work: designing, building, and maintaining systems directly, with a focus on clarity, durability, and long-term coherence.

I’ve produced proprietary, full-scale applications used by millions worldwide, alongside a substantial body of public software spanning multiple languages and domains. In parallel, I maintain and publish hundreds of packages and tools, many of which have become reliable building blocks for modern development workflows.

This work reflects a commitment to disciplined abstraction, clean system design, and engineering practices that hold up under real-world scale — not demos, not experiments, but software that ships, runs, and lasts.

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