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

• Optimizing In Vitro Maturation in Goats: A Comprehensive Evaluation of Follicular and Culture Factors
  Raashid Lateef Dar · mVedra · Jun 30, 2032
• Probing α-carboxysome biogenesis and modularity with bacterial microcompartment counterparts
  Ping Chang · University of Liverpool · 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 …

• 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....

• Optimization and Uncertainty in Learning: Optimizer, Regularization, and Randomized-Search Ablations on a Covertype PyTorch Backbone
  Makan Sabeti · Zenodo (CERN European Organ... · Jul 27, 2027
• Optimization and Uncertainty in Learning: Optimizer, Regularization, and Randomized-Search Ablations on a Covertype PyTorch Backbone
  Makan Sabeti · Zenodo (CERN European Organ... · Jul 27, 2027
• 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…

• 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…

• HydroMg attenuates doxorubicin-induced myocardial injury via promoting mitochondrial function.
  Lu Zhang, Yawei Jin, Yuning Zhang, Mohan Li et al. · PubMed · Jan 1, 2027

  In contrast to molecular hydrogen's intrinsic limitations, HydroMg's structural optimization shortens gastrointestinal transit, reducing hydrogen dissipation while enhancing cellular bioavailability. However, it remains unclear whether it a…

• 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…

• HYBRİD UTİLİZATİON OF WASTE POLYMERS AND GRAPHENE DERİVATİVES İN SUSTAİNABLE CEMENTİTİOUS COMPOSİTES: MULTİ-RESPONSE OPTİMİZATİON AND MİCROSTRUCTURAL CHARACTERİZATİON
  Mücahit Uğur · Zenodo (CERN European Organ... · Jan 1, 2027

  Supplemental Material for: Hybrid Utilization of Waste Polymers and Graphene Derivatives in Sustainable Cementitious Composites: Multi-Response Optimization and Microstructural Characterization This repository contains the supplementary mat…

• Optimization of 5′ UTR mRNA regions for enhanced protein expression in therapeutic cell types
  Madelaine K. 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....

• Gene Selection for Breast Cancer Classification Using T-Test Filtering and Wrapper-Based Optimization
  Abdelhamid Elwaer, Abdeladeem Dreder · Libyan Journal of Informatics · Dec 31, 2026

  يواجه التصنيف الدقيق لسرطان الثدي باستخدام بيانات التعبير الجيني تحديات ناجمة عن الأبعاد العالية والتشويش في مجموعات بيانات المصفوفات الدقيقة. تقترح هذه الدراسة وتقيّم مساراً هجيناً لاختيار الميزات يدمج أسلوب الترشيح باستخدام "اختبار تي" T-…

• Adaptive Scenario Generation for KL-DRO Around a Learned Density Model
  Aron Brenner, Saurabh Amin · arXiv · Oct 29, 2026

  Distributionally robust optimization (DRO) hedges data-driven two-stage stochastic programs against error in the distribution estimated from a finite record, and standard ambiguity sets differ in how they treat realizations absent from that…

• Homebrewed SLMs: Autoregressive and Masked Diffusion Language Models Built at Home for Portuguese
  Pedro Carvalho Brom · arXiv · Oct 29, 2026

  Building a language model of one's own no longer requires a data centre. This tutorial trains one from scratch, tokenizer included, on one consumer GPU, and shows how to tell whether it is good for the tasks of a specialist domain. It propo…

• The Precision Wall: An Optimizer-Dependent Boundary for Optimization under Power-Law Measurement Spectra
  Lngel Kyo · arXiv · Oct 29, 2026

  In low-rank matrix sensing of a rank-$D$ factor $U^\ast \in \mathbb{R}^{V \times D}$, replacing isotropic Gaussian designs with power-law measurement covariance $Σ= \mathrm{diag}(j^{-s})_{j=1}^V$ leaves the Burer--Monteiro landscape free of…

• MuonGS: Pretraining LLMs with Relaxed Weight-Norm Control
  Athanasios Glentis, Dawei Li, Mingyi Hong · arXiv · Oct 27, 2026

  Weight decay and explicit weight-norm constraints improve optimizer performance in language model pretraining, yet a unified understanding of why such control helps and how much is needed remains incomplete. We analyze norm control in Muon …

• Work Agents: Systems, Capabilities, and Optimization
  Zishan Xu, Enjun Du, Mingqi Gao, Cheng Yang et al. · arXiv · Oct 26, 2026

  As foundation models evolve from information generation to digital work execution, agents are increasingly capable of completing complex tasks across tools, applications, and workflows. However, existing research lacks a unified perspective…

• Selective Tie Learning: Mitigating Spurious Correlations in Preference Optimization
  Christian Moya, Purav Matlia, Elliott Thornley, Guang Lin · arXiv · Oct 26, 2026

  Preference optimization can learn spurious correlations, and augmenting the preference data with ties (equally preferred responses) can reduce spurious reliance. However, ties differ in their corrective effect, making their selection for au…

• Model Selection for Off-Policy Evaluation via Pairwise Bellman Validation
  Lars van der Laan, Vasilis Syrgkanis, Nathan Kallus · arXiv · Oct 26, 2026

  Model selection and hyperparameter tuning pose a central challenge in off-policy evaluation (OPE) because errors of fitted Q-functions and occupancy-ratio estimates cannot be directly evaluated from held-out transitions. Bellman-residual an…

• SCAFFOLD under Hessian Similarity: Sharp Convergence Rates
  Zhirayr Tovmasyan, Slavomír Hanzely, Egor Shulgin, Peter Richtárik et al. · arXiv · Oct 26, 2026

  One of the main challenges of federated learning is heterogeneous data split among the clients, leading to biased local updates called client drift. The SCAFFOLD algorithm (Karimireddy et al., 2020) corrects client drift with control variat…

• A Tutorial on Physics Constrained Machine Learning for Hybrid Modeling, Optimization, and Control
  Rahul Golder, Bimol Nath Roy, MM Faruque Hasan · arXiv · Oct 23, 2026

  Physics-constrained machine learning (PCML) integrates domain knowledge, first-principles, and physical constraints into AI/ML models. In this work, we present a step-by-step tutorial of a modular PCML framework, KKT-HardNet, and illustrate…

• PoKER: A Probability of Kill Estimation Rate Model for Air-to-Air Missiles Using Machine Learning on Stochastic Targets
  Joao P. A. Dantas, Andre N. Costa, Diego Geraldo, Marcos R. O. A. Maximo et al. · arXiv · Oct 23, 2026

  This work introduces PoKER, a novel probabilistic model engineered to optimize missile launch effectiveness in air-to-air scenarios, specifically within Beyond Visual Range (BVR) air combat. Unlike conventional Weapon Engagement Zone (WEZ) …

• Multiplicative vs. Additive State Coupling in Learned Optimizer Schedules
  M. Harper Langston, Pierre-David Letourneau, Dalton Jones, Roland Memisevic et al. · NeurIPS · Oct 23, 2026

  We learn schedules for a continuous penalty relaxation of MAX-CUT by backpropagating through an unrolled classical solver. A state-blind schedule generalizes to instances substantially larger than those used for training. A state-adaptive b…

• CARES: Coverage-Aware Replay Exemplar Selection for Continual Learning
  Arundhati S. Shanbhag, Brian B. Moser, Federico Raue, Tobias C. Nauen et al. · arXiv · Oct 23, 2026

  Replay-based Continual Learning mitigates catastrophic forgetting by keeping a buffer of past samples, making buffer composition critical under a fixed memory budget. Recent coreset-based replay methods improve buffer quality using model-de…

• Questioning Instrumental Convergence in RLVR: Checkpoint Evidence from OLMo-3
  Barış Alparslan, Sandy Tanwisuth, Caleb Petterson · arXiv · Oct 22, 2026

  Instrumental convergence is often invoked as a general reason to expect capable agents to acquire or express power-seeking subgoals: self-preservation, resource acquisition, deception, or resistance to oversight which poses an important thr…

• Diffusion Language Models for Perplexity-Aware Black-Box Attacks
  Ali Al-Lawati, Elsayed Eshra, Dongwon Lee, Suhang Wang · arXiv · Oct 22, 2026

  \emph{Continuous-space} attacks find prompts that trigger malicious behaviors, such as overthinking, in black-box LLMs by sampling or optimizing over a low-dimensional Gaussian latent space and decoding it to text through a \emph{random map…

• Fine-Grained Analysis of Catastrophic Forgetting in Continual Learning via Algorithmic Stability
  Guodong Zheng, Yan Sun, Peng Wang, Enneng Yang et al. · arXiv · Oct 22, 2026

  Understanding catastrophic forgetting, in which learning new tasks degrades performance on previously learned tasks, remains a fundamental challenge in continual learning (CL). Existing theoretical analyses often rely on specific model or t…

• PYTHIA, a transferable machine learning framework for simultaneous defect and adsorption site optimization
  Ioannis Kouroudis, Zhaosu Gu, Poonam, Leon Muller et al. · arXiv · Oct 22, 2026

  Machine learning has permeated material optimization, providing high value. Nevertheless, its re- liance on a high number of samples limits its applicability to relatively explored material families. In this paper we extend the applicabilit…

• CORD: Conflict-Aware Optimization for Joint RL and Distillation in Agents
  Wenhao Yu, Zhengcheng Wang, Jiahong Liu, Zexuan Qiu et al. · arXiv · Oct 21, 2026

  Joint reinforcement learning and self-distillation combines task rewards with dense teacher guidance for agent training, but their gradients can partly cancel. A mixing coefficient controls relative scale without selectively removing opposi…