A code-graph and correction-based LLM search framework outperforms full-algorithm generation at equal token budgets on three combinatorial optimization problems.
Optimind: Teaching llms to think like optimization experts
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Graph-grounded optimization sources problem elements from knowledge graphs and shows Rao-family metaheuristics plus OR-tools perform differently across seven real-world KG-backed problems while surfacing data issues.
Regression models fit observed LP solver runtimes well within instance classes, but asymptotic growth rates differ substantially across simplex, interior-point, and PDHG methods.
citing papers explorer
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Budget-Efficient Automatic Algorithm Design via Code Graph
A code-graph and correction-based LLM search framework outperforms full-algorithm generation at equal token budgets on three combinatorial optimization problems.
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Graph-Grounded Optimization: Rao-Family Metaheuristics, Classical OR, and SLM-Driven Formulation over Knowledge Graphs
Graph-grounded optimization sources problem elements from knowledge graphs and shows Rao-family metaheuristics plus OR-tools perform differently across seven real-world KG-backed problems while surfacing data issues.
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Empirical Asymptotic Runtime Analysis of Linear Programming Algorithms
Regression models fit observed LP solver runtimes well within instance classes, but asymptotic growth rates differ substantially across simplex, interior-point, and PDHG methods.