Pub Date : 2023-09-26DOI: 10.1007/s11590-023-02052-2
Francesco Biancalani, Giorgio Gnecco, Rodolfo Metulini, Massimo Riccaboni
Abstract In the recent past, annual CO $$_2$$ 2 emissions at the international level were examined from various perspectives, motivated by rising concerns about pollution and climate change. Nevertheless, to the best of the authors’ knowledge, the problem of dealing with the potential inaccuracy/missingness of such data at the country and economic sector levels has been overlooked. Thereby, in this article we apply a supervised machine learning technique called Matrix Completion (MC) to predict, for each country in the available database, annual CO $$_2$$ 2 emissions data at the sector level, based on past data related to all the sectors, and more recent data related to a subset of sectors. The core idea of MC consists in the formulation of a suitable optimization problem, namely the minimization of a proper trade-off between the approximation error over a set of observed elements of a matrix (training set) and a proxy of the rank of the reconstructed matrix, e.g., its nuclear norm. In the article, we apply MC to the imputation of (artificially) missing elements of country-specific matrices whose elements come from annual CO $$_2$$ 2 emission levels related to different sectors, after proper pre-processing at the sector level. Results highlight typically a better performance of the combination of MC with suitably-constructed baseline estimates with respect to the baselines alone. Potential applications of our analysis arise in the prediction of currently missing elements of matrices of annual CO $$_2$$ 2 emission levels and in the construction of counterfactuals, useful to estimate the effects of policy changes able to influence the annual CO $$_2$$ 2 emission levels of specific sectors in selected countries.
{"title":"Prediction of annual CO2 emissions at the country and sector levels, based on a matrix completion optimization problem","authors":"Francesco Biancalani, Giorgio Gnecco, Rodolfo Metulini, Massimo Riccaboni","doi":"10.1007/s11590-023-02052-2","DOIUrl":"https://doi.org/10.1007/s11590-023-02052-2","url":null,"abstract":"Abstract In the recent past, annual CO $$_2$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:msub> <mml:mrow /> <mml:mn>2</mml:mn> </mml:msub> </mml:math> emissions at the international level were examined from various perspectives, motivated by rising concerns about pollution and climate change. Nevertheless, to the best of the authors’ knowledge, the problem of dealing with the potential inaccuracy/missingness of such data at the country and economic sector levels has been overlooked. Thereby, in this article we apply a supervised machine learning technique called Matrix Completion (MC) to predict, for each country in the available database, annual CO $$_2$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:msub> <mml:mrow /> <mml:mn>2</mml:mn> </mml:msub> </mml:math> emissions data at the sector level, based on past data related to all the sectors, and more recent data related to a subset of sectors. The core idea of MC consists in the formulation of a suitable optimization problem, namely the minimization of a proper trade-off between the approximation error over a set of observed elements of a matrix (training set) and a proxy of the rank of the reconstructed matrix, e.g., its nuclear norm. In the article, we apply MC to the imputation of (artificially) missing elements of country-specific matrices whose elements come from annual CO $$_2$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:msub> <mml:mrow /> <mml:mn>2</mml:mn> </mml:msub> </mml:math> emission levels related to different sectors, after proper pre-processing at the sector level. Results highlight typically a better performance of the combination of MC with suitably-constructed baseline estimates with respect to the baselines alone. Potential applications of our analysis arise in the prediction of currently missing elements of matrices of annual CO $$_2$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:msub> <mml:mrow /> <mml:mn>2</mml:mn> </mml:msub> </mml:math> emission levels and in the construction of counterfactuals, useful to estimate the effects of policy changes able to influence the annual CO $$_2$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:msub> <mml:mrow /> <mml:mn>2</mml:mn> </mml:msub> </mml:math> emission levels of specific sectors in selected countries.","PeriodicalId":49720,"journal":{"name":"Optimization Letters","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134958760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-25DOI: 10.1007/s11590-023-02051-3
Xiao Ling, Anh Bui, Paul Brooks
{"title":"Kernel $$ell ^1$$-norm principal component analysis for denoising","authors":"Xiao Ling, Anh Bui, Paul Brooks","doi":"10.1007/s11590-023-02051-3","DOIUrl":"https://doi.org/10.1007/s11590-023-02051-3","url":null,"abstract":"","PeriodicalId":49720,"journal":{"name":"Optimization Letters","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135817007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-25DOI: 10.1007/s11590-023-02054-0
Michael J. Brusco, Douglas Steinley, Ashley L. Watts
{"title":"A maximal-clique-based set-covering approach to overlapping community detection","authors":"Michael J. Brusco, Douglas Steinley, Ashley L. Watts","doi":"10.1007/s11590-023-02054-0","DOIUrl":"https://doi.org/10.1007/s11590-023-02054-0","url":null,"abstract":"","PeriodicalId":49720,"journal":{"name":"Optimization Letters","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135816094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-23DOI: 10.1007/s11590-023-02062-0
A. Gosavi, L. H. Sneed, L. A. Spearing
{"title":"Deep reinforcement learning for approximate policy iteration: convergence analysis and a post-earthquake disaster response case study","authors":"A. Gosavi, L. H. Sneed, L. A. Spearing","doi":"10.1007/s11590-023-02062-0","DOIUrl":"https://doi.org/10.1007/s11590-023-02062-0","url":null,"abstract":"","PeriodicalId":49720,"journal":{"name":"Optimization Letters","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135966863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-21DOI: 10.1007/s11590-023-02059-9
Nenad Mladenović, Angelo Sifaleras, Andrei Sleptchenko
Abstract This special issue contains 15 papers submitted by the participants of the 8th International Conference on Variable Neighborhood Search (ICVNS 2021), which was held in Abu Dhabi, U.A.E., online due to COVID-19 restrictions, on March 22–24, 2021.
{"title":"Special issue dedicated to the 8th International Conference on Variable Neighborhood Search (ICVNS 2021)","authors":"Nenad Mladenović, Angelo Sifaleras, Andrei Sleptchenko","doi":"10.1007/s11590-023-02059-9","DOIUrl":"https://doi.org/10.1007/s11590-023-02059-9","url":null,"abstract":"Abstract This special issue contains 15 papers submitted by the participants of the 8th International Conference on Variable Neighborhood Search (ICVNS 2021), which was held in Abu Dhabi, U.A.E., online due to COVID-19 restrictions, on March 22–24, 2021.","PeriodicalId":49720,"journal":{"name":"Optimization Letters","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136154354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-20DOI: 10.1007/s11590-023-02047-z
Shiqiang Zhang, Robert M. Lee, Behrang Shafei, David Walz, Ruth Misener
Abstract Constrained Bayesian optimization optimizes a black-box objective function subject to black-box constraints. For simplicity, most existing works assume that multiple constraints are independent. To ask, when and how does dependence between constraints help? , we remove this assumption and implement probability of feasibility with dependence (Dep-PoF) by applying multiple output Gaussian processes (MOGPs) as surrogate models and using expectation propagation to approximate the probabilities. We compare Dep-PoF and the independent version PoF. We propose two new acquisition functions incorporating Dep-PoF and test them on synthetic and practical benchmarks. Our results are largely negative: incorporating dependence between the constraints does not help much. Empirically, incorporating dependence between constraints may be useful if: (i) the solution is on the boundary of the feasible region(s) or (ii) the feasible set is very small. When these conditions are satisfied, the predictive covariance matrix from the MOGP may be poorly approximated by a diagonal matrix and the off-diagonal matrix elements may become important. Dep-PoF may apply to settings where (i) the constraints and their dependence are totally unknown and (ii) experiments are so expensive that any slightly better Bayesian optimization procedure is preferred. But, in most cases, Dep-PoF is indistinguishable from PoF.
{"title":"Dependence in constrained Bayesian optimization","authors":"Shiqiang Zhang, Robert M. Lee, Behrang Shafei, David Walz, Ruth Misener","doi":"10.1007/s11590-023-02047-z","DOIUrl":"https://doi.org/10.1007/s11590-023-02047-z","url":null,"abstract":"Abstract Constrained Bayesian optimization optimizes a black-box objective function subject to black-box constraints. For simplicity, most existing works assume that multiple constraints are independent. To ask, when and how does dependence between constraints help? , we remove this assumption and implement probability of feasibility with dependence (Dep-PoF) by applying multiple output Gaussian processes (MOGPs) as surrogate models and using expectation propagation to approximate the probabilities. We compare Dep-PoF and the independent version PoF. We propose two new acquisition functions incorporating Dep-PoF and test them on synthetic and practical benchmarks. Our results are largely negative: incorporating dependence between the constraints does not help much. Empirically, incorporating dependence between constraints may be useful if: (i) the solution is on the boundary of the feasible region(s) or (ii) the feasible set is very small. When these conditions are satisfied, the predictive covariance matrix from the MOGP may be poorly approximated by a diagonal matrix and the off-diagonal matrix elements may become important. Dep-PoF may apply to settings where (i) the constraints and their dependence are totally unknown and (ii) experiments are so expensive that any slightly better Bayesian optimization procedure is preferred. But, in most cases, Dep-PoF is indistinguishable from PoF.","PeriodicalId":49720,"journal":{"name":"Optimization Letters","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136307342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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