Bibliographic record
Abstract
While data-driven approaches continue to find new applications across many domains, traditional mathematical optimization frameworks remain highly effective for solving realworld problems with well-defined constraints. Linear Programming (LP) is widely applied in industry and is a key component of various other mathematical problem-solving techniques. Although Algebraic Modeling Languages (AMLs) offer a higher level of abstraction for describing LPs, users must still manually specify each set of constraints. A recent work introduced an LP compiler that not only enables conversion of algorithms expressed in a more intuitive high-level programming language into LPs, but also guarantees polynomial-size LPs for P problems having exponential extension complexity. However, the LPs are extremely large in practice, posing challenges for existing LP solvers due to memory limitations, long solution and presolve times, numerical instability, and rounding errors. With the longterm goal of enabling systematic generation of Compact Integer Programming (CIP) formulations for exponential-size Integer Programs (IPs) having polynomial-time separation oracles, we propose a hierarchical linear pipelining technique, called Hierarchical Synchronization Barriers (HSB). HSB decomposes code into tree-like synchronized regions with statically known execution transitions, functions of compile-time parameters. This control-flow abstraction localizes LP constraints and variables to their relevant regions, significantly reducing LP size. We examine the effectiveness of HSB on the makespan problem, which has exponential extension complexity, and the weighted minimum spanning tree problem, both of which have exponentialsize natural LP formulations. Our results show up to a 25-fold reduction in LP size and substantial improvements in solver performance across both commercial and non-commercial LP solvers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".