A Novel Second-Order Cone Programming Algorithm

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Michelle Wei · San Jose, California
Regeneron Young Scientist Award ($50,000) · Regeneron International Science and Engineering Fair · 2024

Abstract

A faster algorithm for second-order cone programming, a convex optimisation form that sits underneath applications in machine learning, transportation and financial systems.

Why it worked

ResearchForge's reading of the public record — not the students' words, and not the judges' reasoning.

Optimising a solver rather than an application means one result improves everything built on top of it. The breadth of downstream use is the impact argument, and it costs nothing to make because it is already true.

Key methods

Second-order cone programming
Algorithm design and complexity analysis
Convex optimisation
Runtime benchmarking

What to take from this

ResearchForge's reading of the public record — not the students' words, and not the judges' reasoning. Borrow the habits and decisions, never the project itself.

Improving a widely used primitive multiplies your impact across every application that calls it
Algorithms projects need a baseline and a benchmark set — the speedup claim lives or dies on what it is measured against

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Compiled by ResearchForge from the Society for Science public award announcement linked on this record. Names, hometowns, project titles and reported results are as published. The "why it worked" and "what to take from this" notes are ResearchForge's editorial reading of that public record — they are not statements by the students and not the judges' rationale. All rights to the original projects remain with their authors; no project materials are rehosted here.