24 papers · ranked by Valyu relevance
Sen Bai, Chunqi Yang, Xin Bai, Xin Zhang + 1 more
Binary (0-1) integer programming (BIP) is pivotal in scientific domains requiring discrete decisionmaking. As the advance of AI computing, recent works explore neural network-based solvers for integer linear programming (ILP) problems. Yet, they lack scalability for tackling nonlinear challenges. To handle…
Christian Blum, Haroldo Gambini Santos
Construct, Merge, Solve & Adapt (CMSA) is a general hybrid metaheuristic for solving combinatorial optimization problems. At each iteration, CMSA (1) constructs feasible solutions to the tackled problem instance in a probabilistic way and (2) solves a reduced problem instance (if possible) to optimality. The…
Vicky Mak‐Hau, John Yearwood, William Moran
In this paper, we investigate the constraint typology of mixed-integer linear programming (MILP) formulations. MILP is a commonly used mathematical programming technique for modelling and solving real-life scheduling, routing, planning, resource allocation, timetabling optimization problems, providing optimized…
Ghanshyam Chandra, Md Helal Hossen, Stephan Scholz, Alexander T Dilthey + 2 more
Affordable genotyping methods are essential in genomics. Commonly used genotyping methods primarily support single nucleotide variants and short indels but neglect structural variants. Additionally, accuracy of read alignments to a reference genome is unreliable in highly polymorphic and repetitive regions, further…
Robert Nieuwenhuis, Albert Oliveras, Enric Rodríguez-Carbonell
State-of-the-art SAT solvers are nowadays able to handle huge real-world instances. The key to this success is the so-called Conflict-Driven Clause-Learning (CDCL) scheme, which encompasses a number of techniques that exploit the conflicts that are encountered during the search for a solution. In this article we extend…
Elisabeth Gaar, Jon Lee, Ivana Ljubić, Markus Sinnl + 1 more
We study a class of integer bilevel programs with second-order cone constraints at the upper-level and a convex-quadratic objective function and linear constraints at the lower-level. We develop disjunctive cuts (DCs) to separate bilevel-infeasible solutions using a second-order-cone-based cut-generating procedure. We…
Xinyu Gu, Stefan Ivanovic, Daniel W. Feng, Mohammed El-Kebir
Summarizing a collection P of related RNA secondary structures is a key challenge in applications like evolutionary analysis, alternative fold studies and mRNA vaccine design. This requires both clustering the input structures into similar groups and identifying the core structural motifs on which they agree or differ.…
Tadashi Kadowaki, Mitsuru Ambai
In edge computing, suppressing data size is a challenge for machine learning models that perform complex tasks such as autonomous driving, in which computational resources (speed, memory size and power) are limited. Efficient lossy compression of matrix data has been introduced by decomposing it into the product of an…
Albert No
The size of the largest binary single deletion code has been unknown for more than 50 years. It is known that Varshamov-Tenengolts (VT) code is an optimum single deletion code for block length $n\leq10$; however, only a few upper bounds of the size of single deletion code are proposed for larger n. We provide improved…
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…
Theo Knijnenburg, Gunnar Klau, Francesco Iorio, Mathew Garnett + 3 more
Mining large datasets using machine learning approaches often leads to models that are hard to interpret and not amenable to the generation of hypotheses that can be experimentally tested. Finding ‘actionable knowledge’ is becoming more important, but also more challenging as datasets grow in size and complexity. We…
Broderick Crawford, Álex Paz, Ricardo Soto, Álvaro Peña Fritz + 7 more
Metaheuristics are a fundament pillar of Industry 4.0, as they allow for complex optimization problems to be solved by finding good solutions in a reasonable amount of computational time. One category of important problems in modern industry is that of binary problems, where decision variables can take values of zero…
William Pettersson, Melih Özlen
To obtain a better understanding of the trade-offs between various objectives, Bi-Objective Integer Programming (BOIP) algorithms calculate the set of all non-dominated vectors and present these as the solution to a BOIP problem. Historically, these algorithms have been compared in terms of the number of…
Gioni Mexi, Dominik Kamp, Yuji Shinano, Shanwen Pu + 7 more
'Ksenia Bestuzheva' 'Christopher Hojny' 'Matthias Walter' 'Marc E. Pfetsch' 'Sebastian Pokutta' 'Thorsten Koch'] The Pseudo-Boolean problem deals with linear or polynomial constraints with integer coefficients over Boolean variables. The objective lies in optimizing a linear objective function, or finding a feasible…
Dante Leiva, Benjamín Ramos-Tapia, Broderick Crawford, Ricardo Soto + 2 more
'Felipe Cisternas-Caneo' 'Ameer Hamza Khan'] The set-covering problem aims to find the smallest possible set of subsets that cover all the elements of a larger set. The difficulty of solving the set-covering problem increases as the number of elements and sets grows, making it a complex problem for which traditional…
Ayşe Beşkirli, Changsheng Zhang, Haitong Zhao
In this study, the pied kingfisher optimizer (PKO) algorithm is adapted to the uncapacitated facility location problem (UFLP), and its performance is evaluated. The PKO algorithm is binarized with fourteen different transfer functions (TF), and each variant is tested on a total of fifteen different Cap problems. In…
Daniel Lokshtanov
In the Integer Quadratic Programming problem input is an n × n integer matrix Q, an m × n integer matrix A and an m-dimensional integer vector b. The task is to find a vector x ∈ Z n minimizing x TQx, subject to Ax ≤ b. We give a fixed parameter tractable algorithm for Integer Quadratic Programming parameterized by n +…
Broderick Crawford, Benjamín López Cortés, Felipe Cisternas-Caneo, José Manuel Gómez-Pulido + 7 more
Binarizing continuous metaheuristics to solve challenging NP-hard binary optimization problems is a fundamental step in adapting continuous algorithms for discrete domains. Binary optimization problems, such as the Set Covering Problem and the 0-1 Knapsack Problem, demand tailored approaches to efficiently explore and…
Michael Hartisch, Ulf Lorenz
The necessity to deal with uncertain data is a major challenge in decision making. Robust optimization emerged as one of the predominant paradigms to produce solutions that hedge against uncertainty. In order to obtain an even more realistic description of the underlying problem where the decision maker can react to…
Shouyong Jiang, Yong Wang, Marcus Kaiser, Natalio Krasnogor
Flux balance analysis (FBA) based bilevel optimisation has been a great success in redesigning metabolic networks for biochemical overproduction. To date, many computational approaches have been developed to solve the resulting bilevel optimisation problems. However, most of them are of limited use due to biased…
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…
Liwei Cao, Danilo Russo, Vassilios S. Vassiliadis, Alexei Lapkin
A mixed-integer nonlinear programming (MINLP) formulation for symbolic regression was proposed to identify physical models from noisy experimental data. The formulation was tested using numerical models and was found to be more efficient than the previous literature example with respect to the number of predictor…
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…
Eugene Christo V R, Christoph Robert Meinecke, Bert Nitzsche, Roman Lyttleton + 5 more
Network-based biocomputing (NBC) presents an energy-efficient, parallel computing approach for solving nondeterministic polynomial time (NP) complete problems by leveraging motor-driven cytoskeletal filaments that explore all possible solutions through nanofabricated networks in a massively parallel fashion. However…