arXiv Machine Learning
Scen-Opt: A Scenario Optimization Toolbox for Data-Driven Convex Programming
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arXiv AI
Jun 9
Scaling Decision-Focused Learning to Large Problems with Lagrangian Decomposition
arXiv:2606. 08797v1 Announce Type: cross Abstract: Decision-focused learning has shown great promise for addressing predict-then-optimize problems, particularly in the presence of under-specified models.
arXiv Machine Learning
Oct 5
Learn Feasibility Once, Optimize All Objectives: Derivative-Free Diffusion Models for Chance-Constrained Programming
The paper introduces D$^3$Opt, a derivative‑free diffusion‑based framework that separates constraint modeling from objective optimization in chance‑constrained programs. It learns a risk‑conditioned diffusion model once on constraint‑filtered decisions, then uses an annealed, particle‑based Feynman–Kac correction to optimize any post‑specified objective without retraining. The method is proven to preserve feasibility and provides an error bound, and experiments show effective optimization across smooth, non‑smooth, and non‑convex objectives, including objective transfer tasks.
arXiv AI
Oct 12
MPMMine: Better Benchmarks for Constraint Acquisition
MPMMine introduces a comprehensive benchmark suite for Constraint Acquisition (CA) in Mathematical Programming, addressing shortcomings of existing datasets such as inconsistent formats and limited problem classes. The suite offers standardized, extensible artifacts—including multiple model formulations, numerous parameter instantiations, and thousands of solutions and non‑solutions across discrete, continuous, and mixed domains—alongside natural‑language descriptions for text‑to‑model methods. Accompanying tools, the mpmmine‑py API and an interactive leaderboard, enable consistent evaluation of CA algorithms within isolated containers.
arXiv Statistics ML
Oct 26
Contextual Decision Making: Predictive or Generative
The paper compares two strategies for contextual decision making: predict‑then‑optimize (PtO), which learns the conditional expected objective, and generate‑then‑optimize (GtO), which learns conditional uncertainty and optimizes over generated scenarios. It analyzes how the choice of learning target influences statistical accuracy and optimization tractability, showing that smoothness can compensate for GtO’s higher dimensionality. The study also discusses tradeoffs in convexity preservation, scenario‑wise convexity, and model flexibility, supported by synthetic experiments and a real case study.
arXiv Machine Learning
Jun 9
OpenCompass: A Universal Evaluation Platform for Large Language Models
arXiv:2605. 19276v3 Announce Type: replace-cross Abstract: In recent years, the field of artificial intelligence has undergone a paradigm shift from task-specific small-scale models to general-purpose large language models (LLMs).
arXiv Machine Learning
Sep 15
Generation of Custom Solvers in Rust for Convex Optimization
The paper presents cvxgenrust, an open‑source tool that generates custom Rust code for solving families of parameterized convex optimization problems defined in CVXPY. It canonicalizes problem families, extracts affine maps to Clarabel cone‑program data, and produces a specialized Rust crate that updates parameters and calls Clarabel natively at runtime. The generated solver can also be exposed to Python and registered as a custom CVXPY solver, supporting a wide range of convex problems up to semidefinite and exponential‑cone programs, and demonstrates reduced runtime compared to direct CVXPY solves and performance comparable to CVXPYgen.