Reference number : GC-2017-Q1-01791, amendment number : , agreement type : G, recipient legal name : Lu, Zhaosong (Simon Fraser University), recipient country : CA, recipient province : nan, recipient city : British Columbia|Colombie-Britannique, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : Nowadays structural optimization problems frequently arise in engineering and sciences, especially in data analytics, machine learning and statistics. In big data era, these problems are often of high dimension and consist of large number of functions in their objective and/or constraints. This brings unprecedented challenges to traditional optimization methods. The objectives of this proposal are to propose efficient methods and develop software for solving them, and explore their applications in data analytics and machine learning. 
 
The proposed research consists of three parts: 1) developing efficient methods for minimization of a finite sum of functions that has numerous applications in machine learning and statistics; 2) exploring block coordinate updated methods for solving large-scale structural optimization problems; and 3) studying efficient methods for solving large-scale constrained structural optimization problems. 
 
The success of this project will provide efficient methods and software for solving large-scale structural optimization problems. They will help government, financial institutions, business and industry to use larger data sets to make a better prediction and decision for the future that will produce long-term benefits. The success of this proposed research will also result in new optimization theory and techniques that complement existing knowledge of continuous optimization., program purpose french : Nowadays structural optimization problems frequently arise in engineering and sciences, especially in data analytics, machine learning and statistics. In big data era, these problems are often of high dimension and consist of large number of functions in their objective and/or constraints. This brings unprecedented challenges to traditional optimization methods. The objectives of this proposal are to propose efficient methods and develop software for solving them, and explore their applications in data analytics and machine learning. 
 
The proposed research consists of three parts: 1) developing efficient methods for minimization of a finite sum of functions that has numerous applications in machine learning and statistics; 2) exploring block coordinate updated methods for solving large-scale structural optimization problems; and 3) studying efficient methods for solving large-scale constrained structural optimization problems. 
 
The success of this project will provide efficient methods and software for solving large-scale structural optimization problems. They will help government, financial institutions, business and industry to use larger data sets to make a better prediction and decision for the future that will produce long-term benefits. The success of this proposed research will also result in new optimization theory and techniques that complement existing knowledge of continuous optimization., agreement title english : Efficient methods for large-scale structural optimization with application to machine learning, agreement title french : Efficient methods for large-scale structural optimization with application to machine learning, agreement number : RGPIN, agreement value : 140000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada