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This paper proposes Drain-Vortex Optimization (DVO), a population-based metaheuristic for continuous optimization. DVO models each candidate solution as a particle moving in a multi-drain vortex field. Its update rule decomposes motion into…

Neural and Evolutionary Computing · Computer Science 2026-05-28 Mohsen Omidi , Brian Vaughan

Among on-policy reinforcement learning algorithms, Proximal Policy Optimization (PPO) demonstrates is widely favored for its simplicity, numerical stability, and strong empirical performance. Standard PPO relies on surrogate objectives…

Machine Learning · Computer Science 2026-02-03 Shunpeng Yang , Ben Liu , Hua Chen

This paper presents an evolutionary algorithm with a new goal-sequence domination scheme for better decision support in multi-objective optimization. The approach allows the inclusion of advanced hard/soft priority and constraint…

Artificial Intelligence · Computer Science 2011-06-02 E. F. Khor , T. H. Lee , R. Sathikannan , K. C. Tan

Generative models excel at synthesizing high-fidelity samples from complex data distributions, but they often violate hard constraints arising from physical laws or task specifications. A common remedy is to project intermediate samples…

Machine Learning · Computer Science 2025-09-30 Jinhao Liang , Yixuan Sun , Anirban Samaddar , Sandeep Madireddy , Ferdinando Fioretto

This paper proposes a novel Unified Feature Optimization (UFO) paradigm for training and deploying deep models under real-world and large-scale scenarios, which requires a collection of multiple AI functions. UFO aims to benefit each single…

Computer Vision and Pattern Recognition · Computer Science 2022-07-22 Teng Xi , Yifan Sun , Deli Yu , Bi Li , Nan Peng , Gang Zhang , Xinyu Zhang , Zhigang Wang , Jinwen Chen , Jian Wang , Lufei Liu , Haocheng Feng , Junyu Han , Jingtuo Liu , Errui Ding , Jingdong Wang

Many machine learning algorithms minimize a regularized risk, and stochastic optimization is widely used for this task. When working with massive data, it is desirable to perform stochastic optimization in parallel. Unfortunately, many…

Machine Learning · Statistics 2023-11-27 Shin Matsushima , Hyokun Yun , Xinhua Zhang , S. V. N. Vishwanathan

The performance of cluster computing depends on how concurrent jobs share multiple data center resource types like CPU, RAM and disk storage. Recent research has discussed efficiency and fairness requirements and identified a number of…

Performance · Computer Science 2014-04-10 Thomas Bonald , James Roberts

Bayesian optimization (BO) is a widely-used sequential method for zeroth-order optimization of complex and expensive-to-compute black-box functions. The existing BO methods assume that the function evaluation (feedback) is available to the…

Machine Learning · Computer Science 2022-06-22 Arun Verma , Zhongxiang Dai , Bryan Kian Hsiang Low

We derive a stochastic gradient algorithm for semidefinite optimization using randomization techniques. The algorithm uses subsampling to reduce the computational cost of each iteration and the subsampling ratio explicitly controls…

Optimization and Control · Mathematics 2011-08-30 Alexandre d'Aspremont

Several fundamental problems in science and engineering consist of global optimization tasks involving unknown high-dimensional (black-box) functions that map a set of controllable variables to the outcomes of an expensive experiment.…

Machine Learning · Computer Science 2023-09-15 Mohamed Aziz Bhouri , Michael Joly , Robert Yu , Soumalya Sarkar , Paris Perdikaris

Completely positive factorization (CPF) is a critical task with applications in many fields. This paper proposes a novel method for the CPF. Based on the idea of exterior point iteration, an optimization model is given, which aims to…

Optimization and Control · Mathematics 2021-02-17 Zhenyue Zhang , Bingjie Li

First Order Bayesian Optimization (FOBO) is a sample efficient sequential approach to find the global maxima of an expensive-to-evaluate black-box objective function by suitably querying for the function and its gradient evaluations. Such…

Machine Learning · Computer Science 2023-06-21 Utkarsh Prakash , Aryan Chollera , Kushagra Khatwani , Prabuchandran K. J. , Tejas Bodas

Bayesian Optimization (BO) is an effective method for optimizing expensive-to-evaluate black-box functions with a wide range of applications for example in robotics, system design and parameter optimization. However, scaling BO to problems…

Systems and Control · Electrical Eng. & Systems 2020-01-22 Lukas P. Fröhlich , Edgar D. Klenske , Christian G. Daniel , Melanie N. Zeilinger

Deep Learning is considered to be a quite young in the area of machine learning research, found its effectiveness in dealing complex yet high dimensional dataset that includes but limited to images, text and speech etc. with multiple levels…

Computer Vision and Pattern Recognition · Computer Science 2016-10-19 Mrutyunjaya Panda

We study reinforcement learning in hybrid discrete-continuous action spaces, such as settings where the discrete component selects a regime (or index) and the continuous component optimizes within it -- a structure common in robotics,…

Machine Learning · Computer Science 2026-05-15 Matias Alvo , Daniel Russo , Yash Kanoria

In Bayesian optimization (BO) for expensive black-box optimization tasks, acquisition function (AF) guides sequential sampling and plays a pivotal role for efficient convergence to better optima. Prevailing AFs usually rely on artificial…

Machine Learning · Computer Science 2022-10-04 Zijing Liu , Xiyao Qu , Xuejun Liu , Hongqiang Lyu

We introduce a statistical physics inspired supervised machine learning algorithm for classification and regression problems. The method is based on the invariances or stability of predicted results when known data is represented as…

Machine Learning · Statistics 2018-11-19 Patrick Chao , Tahereh Mazaheri , Bo Sun , Nicholas B. Weingartner , Zohar Nussinov

In constrained Markov decision processes, enforcing constraints during training is often thought of as decreasing the final return. Recently, it was shown that constraints can be incorporated directly into the policy geometry, yielding an…

Machine Learning · Computer Science 2025-08-18 Nikola Milosevic , Johannes Müller , Nico Scherf

Reading order detection is the foundation of document understanding. Most existing methods rely on uniform supervision, implicitly assuming a constant difficulty distribution across layout regions. In this work, we challenge this assumption…

Computer Vision and Pattern Recognition · Computer Science 2026-01-13 Fuyuan Liu , Dianyu Yu , He Ren , Nayu Liu , Xiaomian Kang , Delai Qiu , Fa Zhang , Genpeng Zhen , Shengping Liu , Jiaen Liang , Wei Huang , Yining Wang , Junnan Zhu

Preference optimization (PO) is indispensable for large language models (LLMs), with methods such as direct preference optimization (DPO) and proximal policy optimization (PPO) achieving great success. A common belief is that DPO is…

Machine Learning · Computer Science 2026-05-18 Yue Wang , Qizhou Wang , Zizhuo Zhang , Gang Niu , Bo Han , Masashi Sugiyama