Latest Optimization Research Papers

The newest Optimization papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Optimization so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• Probing α-carboxysome biogenesis and modularity with bacterial microcompartment counterparts
  Ping Chang · Open MIND · Jan 1, 2029

  Bacterial microcompartments (BMCs) are protein-based organelles in prokaryotes that optimize metabolic pathways by confining specific enzymatic reactions within selectively permeable shells, playing critical roles in processes such as CO2 f…

• Performance Analysis of Solar-Powered Cooling Systems.
  Ahmed Abdellaoui · Zenodo (CERN European Organ... · Jul 9, 2028

  This technical report summarizes research conducted at the Solar Equipment Development Unit (UDES) regarding the integration of renewable energy in residential cooling systems. The study focuses on the experimental testing of solar thermal …

• Performance Analysis of Solar-Powered Cooling Systems.
  Ahmed Abdellaoui · Zenodo (CERN European Organ... · Jul 9, 2028

  This technical report summarizes research conducted at the Solar Equipment Development Unit (UDES) regarding the integration of renewable energy in residential cooling systems. The study focuses on the experimental testing of solar thermal …

• Towards dietary optimization for patients with Parkinson's disease : insights from a phase II, crossover, randomized controlled trial of two Mediterranean-ketogenic dietary interventions
  Kira Nicole Tosefsky · cIRcle (University of Briti... · Jan 1, 2028

  The full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires....

• Advanced optimization methods for interpretable machine learning models and their applications in chemical engineering
  Jiayang Ren · cIRcle (University of Briti... · Jan 1, 2028

  The full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires....

• Deep q-learning aided k-means clustering protocol for optimizing network lifetime in wireless sensor networks
  Flynn Dowey · Open Collections · Jan 1, 2028

  The full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires....

• Data and Code for 'Air quality and health impacts of Data Center electricity demand in the United States'
  Yuang Chen · Zenodo (CERN European Organ... · Jan 1, 2027

  This repository contains the data, model inputs, simulation outputs, and analysis scripts used to quantify the air quality and public health impacts attributable to U.S. data center electricity demand in 2023. The analysis combines electric…

• Optimization of 5′ UTR mRNA regions for enhanced protein expression in therapeutic cell types
  Madelaine Kate Robertson · Open Collections · Jan 1, 2027

  The full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires....

• MODELING AND CHARACTERIZATION OF ALGAL GROWTH IN ANAEROBIC DIGESTION WASTEWATER FOR PROCESS OPTIMIZATION
  S M Hasan Shahriar Rahat · Washington State University · Jan 1, 2027

  Microalgae cultivation using anaerobic digestion (AD) wastewater is an attractive way of producing nutrient-rich biomass and treating wastewater. Integration of computational modeling provides valuable insights into algal-bacterial interact…

• Data and Code for 'Air quality and health impacts of Data Center electricity demand in the United States'
  Yuang Chen · Zenodo (CERN European Organ... · Jan 1, 2027

  This repository contains the data, model inputs, simulation outputs, and analysis scripts used to quantify the air quality and public health impacts attributable to U.S. data center electricity demand in 2023. The analysis combines electric…

• Barzilai-Borwein Fails Superlinear Convergence on an Open Set of Quadratics for Every Dimension $n\geq 4$
  Dawei Li, Xiaotian Jiang, Mingyi Hong · arXiv · Jul 23, 2026

  Barzilai--Borwein (BB) method has shown strong practical performance in continuous optimization, yet its convergence dynamics remains poorly understood. In particular, a central unresolved question is whether BB converges superlinearly for …

• Token Budget Saturation and Mechanistic Early Detection of Reasoning Non-Convergence in Chain-of-Thought Models
  Renuka Oladri, Niveda Jawahar, Abdirisak Mohamed · arXiv · Jul 23, 2026

  Chain-of-thought reasoning models such as DeepSeek-R1-Distill-Qwen-7B exhibit a bimodal convergence pattern: generations either terminate within a token budget (converged) or exhaust it without reaching a conclusion (non-converged). We char…

• Lipschitzian SLLNs for random functions
  Lai Tian, Johannes O. Royset · arXiv · Jul 22, 2026

  We prove strong laws of large numbers for locally Lipschitz functions in the Lipschitz pseudometric. Our results hold under either a topological or a model-theoretic condition, with the latter encompassing functions jointly definable in o-m…

• Self-supervision drives representational convergence in medical foundation models more than clinical supervision
  Soroosh Tayebi Arasteh, Sebastian Ziegelmayer, Mahshad Lotfinia, Lisa Adams et al. · arXiv · Jul 22, 2026

  Medical image encoders from different groups are increasingly treated as interchangeable, on the assumption that scale and clinical supervision concentrate their representations onto a shared structure. Whether this convergence is real, wha…

• Dynamical and Optimization Trade-offs of Levi--Civita Coordinates for Learned Close-Encounter Dynamics
  Abhishek Shankar · arXiv · Jul 22, 2026

  Classical regularization removes the binary-collision singularity from the Kepler problem, but its value as a representation for learned Hamiltonian dynamics has not been systematically isolated. We compare Cartesian and planar Levi--Civita…

• 1-Lipschitz Neural Networks on Hadamard Manifolds
  Davide Murari, Marta Ghirardelli, Ben Adcock, Elena Celledoni et al. · arXiv · Jul 21, 2026

  Controlling the Lipschitz constant of a neural network is a standard way to promote robustness and stability. Most existing constraining strategies are designed for Euclidean spaces. In this work, we construct and analyze a class of 1-Lipsc…

• ISO: An RLVR-Native Optimization Stack
  Hanqing Zhu, Wenyan Cong, Zhizhou Sha, Sagnik Mukherjee et al. · arXiv · Jul 21, 2026

  Reinforcement learning with verifiable rewards (RLVR) is rapidly advancing the reasoning capabilities of language models, yet the optimization layer that converts reward feedback into weight-space updates remains poorly understood. Building…

• Contrastive-Collapsed Loss for Flexible and Geometrically Optimal Embeddings and Faster Convergence
  Blanca Cano-Camarero, Ángela Fernández-Pascual, José R. Dorronsoro · arXiv · Jul 14, 2026

  In this work, we introduce CoCo, a loss function aimed at learning normalized and well-structured representations. The proposed loss encourages intra-class collapse and inter-class contrast while preserving sufficient flexibility for neural…

• Paradoxes of Game Theoretic Equilibria and Price of Anarchy
  Georgios Piliouras, Ian Gemp, Siqi Liu, Luke Marris · arXiv · Jul 13, 2026

  For decades, static solution concepts (Nash, Correlated, and Coarse Correlated Equilibria) and the Price of Anarchy (PoA) have formed the bedrock of algorithmic game theory, with no-regret learning proving fast convergence to such game-theo…

• HiFi-LLP: High-Fidelity, Low-Cost Latency Predictors with Confidence for Robust HW-NAS
  Shambhavi Balamuthu Sampath, Behzad Shomali, Nael Fasfous, Moritz Thoma et al. · arXiv · Jul 13, 2026

  With deep neural networks (DNNs) increasingly deployed on edge devices, hardware (HW)-aware optimization techniques--such as HW-aware compression and HW-aware neural architecture search (HW-NAS)--have become essential. These methods rely on…

• Graph-Regularized Low-Rank Matrix Completion by Variable Projection
  Benoît Loucheur, P. -A. Absil, Michel Journée · arXiv · Jul 10, 2026

  We address the low-rank matrix completion problem by incorporating graph regularization into the existing Riemannian Trust-Region Matrix Completion (RTRMC) framework. The latter uses the geometry of the low-rank constraint to remodel the pr…

• Pose-to-Biomechanics: Bridging 3D Human Pose Estimation and Biomechanical Attribute Prediction
  Ayda Eghbalian, Kevin Desai · arXiv · Jul 9, 2026

  Recent progress in 3D human pose estimation has made markerless recovery of skeletal motion increasingly accurate and scalable. However, most pose estimators remain optimized for geometric keypoint accuracy, while many real-world applicatio…

• MPFlow: Learning Budgeted Max-Flow Optimization on the Lightning Network with Deep Graph Reinforcement Learning
  Harrison Rush, Vincent Davis, Simone Antonelli, Vikash Singh et al. · arXiv · Jul 9, 2026

  We address liquidity placement in the Bitcoin Lightning Network (LN): given a fixed budget, which channels should a node open to maximize its routing capacity? We cast this as a budget-constrained combinatorial optimization problem on graph…

• Neural Operator-enabled Topology-informed Evolutionary Strategy for PDE-Constrained Optimization
  Xiangming Huang, Guannan Zhang, Lu Lu, Raphaël Pestourie · arXiv · Jul 8, 2026

  The inverse design of physical systems governed by partial differential equations is computationally demanding due to the high dimensionality and non-convexity of design spaces. Generative models for inverse design often lack robustness and…

• PeTeR: Post-Training Robustification of Probabilistic Circuits
  Adrian Ciotinga, Yeming Dai, YooJung Choi · arXiv · Jul 8, 2026

  Probabilistic circuits (PCs) can model complex joint distributions while supporting exact and efficient computation of many inference queries. However, standard likelihood-based PC learning is vulnerable to overfitting and fragile generaliz…

• Higher-Order Geometric Updates for Levenberg-Marquardt Method via Riemann Normal Coordinates
  Jianing Liu, Dong H. Zhang · arXiv · Jul 8, 2026

  Nonlinear least-squares optimization is central to regression, physics-informed neural networks, and other machine-learning tasks. Such problems have a natural geometric interpretation, model predictions form a manifold in data space, while…

• Quantitative Gaussian-Process limits of Tensor Programs
  Andrea Agazzi, Eloy Mosig García, Dario Trevisan · arXiv · Jul 7, 2026

  We study the infinite-width Gaussian-process limit of random neural networks through the lens of tensor programs, and we provide a quantitative convergence theory in Wasserstein distance. Our main result gives explicit finite-width error bo…

• TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios
  Hong Lyu, Mingru Yang, Qianhua He, Yanxiong Li et al. · arXiv · Jul 7, 2026

  There are some datasets of varying scales for audio classification (AC) applied to different tasks. However, annotated data is limited for most scenarios, such as domestic environments. To address this challenge, we propose an $\textbf{A}$u…

• Beyond Adam: SOAP and Muon for Faster, Label-Efficient Training of Machine Learning Interatomic Potentials
  Gil Harari, Yoel Zimmermann, Ola Tangen Kulseng, Laura Zichi et al. · arXiv · Jul 2, 2026

  Machine learning interatomic potentials (MLIPs) have become a hallmark of AI for scientific simulation. While efforts on new architectures and datasets have led to increasingly accurate and general models, the choice of optimizer for traini…

• WattGPU: Predicting Inference Power and Latency on Unseen GPUs and LLMs
  Mauricio Fadel Argerich, Jonathan Fürst, Marta Patiño-Martínez · arXiv · Jul 2, 2026

  Large Language Model (LLM) inference workloads are a rapidly growing contributor to data center energy consumption. Optimizing these deployments requires matching specific LLMs to the most efficient GPUs, but operators currently lack the to…