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Tensor denoising via dual Schatten norms 利用双夏腾范数进行张量去噪
4区 数学
Optimization Letters Pub Date : 2023-09-27 DOI: 10.1007/s11590-023-02068-8
Maryam Bagherian
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引用次数: 0
A neurodynamic approach for joint chance constrained rectangular geometric optimization 关节机会约束矩形几何优化的神经动力学方法
4区 数学
Optimization Letters Pub Date : 2023-09-26 DOI: 10.1007/s11590-023-02050-4
Siham Tassouli, Abdel Lisser
{"title":"A neurodynamic approach for joint chance constrained rectangular geometric optimization","authors":"Siham Tassouli, Abdel Lisser","doi":"10.1007/s11590-023-02050-4","DOIUrl":"https://doi.org/10.1007/s11590-023-02050-4","url":null,"abstract":"","PeriodicalId":49720,"journal":{"name":"Optimization Letters","volume":"20 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":"134960073","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}
引用次数: 0
Prediction of annual CO2 emissions at the country and sector levels, based on a matrix completion optimization problem 基于矩阵完成优化问题,预测国家和部门的年二氧化碳排放量
4区 数学
Optimization Letters Pub Date : 2023-09-26 DOI: 10.1007/s11590-023-02052-2
Francesco Biancalani, Giorgio Gnecco, Rodolfo Metulini, Massimo Riccaboni
{"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}
引用次数: 0
Kernel $$ell ^1$$-norm principal component analysis for denoising 核$$ell ^1$$ -范数主成分分析去噪
4区 数学
Optimization Letters Pub Date : 2023-09-25 DOI: 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}
引用次数: 1
A maximal-clique-based set-covering approach to overlapping community detection 基于最大集团的集合覆盖重叠社区检测方法
4区 数学
Optimization Letters Pub Date : 2023-09-25 DOI: 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}
引用次数: 0
Deep reinforcement learning for approximate policy iteration: convergence analysis and a post-earthquake disaster response case study 近似策略迭代的深度强化学习:收敛分析和震后灾难响应案例研究
4区 数学
Optimization Letters Pub Date : 2023-09-23 DOI: 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}
引用次数: 0
Higher-order optimality conditions of robust Benson proper efficient solutions in uncertain vector optimization problems 不确定向量优化问题鲁棒Benson固有有效解的高阶最优性条件
4区 数学
Optimization Letters Pub Date : 2023-09-23 DOI: 10.1007/s11590-023-02061-1
Qilin Wang, Jing Jin, Yuwen Zhai
{"title":"Higher-order optimality conditions of robust Benson proper efficient solutions in uncertain vector optimization problems","authors":"Qilin Wang, Jing Jin, Yuwen Zhai","doi":"10.1007/s11590-023-02061-1","DOIUrl":"https://doi.org/10.1007/s11590-023-02061-1","url":null,"abstract":"","PeriodicalId":49720,"journal":{"name":"Optimization Letters","volume":"45 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":"135966855","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}
引用次数: 0
On optimal universal first-order methods for minimizing heterogeneous sums 非均匀和最小化的最优通用一阶方法
4区 数学
Optimization Letters Pub Date : 2023-09-22 DOI: 10.1007/s11590-023-02060-2
Benjamin Grimmer
{"title":"On optimal universal first-order methods for minimizing heterogeneous sums","authors":"Benjamin Grimmer","doi":"10.1007/s11590-023-02060-2","DOIUrl":"https://doi.org/10.1007/s11590-023-02060-2","url":null,"abstract":"","PeriodicalId":49720,"journal":{"name":"Optimization Letters","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136061604","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}
引用次数: 3
Special issue dedicated to the 8th International Conference on Variable Neighborhood Search (ICVNS 2021) 第八届可变邻域搜索国际会议(ICVNS 2021)特刊
4区 数学
Optimization Letters Pub Date : 2023-09-21 DOI: 10.1007/s11590-023-02059-9
Nenad Mladenović, Angelo Sifaleras, Andrei Sleptchenko
{"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}
引用次数: 0
Dependence in constrained Bayesian optimization 约束贝叶斯优化中的相关性
4区 数学
Optimization Letters Pub Date : 2023-09-20 DOI: 10.1007/s11590-023-02047-z
Shiqiang Zhang, Robert M. Lee, Behrang Shafei, David Walz, Ruth Misener
{"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}
引用次数: 0
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