11 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…
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…
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…
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 +…
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…
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…