REVIEW 8 cited by
The SCIP Optimization Suite 9.0
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The SCIP Optimization Suite provides a collection of software packages for mathematical optimization, centered around the constraint integer programming (CIP) framework SCIP. This report discusses the enhancements and extensions included in the SCIP Optimization Suite 9.0. The updates in SCIP 9.0 include improved symmetry handling, additions and improvements of nonlinear handlers and primal heuristics, a new cut generator and two new cut selection schemes, a new branching rule, a new LP interface, and several bug fixes. The SCIP Optimization Suite 9.0 also features new Rust and C++ interfaces for SCIP, new Python interface for SoPlex, along with enhancements to existing interfaces. The SCIP Optimization Suite 9.0 also includes new and improved features in the LP solver SoPlex, the presolving library PaPILO, the parallel framework UG, the decomposition framework GCG, and the SCIP extension SCIP-SDP. These additions and enhancements have resulted in an overall performance improvement of SCIP in terms of solving time, number of nodes in the branch-and-bound tree, as well as the reliability of the solver.
Forward citations
Cited by 8 Pith papers
-
Automating Idealness Proofs for Binary Programs with Application to Rectangle Packing
An automated framework certifies pairwise idealness of rectangle-packing MILP formulations up to numeric tolerances, disproving one existing formulation and introducing a new multilinear one.
-
New lower bounds for binary constant-weight codes: $A(23,6,10)\geq 2979$ and $A(24,6,10)\geq 4214$
New explicit codes prove A(23,6,10)≥2979 and A(24,6,10)≥4214, surpassing two 'lost' 1990 bounds, plus two further improved lower bounds.
-
Efficient Multi-round LLM Inference over Disaggregated Serving
AMPD adaptively routes incremental prefill tasks between prefill and decode workers and reorders queued prefill jobs to improve SLO attainment for multi-round LLM inference under prefill-decode disaggregation.
-
Equity by Design? On the Trade-Offs in Fairness-Driven Recommendation in Heterogeneous Two-Sided Markets
The 'free fairness' result for producer constraints vanishes for multi-item recommendations; a CVaR group-fairness objective and business constraints can be added with moderate trade-offs.
-
Exact algorithms for quadratic optimization over roots of unity
The SOS hierarchy for quadratic optimization over m-th roots of unity is exact at level floor(n/2)+1, and for even m a zonotope reformulation cuts the binary variables in half.
-
Hybrid Quantum-Classical Optimization Workflows for the Shipment Selection Problem
Iterative-QAOA warm-starts for the shipment selection problem yield hybrid logistics plans with up to 12% more shipments delivered on specific real instances while keeping operational cost flat.
-
Instance-Optimized String Fingerprints
A mixed-integer optimization model assigns characters to fingerprint bins per workload, reducing LIKE false positives and yielding up to 1.36x faster table scans in DuckDB.
-
MathOptAI.jl: Embed trained machine learning predictors into JuMP models
A Julia package that embeds trained ML predictors into JuMP optimization models, uniquely offering GPU-accelerated gray-box nonlinear formulations.
Discussion (0). Sign in to comment.