The paper introduces BOTH, a method that differentiates topology optimization (TO) itself to compute hypergradients for tuning hyperparameters alongside the primary design optimization. By evaluating only one or two TO steps, the approach provides sufficient information and scales to thousands of hyperparameters with a cost comparable to a few standard TO runs. Experiments on stress‑constrained and compliance problems, including a neural‑parameterized density field, demonstrate the effectiveness of this joint optimization strategy.
By Suryanarayanan Manoj Sanu, Miguel Anibal Bessa, Alejandro Marcos Arag\'on
The paper introduces PolyFormer, a physics-informed machine learning framework that learns compact polytopic representations of complex constraints. By transforming constraint-induced geometry into efficient polytopic reformulations, PolyFormer reduces optimization complexity and enables the use of standard solvers. Evaluations on large‑scale resource aggregation, network‑constrained optimization, and uncertainty‑aware optimization show up to 6,400‑fold speedups and 99.87% memory savings while keeping feasibility and objective errors low.
By Yilin Wen, Yi Guo, Bo Zhao, Wei Qi, Zechun Hu, Colin Jones, Jian Sun
arXiv:2607. 23448v1 Announce Type: cross Abstract: Expensive constrained optimization problems in real-world industry design often involve constraint thresholds that are difficult to determine in advance.
By Jin Wang, Xi Lin, Handing Wang
arXiv:2609.01493v1 Announce Type: cross
Abstract: Black-Box Optimization (BBO) has found broad applications, but evolutionary algorithms and Bayesian optimization face efficiency challenges as real-w...
By Chao Qian, Chen-Guang Wang, Rong-Xi Tan, Ke Xue
arXiv:2607. 16523v1 Announce Type: new Abstract: One of the main strengths of Constraint Programming is the ability to reduce the search space via propagation.
By Sascha Van Cauwelaert, Michele Lombardi, Pierre Schaus
arXiv:2605. 30456v2 Announce Type: replace Abstract: Many learning tasks in science and engineering are characterized by sparse datasets, which limits the effectiveness of purely data-driven approaches.
By Shraman Pal, Can Li
arXiv:2511. 02570v3 Announce Type: replace Abstract: Bayesian optimization (BO) is a widely used approach to hyperparameter optimization (HPO).
By Lukas Fehring, Marcel Wever, Maximilian Splieth\"over, Leona Hennig, Henning Wachsmuth, Marius Lindauer
The rapid deployment of machine learning systems across cloud, edge, and enterprise environments has brought model optimization to the forefront of systems-engineering. Despite a rich literature spanning quantization, pruning, knowledge distillation, parameter-efficient fine-tuning (PEFT), and inference-time optimization, practitioners are often left navigating these techniques through heuristics rather than principled methodology.
arXiv:2607. 14398v1 Announce Type: cross Abstract: Constrained generative models aim to produce samples that satisfy complex feasibility constraints while remaining faithful to the data distribution.
By Xiaoxuan Liang, Saeid Naderiparizi, Berend Zwartsenberg, Frank Wood
arXiv:2609.38434v1 Announce Type: cross
Abstract: Real-world optimization problems are difficult to model accurately because many objectives and constraints reside in domain experts' tacit knowledge,...
By Maxime Bouscary, Marco Molinaro, Sirui Li, Saurabh Amin, Ishai Menache, Konstantina Mellou
arXiv:2606. 03073v1 Announce Type: cross Abstract: Reinforcement learning (RL) for large language models (LLMs) is highly sensitive to hyperparameter configurations, making hyperparameter optimization (HPO) essential yet computationally expensive.
By Minping Chen, Bowen Xiao, Du Liang, Chuxuan Zeng, Zeyi Wen
arXiv:2607. 13735v1 Announce Type: new Abstract: The rapid deployment of machine learning systems across cloud, edge, and enterprise environments has brought model optimization to the forefront of systems-engineering.
By Dhruv Shivkant, Saket Mohanty, Utkarsh Wadhwa