26 papers · ranked by Valyu relevance
Jing He, Qi-wei Kong, Ho-Chung Lui, Haitao Liu + 1 more
The definition of factor space and a unified optimization based classification model were developed for linear programming and supervised learning. Intelligent behaviour appeared in a decision process can be treated as a moving point y, the dynamic state observed and controlled by the agent, moving in a factor space…
Xijun Li, Qingyu Qu, Fangzhou Zhu, Jia Zeng + 3 more
'Kun Mao' 'Jie Wang'] It has been verified that the linear programming (LP) is able to formulate many real-life optimization problems, which can obtain the optimum by resorting to corresponding solvers such as OptVerse, Gurobi and CPLEX. In the past decades, a serial of traditional operation research algorithms have…
Radoslaw Ryńca, Yasmin Ziaeian, Claudia Noemi González Brambila
In the past few decades, any type of organization, from factories to government organizations, the banking sector, or educational institutions concentrates on increasing profit margins. To achieve this, one of the key factors is to achieve maximum output with minimum resources (input). Therefore, having an optimal plan…
Catalina J. Villalba, Aurélio Ribeiro Leite de Oliveira
columns Authors: ['Catalina J. Villalba' 'Aurélio Ribeiro Leite de Oliveira'] The Interior-Point Methods are a class for solving linear programming problems that rely upon the solution of linear systems. At each iteration, it becomes important to determine how to solve these linear systems when the constraint matrix of…
Raj Bridgelall
This tutorial is an andragogical guide for students and practitioners seeking to understand the fundamentals and practice of linear programming. The exercises demonstrate how to solve classical optimization problems with an emphasis on spatial analysis in supply chain management and transport logistics. All exercises…
Somdeb Lahiri
In this paper we assemble some results about the upper-semicontinuity and lowersemicontinuity of the feasible correspondence and the solution correspondence of linear programming problems allowing variability of all parameters of such problems. We also prove continuity properties of optimal value functions, once again…
Olesya Melnichenko, Venkat S. Malladi
In the field of genomics, bioinformatics pipelines play a crucial role in processing and analyzing vast biological datasets. These pipelines, consisting of interconnected tasks, can be optimized for efficiency and scalability by leveraging cloud platforms such as Microsoft Azure. The choice of compute resources…
Mengzhen Guo, Stefan Grünewald
We present Lpnet, a variant of the widely used Neighbor-net method that approximates pairwise distances between taxa by a circular phylogenetic network. We first apply standard methods to construct a binary phylogenetic tree and then use integer linear programming to compute an optimal circular orderings that agrees…
Ahmad Abdi, Gérard Cornuéjols, Bertrand Guenin, Levent Tunçel
A rational number is dyadic if it has a finite binary representation $p/2^k$, where p is an integer and k is a nonnegative integer. Dyadic rationals are important for numerical computations because they have an exact representation in floating-point arithmetic on a computer. A vector is dyadic if all its entries are…
Jaan Übi, Evald Übi
In order to find a non-negative solution to a system of inequalities, the corresponding dual problem is composed, which has a suitable unity basic matrix. In such a formulation, the objective function is replaced by set of constraints based on that function. Additional constraints can be used for accelerating…
K. H. Benjamin Leung, Nasrin Yousefi, Timothy C. Y. Chan, Ahmed M. Bayoumi
Putting the 4 components together, a general optimization model can be formulated as follows: maximize f ( x 1 , … , x n ; α 1 , … , α k ) subject to g i ( x 1 , … , x n ; α 1 , … , α k ) ≥ 0 , i = 1 , … , m This optimization model aims to maximize an objective function $f$ with $n$ decision variables $x_{1},…,x_{n}$…
Kirill Sechkar, Zoltan A. Tuza, Guy-Bart Stan
Laboratory automation and mathematical optimisation are key to improving the efficiency of synthetic biology research. While there are algorithms optimising the construct designs and synthesis strategies for DNA assembly, the optimisation of how DNA assembly reaction mixes are prepared remains largely unexplored. Here…
Ali Kadhim Yaqoob, Mohamed O. Saeed, Ghufran Khalil Joad, Oliyath Ali
systems Authors: ['Ali Kadhim Yaqoob' 'Mohamed O. Saeed' 'Ghufran Khalil Joad' 'Oliyath Ali'] Increasing the complexity of solving budgetary allocation (NP-hardness problem) has led a wide range of methods to minimize the costs. Metaheuristics and Linear Programming (LP) are the most optimisation in this fields.…
Robert Wakhata, Sudi Balimuttajjo, Védaste Mutarutinya
The study explored the direct and indirect relationship between students’ attitude towards, and performance in mathematics word problems (MWTs), mediated by the active learning heuristic problem solving (ALHPS) approach. Specifically, this study investigated the correlation between students’ performance and their…
Mustafa Ozen, Ali Abdi, Effat S. Emamian
Analysis of intracellular molecular networks has many applications in understanding of the molecular bases of some complex diseases and finding the effective therapeutic targets for drug development. To perform such analyses, the molecular networks need to be converted into computational models. In general, network…
Qianxiang Ai, Joshua Schrier
In a recent paper in this journal (Chem. Mater. 2022, 34, 2545-2552), Twyman et al. studied the environmental stability of crystals by introducing a greedy heuristic algorithm for determining possible oxidation reactions. We show how the problem can be solved exactly, with less code and comparable computational time by…
Authors not listed
Automated chemistry platforms hold the potential to enable large-scale organic synthesis campaigns, such as producing a library of compounds for biological evaluation. The efficiency of such platforms will depend on the schedule according to which the synthesis operations are executed. In this work, we study the…
A.J.R. Cotter
A simulator, ‘ECOLPS’ in R, is developed and trialed for ecological studies of closed aquatic ecosystems. Its constraint-based approach contrasts with function-based models widely applied in ecology. Total gross production (ΣGP) by ‘wild components’ (= species/life stages, grouped by ecological roles) is maximized…
Zhuo Dai, Yefu Zhou, Bibhas Chandra Giri
In supply chain management, the location of facilities, inventory control, and vehicle routing are three key components. This paper incorporates a two-warehouse inventory system into the location- inventory-routing problems (LIRPs) and develops LIRP models with two warehouses in one-level, two-level, and three-level…
Wynand S. Verwoerd, Longfei Mao
The solution space of an FBA-based model of cellular metabolism, can be characterized by extraction of a bounded, low dimensional kernel (the SSK) that facilitates perceiving it as a geometric object in multidimensional flux space. The aim is to produce an amenable description, intermediate between the single feasible…
S. Angammal, G. Hannah Grace
In agriculture, crop planning and land distribution have been important research subjects. The distribution of land involves several multi-functional tasks, such as maximizing output and profit and minimizing costs. These functions are influenced by a variety of uncertain elements, including yield, crop price, and…
Ibrahim M. Hezam, Sarah A. H. Taher, Abdelaziz Foul, Adel Fahad Alrasheedi
'Adel Fahad Alrasheedi'] We develop neutrosophic goal programming models for sustainable resource planning in a healthcare organization. The neutrosophic approach can help examine the imprecise aspiration levels of resources. For deneutrosophication, the neutrosophic value is transformed into three intervals based on…
Authors not listed
We present a vector-based method to balance chemical reactions. The algorithm builds candidates in a deterministic way, removes duplicates, and always prints coefficients in the lowest whole-number form. For redox cases, electrons and protons/hydroxide are treated explicitly, so both mass and charge are balanced. We…
Andrew McCluskey
The use of mathematical transformations to reduce non-linear functions to linear problems, which can be tackled with analytical linear regression, is commonplace in the chemistry curriculum. The linearization procedure, however, assumes an incorrect statistical model for real experimental data; leading to biased…
Authors not listed
Inverse problems, where we seek the values of inputs to a model that lead to a desired set of outputs, are a challenges subset of problems in science and engineering. In this work we demonstrate the use of two generative AI methods to solve inverse problems. We compare this approach to two more conventional approaches…
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…