25 papers · ranked by Valyu relevance
Sanpawat Kantabutra, Mehmet Cunkas
In many combinatorial optimization problems we want a particular set of k out of n items with some certain properties (or constraints). These properties may involve the k items. In the worst case a deterministic algorithm must scan n−k items in the set to verify the k items. If we pick a set of k items randomly and…
Raffaele Marino
This chapter delves into the realm of computational complexity, exploring the world of challenging combinatorial problems and their ties with statistical physics. Our exploration starts by delving deep into the foundations of combinatorial challenges, emphasizing their nature. We will traverse the class P, which…
Gita Naseri, Mattheos A. G. Koffas
In the first wave of synthetic biology, genetic elements, combined into simple circuits, are used to control individual cellular functions. In the second wave of synthetic biology, the simple circuits, combined into complex circuits, form systems-level functions. However, efforts to construct complex circuits are often…
Henrik Abgaryan, Tristan Cazenave, Ararat Harutyunyan
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, yet their direct application to NP-hard combinatorial problems (CPs) remains underexplored. In this work, we systematically investigate the reasoning abilities of LLMs on a variety of NP-hard combinatorial optimization tasks and introduce…
Dehua Xu, Dar-Li Yang, Ming Liu, Feng Chu + 1 more
Combinatorial optimization is one of the most active branches of operations research. The essence of a combinatorial optimization problem is to find optimal solutions or near optimal solutions from a finite set of feasible solutions. In such problems, the size of feasible solution space usually increases exponentially…
Otto I. Pulkkinen, Prson Gautam, Ville Mustonen, Tero Aittokallio
Combinatorial therapies are required to treat patients with advanced cancers that have become resistant to monotherapies through rewiring of redundant pathways. Due to a massive number of potential drug combinations, there is a need for systematic approaches to identify safe and effective combinations for each patient…
Michael Emmerich, André Deutz
and Case Studies Authors: ['Michael Emmerich' 'André Deutz'] | 1 | | Introduction | | | | | | 4 | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | | 1.1 | Viewing mulicriteria optimization as a task in system design and | | | | | | | | | | | | | analysis | . | | | | | 5 | | | | | | 1.2…
Naoya Onizawa, Takahiro Hanyu
This article critically investigates the limitations of the simulated annealing algorithm using probabilistic bits (pSA) in solving large-scale combinatorial optimization problems. The study begins with an in-depth analysis of the pSA process, focusing on the issues resulting from unexpected oscillations among p-bits.…
Claude Tadonki
The next frontier of high performance computing is the Exascale, and this will certainly stand as a noteworthy step in the quest for processing speed potential. In fact, we always get a fraction of the technically available computing power (so-called theoretical peak), and the gap is likely to go hand-to-hand with the…
Md. Rafiqul Islam, Md. Shahidul Islam, Pritam Khan Boni, Aldrin Saurov Sarker + 2 more
'Aldrin Saurov Sarker' 'Md. Asif Anam' 'Ali Safaa Sadiq'] The objective of the max-cut problem is to cut any graph in such a way that the total weight of the edges that are cut off is maximum in both subsets of vertices that are divided due to the cut of the edges. Although it is an elementary graph partitioning…
Menghao Tang, Zimin Liang, Miqing Li
Scalability of evolutionary algorithms refers to assessing how their performance changes as problem size increases. In the area of multi-objective optimisation, research on the scalability of multi-objective evolutionary algorithms (MOEAs) has predominantly focussed on continuous problems. However, multi-objective…
Wei Chen Esmonde Lim, G. Kanagaraj, S. G. Ponnambalam
Optimization of drill path can lead to significant reduction in machining time which directly improves productivity of manufacturing systems. In a batch production of a large number of items to be drilled such as printed circuit boards (PCB), the travel time of the drilling device is a significant portion of the…
Yaoxin Li, Jing Liu, Guozheng Lin, Yueyuan Hou + 2 more
'Jiang Zhang'] In computer science, there exist a large number of optimization problems defined on graphs, that is to find a best node state configuration or a network structure such that the designed objective function is optimized under some constraints. However, these problems are notorious for their hardness to…
Tao Hong, William R. Stauffer
Complex economic decisions are often combinatorial: they require individuals to select from many alternatives under strict constraints on time, resources, and energy. Combinatorial reasoning is the cognitive process that enables decision makers to construct and evaluate multiple potential solutions in the face of these…
Kirsten Van Huffel, Michiel Stock, Bernard De Baets
In combinatorial biotechnology, it is crucial for screening experiments to sufficiently cover the design space. In the BioCCP.jl package, we provide functions for minimum sample size determination based on the mathematical framework coined the Coupon Collector Problem. BioCCP.jl, including source code, documentation…
Iman Farasat, Manish Kushwaha, Jason Collens, Michael Easterbrook + 2 more
Engineering multi-protein genetic systems to maximize their performance remains a combinatorial challenge, particularly when measurement throughput is limited. We have developed a computational design and modeling approach to build predictive models and identify optimal expression levels, while circumventing…
Yifan Wu, Aron Walsh, Alex Ganose
What is the minimum number of experiments, or calculations, required to find an optimal solution? Relevant chemical problems range from identifying a compound with target functionality within a given phase space to controlling materials synthesis and device fabrication conditions. A common feature in this application…
Lucian Chan, Geoffrey Hutchison, Garrett Morris
Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here…
Authors not listed
Experimental design plays an important role in efficiently acquiring informative data for system characterization and deriving robust conclusions under resource limitations. Recent advancements in high-throughput experimentation coupled with machine learning have notably improved experimental procedures. While Bayesian…
Benjamin M. David, Ryan M. Wyllie, Ramdane Harouaka, Paul A. Jensen
The goal of oligonucleotide (oligo) design is to select oligos that optimize a set of design criteria. Oligo design problems are combinatorial in nature and require computationally intensive models to evaluate design criteria. Even relatively small problems can be intractable for brute-force approaches that test every…
Pablo Quijano Velasco, Kedar Hippalgaonkar, Balamurugan Ramalingam
The discovery of optimal conditions of chemical reactions is a labor-intensive, time-consuming task that requires exploring a high-dimensional parametric space. Historically the optimization of chemical reactions has been performed by manual experimentation guided by human intuition and Design of Experiments where one…
Riley Hickman, Priyansh Parakh, Austin Cheng, Qianxiang Ai + 3 more
Experiment planning algorithms are a required component of autonomous platforms for scientific discovery. Selecting a suitable optimization algorithm for a novel application is an important yet difficult choice a researcher has to make based on past empirical performance on similar tasks. To facilitate the evaluation…
Andre KY Low, Flore Mekki-Berrada, Aleksandr Ostudin, Jiaxun Xie + 7 more
The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are increasingly paired with intelligent experimental design. When solving optimization problems, Bayesian-based optimizers are often…
Changin Oh, Kathleen P. Wilkie
We present the Toroidal Search Algorithm (TSA), a novel population-based metaheuristic optimization method inspired by the topology of a torus. Conventional metaheuristics frequently suffer from boundary stagnation, a phenomenon that severely degrades performance in bounded and high-dimensional search spaces. TSA…
Riley Hickman, Matteo Aldeghi, Alán Aspuru-Guzik
Model-based optimization strategies, such as Bayesian optimization (BO), have been deployed across the natural sciences in design and discovery campaigns due to their sample efficiency and flexibility. The combination of such strategies with automated laboratory equipment and/or high-performance computing in a…