Progress in Academic Computational Integer Programming

Koch, Thorsten; Martín, Alexander; Pfetsch, Marc E.

doi:10.1007/978-3-642-38189-8_19

Cited by 13 publications

(8 citation statements)

References 41 publications

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“…This is one of the most important and cited library in the contect of MILPs. The comparison shows that three closed source and commercial solvers (GUROBI, XPRESS, and CPLEX) are close in their performance and have better performance than the other solvers with respect to both computation time and number of optimally solved problems (Koch et al, 2013;Jablonský, 2015;Meindl and Templ, 2013). Further literature research shows that CPLEX is the solver used most by researchers (Koch et al, 2013).…”

Section: Solvermentioning

confidence: 99%

“…All solvers have been used in the implementation of a vast number of techniques within a branch and cut method. Koch et al (2013), Jablonský (2015), and Meindl and Templ (2013) compare the performance of selected solvers on test cases taken from the Mixed Integer Programming LIBrary (MIPLIB) from https://miplib.zib.de/ . This is one of the most important and cited library in the contect of MILPs.…”

Section: Solvermentioning

confidence: 99%

See 1 more Smart Citation

Green hub-and-spoke network design for aviation industry

Parsa

Nookabadi

Flapper

et al. 2019

Journal of Cleaner Production

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Section: Solvermentioning

confidence: 99%

Section: Solvermentioning

confidence: 99%

Green hub-and-spoke network design for aviation industry

Parsa

Nookabadi

Flapper

et al. 2019

Journal of Cleaner Production

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“…Although Moore's law has held true for approximately 40 years [31,32] and there have been significant advances made in the branch-and-bound algorithms used for solving big mixed integer linear programs (MILPs) such as those used in energy system optimization models [33], a decelerating increase of transistor density could be observed in recent years [34]. On the other hand, liberalization, decentralization, and an increasing volatility in energy generation [35] are leading to more complex applications for ESMs.…”

Section: Motivation and Scope Of The Reviewmentioning

confidence: 99%

A Review on Time Series Aggregation Methods for Energy System Models

et al. 2020

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Due to the high degree of intermittency of renewable energy sources (RES) and the growing interdependences amongst formerly separated energy pathways, the modeling of adequate energy systems is crucial to evaluate existing energy systems and to forecast viable future ones. However, this corresponds to the rising complexity of energy system models (ESMs) and often results in computationally intractable programs. To overcome this problem, time series aggregation (TSA) is frequently used to reduce ESM complexity. As these methods aim at the reduction of input data and preserving the main information about the time series, but are not based on mathematically equivalent transformations, the performance of each method depends on the justifiability of its assumptions. This review systematically categorizes the TSA methods applied in 130 different publications to highlight the underlying assumptions and to evaluate the impact of these on the respective case studies. Moreover, the review analyzes current trends in TSA and formulates subjects for future research. This analysis reveals that the future of TSA is clearly feature-based including clustering and other machine learning techniques which are capable of dealing with the growing amount of input data for ESMs. Further, a growing number of publications focus on bounding the TSA induced error of the ESM optimization result. Thus, this study can be used as both an introduction to the topic and for revealing remaining research gaps.

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“…For each variable x j , the interval [ j , u j ] is called its domain. Surprisingly fast solvers for solving MIPs have been developed in practice despite MIPs being N P-hard in the worst case [3,8]. To this end, the most successful method has been the branch-and-bound algorithm [9] and its numerous extensions.…”

Section: Introductionmentioning

confidence: 99%

An Algorithm-Independent Measure of Progress for Linear Constraint Propagation

Sofranac¹,

Gleixner²,

Pokutta³

2021

Preprint

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Domain propagation of linear constraints has become a crucial sub-routine in modern Mixed-Integer Programming (MIP) solvers. In practice, iterative algorithms with tolerance-based stopping criteria are used to avoid problems with slow or infinite convergence. However, these heuristic stopping criteria can pose difficulties for fairly comparing the efficiency of different implementations of iterative domain propagation in a real-world setting. Most significantly, the presence of unbounded variable domains in the problem formulation makes it difficult to quantify the relative size of reductions performed on them. In this work, we develop a method to measure-independently of the algorithmic design-the progress that a given iterative domain propagation procedure has made at a given point in time during its execution. Our measure makes it possible to study and better compare the behavior of domain propagation algorithms for linear constraints. We apply the new measure to answer two questions of practical relevance: (i) We investigate to what extent heuristic stopping criteria can lead to premature termination on real-world MIP instances. (ii) We compare a GPU-parallel domain propagation algorithm against a sequential state-of-the-art implementation and show that the parallel version is even more competitive in a real-world setting than originally reported.

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Progress in Academic Computational Integer Programming

Cited by 13 publications

References 41 publications

Green hub-and-spoke network design for aviation industry

Green hub-and-spoke network design for aviation industry

A Review on Time Series Aggregation Methods for Energy System Models

An Algorithm-Independent Measure of Progress for Linear Constraint Propagation

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