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The Quantum Approximate Optimization Algorithm (QAOA) is a well-known hybrid quantum-classical algorithm for combinatorial optimization problems. Improving QAOA involves enhancing its approximation ratio while addressing practical…

Quantum Physics · Physics 2025-02-07 Yukun Wang , ZeYang Li , Linchun Wan

In this paper we introduce DISROPT, a Python package for distributed optimization over networks. We focus on cooperative set-ups in which an optimization problem must be solved by peer-to-peer processors (without central coordinators) that…

Optimization and Control · Mathematics 2021-04-21 Francesco Farina , Andrea Camisa , Andrea Testa , Ivano Notarnicola , Giuseppe Notarstefano

We study the distributed optimization of transmit strategies in a multiple-input, single-output (MISO) interference channel (IFC). Existing distributed algorithms rely on stricly synchronized update steps by the individual users. They…

Information Theory · Computer Science 2015-06-19 Stefan Wesemann , Gerhard Fettweis

With dynamic pricing on the rise, firms are using sophisticated algorithms for price determination. These algorithms are often non-interpretable and there has been a recent interest in their seemingly emergent ability to tacitly collude…

Computer Science and Game Theory · Computer Science 2024-10-25 Pravesh Koirala , Forrest Laine

Particle Swarm Optimization (PSO) is a stochastic technique for solving the optimization problem. Attempts have been made to shorten the computation times of PSO based algorithms with massive threads on GPUs (graphic processing units),…

Distributed, Parallel, and Cluster Computing · Computer Science 2023-12-05 Chuan-Chi Wang , Chun-Yen Ho , Chia-Heng Tu , Shih-Hao Hung

Finding the optimal solution is often the primary goal in combinatorial optimization (CO). However, real-world applications frequently require diverse solutions rather than a single optimum, particularly in two key scenarios. The first…

Machine Learning · Statistics 2025-08-15 Yuma Ichikawa , Hiroaki Iwashita

Policy optimization methods are popular reinforcement learning algorithms, because their incremental and on-policy nature makes them more stable than the value-based counterparts. However, the same properties also make them slow to converge…

Machine Learning · Computer Science 2021-07-01 Andrea Zanette , Ching-An Cheng , Alekh Agarwal

Positive linear programs (LP), also known as packing and covering linear programs, are an important class of problems that bridges computer science, operations research, and optimization. Despite the consistent efforts on this problem, all…

Data Structures and Algorithms · Computer Science 2016-11-15 Zeyuan Allen-Zhu , Lorenzo Orecchia

Within the domain of large language models, reinforcement fine-tuning algorithms necessitate the generation of a complete reasoning trajectory beginning from the input query, which incurs significant computational overhead during the…

Direct Preference Optimization (DPO) is a method for enhancing model performance by directly optimizing for the preferences or rankings of outcomes, instead of traditional loss functions. This approach has proven effective in aligning Large…

Machine Learning · Computer Science 2024-09-18 Ruoyu Wang , Jiachen Sun , Shaowei Hua , Quan Fang

Proximal Policy Optimization (PPO) is among the most widely used algorithms in reinforcement learning, which achieves state-of-the-art performance in many challenging problems. The keys to its success are the reliable policy updates through…

Machine Learning · Computer Science 2021-07-02 Mónika Farsang , Luca Szegletes

We study non-convex distributed optimization problems where a set of agents collaboratively solve a separable optimization problem that is distributed over a time-varying network. The existing methods to solve these problems rely on (at…

Optimization and Control · Mathematics 2022-04-26 Hadi Reisizadeh , Behrouz Touri , Soheil Mohajer

Direct Preference Optimization (DPO) is a simple and efficient framework that has attracted substantial attention. However, it often struggles to meet its primary objectives -- increasing the generation probability of chosen responses while…

Artificial Intelligence · Computer Science 2025-06-17 Jay Hyeon Cho , JunHyeok Oh , Myunsoo Kim , Byung-Jun Lee

The efficient scheduling of independent computational tasks in a heterogeneous computing environment is an important problem that occurs in domains such as Grid and Cloud computing. Finding optimal schedules is an NP-hard problem in…

Distributed, Parallel, and Cluster Computing · Computer Science 2013-12-24 John Levine , Graeme Ritchie , Alastair Andrew , Simon Gates

Solving convex Semi-Infinite Programming (SIP) problems is challenging when the separation problem, i.e., the problem of finding the most violated constraint, is computationally hard. We propose to tackle this difficulty by solving the…

Optimization and Control · Mathematics 2025-06-11 Antoine Oustry , Martina Cerulli

Particle swarm optimization (PSO) is a widely used nature-inspired meta-heuristic for solving continuous optimization problems. However, when running the PSO algorithm, one encounters the phenomenon of so-called stagnation, that means in…

Neural and Evolutionary Computing · Computer Science 2013-08-09 Manuel Schmitt , Rolf Wanka

This paper proposes a control algorithm for stable implementation of asynchronous parallel quadratic programming (PQP) through dual decomposition technique. In general, distributed and parallel optimization requires synchronization of data…

Systems and Control · Electrical Eng. & Systems 2019-11-26 Kooktae Lee

We introduce KAPSO, a modular framework for autonomous program synthesis and optimization. Given a natural language goal and an evaluation method, KAPSO iteratively performs ideation, code synthesis and editing, execution, evaluation, and…

Artificial Intelligence · Computer Science 2026-02-03 Alireza Nadafian , Alireza Mohammadshahi , Majid Yazdani

Direct Preference Optimization (DPO) has emerged as a predominant alignment method for diffusion models, facilitating off-policy training without explicit reward modeling. However, its reliance on large-scale, high-quality human preference…

Computer Vision and Pattern Recognition · Computer Science 2026-02-09 Khiem Pham , Quang Nguyen , Tung Nguyen , Jingsen Zhu , Michele Santacatterina , Dimitris Metaxas , Ramin Zabih

Surrogate Optimization (SO) algorithms have shown promise for optimizing expensive black-box functions. However, their performance is heavily influenced by hyperparameters related to sampling and surrogate fitting, which poses a challenge…

Machine Learning · Computer Science 2023-10-13 Nazanin Nezami , Hadis Anahideh
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