{"title":"参数化复杂性中的整数规划:五个缩影","authors":"Tomáš Gavenčiak , Martin Koutecký , Dušan Knop","doi":"10.1016/j.disopt.2020.100596","DOIUrl":null,"url":null,"abstract":"<div><p>Powerful results from the theory of integer programming have recently led to substantial advances in parameterized complexity. However, our perception is that, except for Lenstra’s algorithm for solving integer linear programming in fixed dimension, there is still little understanding in the parameterized complexity community of the strengths and limitations of the available tools. This is understandable: it is often difficult to infer exact runtimes or even the distinction between <span><math><mi>FPT</mi></math></span> and <span><math><mi>XP</mi></math></span> algorithms, and some knowledge is simply unwritten folklore in a different community. We wish to make a step in remedying this situation. To that end, we first provide an easy to navigate quick reference guide of integer programming algorithms from the perspective of parameterized complexity. Then, we show their applications in three case studies, obtaining <span><math><mi>FPT</mi></math></span> algorithms with runtime <span><math><mrow><mi>f</mi><mrow><mo>(</mo><mi>k</mi><mo>)</mo></mrow></mrow></math></span>poly<span><math><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow></math></span>. We focus on: </p><ul><li><span>•</span><span><p><em>Modeling</em>: since the algorithmic results follow by applying existing algorithms to new models, we shift the focus from the complexity result to the modeling result, highlighting common patterns and tricks which are used.</p></span></li><li><span>•</span><span><p><em>Optimality program:</em> after giving an <span><math><mi>FPT</mi></math></span> algorithm, we are interested in reducing the dependence on the parameter; we show which algorithms and tricks are often useful for speed-ups.</p></span></li><li><span>•</span><span><p><em>Minding the</em> poly<span><math><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow></math></span>: reducing <span><math><mrow><mi>f</mi><mrow><mo>(</mo><mi>k</mi><mo>)</mo></mrow></mrow></math></span> often has the unintended consequence of increasing poly<span><math><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow></math></span>; so we highlight the common trade-offs and show how to get the best of both worlds.</p></span></li></ul> Specifically, we consider graphs of bounded neighborhood diversity which are in a sense the simplest of dense graphs, and we show several <span><math><mi>FPT</mi></math></span> algorithms for <span>Capacitated Dominating Set</span>, <span>Sum Coloring</span>, <span>Max-</span>\n<span><math><mi>q</mi></math></span>\n<span>-Cut</span>, and certain other coloring problems by modeling them as convex programs in fixed dimension, <span><math><mi>n</mi></math></span>-fold integer programs, bounded dual treewidth programs, indefinite quadratic programs in fixed dimension, parametric integer programs in fixed dimension, and 2-stage stochastic integer programs.</div>","PeriodicalId":50571,"journal":{"name":"Discrete Optimization","volume":"44 ","pages":"Article 100596"},"PeriodicalIF":0.9000,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.disopt.2020.100596","citationCount":"15","resultStr":"{\"title\":\"Integer programming in parameterized complexity: Five miniatures\",\"authors\":\"Tomáš Gavenčiak , Martin Koutecký , Dušan Knop\",\"doi\":\"10.1016/j.disopt.2020.100596\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Powerful results from the theory of integer programming have recently led to substantial advances in parameterized complexity. However, our perception is that, except for Lenstra’s algorithm for solving integer linear programming in fixed dimension, there is still little understanding in the parameterized complexity community of the strengths and limitations of the available tools. This is understandable: it is often difficult to infer exact runtimes or even the distinction between <span><math><mi>FPT</mi></math></span> and <span><math><mi>XP</mi></math></span> algorithms, and some knowledge is simply unwritten folklore in a different community. We wish to make a step in remedying this situation. To that end, we first provide an easy to navigate quick reference guide of integer programming algorithms from the perspective of parameterized complexity. Then, we show their applications in three case studies, obtaining <span><math><mi>FPT</mi></math></span> algorithms with runtime <span><math><mrow><mi>f</mi><mrow><mo>(</mo><mi>k</mi><mo>)</mo></mrow></mrow></math></span>poly<span><math><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow></math></span>. We focus on: </p><ul><li><span>•</span><span><p><em>Modeling</em>: since the algorithmic results follow by applying existing algorithms to new models, we shift the focus from the complexity result to the modeling result, highlighting common patterns and tricks which are used.</p></span></li><li><span>•</span><span><p><em>Optimality program:</em> after giving an <span><math><mi>FPT</mi></math></span> algorithm, we are interested in reducing the dependence on the parameter; we show which algorithms and tricks are often useful for speed-ups.</p></span></li><li><span>•</span><span><p><em>Minding the</em> poly<span><math><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow></math></span>: reducing <span><math><mrow><mi>f</mi><mrow><mo>(</mo><mi>k</mi><mo>)</mo></mrow></mrow></math></span> often has the unintended consequence of increasing poly<span><math><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow></math></span>; so we highlight the common trade-offs and show how to get the best of both worlds.</p></span></li></ul> Specifically, we consider graphs of bounded neighborhood diversity which are in a sense the simplest of dense graphs, and we show several <span><math><mi>FPT</mi></math></span> algorithms for <span>Capacitated Dominating Set</span>, <span>Sum Coloring</span>, <span>Max-</span>\\n<span><math><mi>q</mi></math></span>\\n<span>-Cut</span>, and certain other coloring problems by modeling them as convex programs in fixed dimension, <span><math><mi>n</mi></math></span>-fold integer programs, bounded dual treewidth programs, indefinite quadratic programs in fixed dimension, parametric integer programs in fixed dimension, and 2-stage stochastic integer programs.</div>\",\"PeriodicalId\":50571,\"journal\":{\"name\":\"Discrete Optimization\",\"volume\":\"44 \",\"pages\":\"Article 100596\"},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2022-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/j.disopt.2020.100596\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Discrete Optimization\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S157252862030030X\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MATHEMATICS, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Discrete Optimization","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S157252862030030X","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
Integer programming in parameterized complexity: Five miniatures
Powerful results from the theory of integer programming have recently led to substantial advances in parameterized complexity. However, our perception is that, except for Lenstra’s algorithm for solving integer linear programming in fixed dimension, there is still little understanding in the parameterized complexity community of the strengths and limitations of the available tools. This is understandable: it is often difficult to infer exact runtimes or even the distinction between and algorithms, and some knowledge is simply unwritten folklore in a different community. We wish to make a step in remedying this situation. To that end, we first provide an easy to navigate quick reference guide of integer programming algorithms from the perspective of parameterized complexity. Then, we show their applications in three case studies, obtaining algorithms with runtime poly. We focus on:
•
Modeling: since the algorithmic results follow by applying existing algorithms to new models, we shift the focus from the complexity result to the modeling result, highlighting common patterns and tricks which are used.
•
Optimality program: after giving an algorithm, we are interested in reducing the dependence on the parameter; we show which algorithms and tricks are often useful for speed-ups.
•
Minding the poly: reducing often has the unintended consequence of increasing poly; so we highlight the common trade-offs and show how to get the best of both worlds.
Specifically, we consider graphs of bounded neighborhood diversity which are in a sense the simplest of dense graphs, and we show several algorithms for Capacitated Dominating Set, Sum Coloring, Max--Cut, and certain other coloring problems by modeling them as convex programs in fixed dimension, -fold integer programs, bounded dual treewidth programs, indefinite quadratic programs in fixed dimension, parametric integer programs in fixed dimension, and 2-stage stochastic integer programs.
期刊介绍:
Discrete Optimization publishes research papers on the mathematical, computational and applied aspects of all areas of integer programming and combinatorial optimization. In addition to reports on mathematical results pertinent to discrete optimization, the journal welcomes submissions on algorithmic developments, computational experiments, and novel applications (in particular, large-scale and real-time applications). The journal also publishes clearly labelled surveys, reviews, short notes, and open problems. Manuscripts submitted for possible publication to Discrete Optimization should report on original research, should not have been previously published, and should not be under consideration for publication by any other journal.