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公开(公告)号：US20200027028A1
公开(公告)日：2020-01-23
申请号：US16297952
申请日：2019-03-11
Applicant: SAS Institute Inc.
Inventor： Rui Shi , Guixian Lin , Xiangqian Hu , Yan Xu
Abstract: A computing device compresses a gradient boosting tree predictive model. A gradient boosting tree predictive model is trained using a plurality of observation vectors. Each observation vector includes an explanatory variable value of an explanatory variable and a response variable value for a response variable. The gradient boosting tree predictive type model is trained to predict the response variable value of each observation vector based on a respective explanatory variable value of each observation vector. The trained gradient boosting tree predictive model is compressed using a compression model with a predefined penalty constant value and with a predefined array of coefficients to reduce a number of trees of the trained gradient boosting tree predictive model. The compression model minimizes a sparsity norm loss function. The compressed, trained gradient boosting tree predictive model is output for predicting a new response variable value from a new observation vector.
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公开(公告)号：US10049302B1
公开(公告)日：2018-08-14
申请号：US15911882
申请日：2018-03-05
Applicant: SAS Institute Inc.
Inventor： Jun Liu , Yan Xu , Joshua David Griffin , Manoj Keshavmurthi Chari
Abstract: A computing device trains models for streaming classification. A baseline penalty value is computed that is inversely proportional to a square of a maximum explanatory variable value. A set of penalty values is computed based on the baseline penalty value. For each penalty value of the set of penalty values, a classification type model is trained using the respective penalty value and the observation vectors to compute parameters that define a trained model, the classification type model is validated using the respective penalty value and the observation vectors to compute a validation criterion value that quantifies a validation error, and the validation criterion value, the respective penalty value, and the parameters that define a trained model are stored to the computer-readable medium. The classification type model is trained to predict the response variable value of each observation vector based on the respective explanatory variable value of each observation vector.
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13.发明授权Systems and methods for conflict resolution and stabilizing cut generation in a mixed integer program solver 有权混合整数程序求解器中的冲突解决和稳定切割生成的系统和方法公开(公告)号：US09524471B2
公开(公告)日：2016-12-20
申请号：US14068581
申请日：2013-10-31
Applicant: SAS Institute Inc.
Inventor： Amar K. Narisetty , Philipp M. Christophel , Yan Xu
CPC classification number: G06N99/005 , G06F17/11
Abstract: Systems and methods for conflict resolution and stabilizing cut generation in a mixed integer linear program (MILP) solver are disclosed. One disclosed method includes receiving a mixed integer linear problem (MILP), the MILP having a root node and one or more global bounds; pre-processing the MILP, the MILP being associated with nodes; establishing a first threshold for a learning phase branch-and-cut process; performing, by one or more processors, the learning phase branch-and-cut process for nodes associated with the MILP, wherein performing the learning phase branch-and-cut process includes: evaluating the nodes associated with the MILP, collecting conflict information about the MILP, and determining whether the first threshold has been reached; responsive to reaching the first threshold, removing all of the nodes and restoring a root node of the MILP; and solving, with the one or more processors, the MILP using the restored root node and the collected conflict information.
Abstract translation: 公开了一种用于混合整数线性程序（MILP）求解器中的冲突解决和稳定切割生成的系统和方法。 一种公开的方法包括接收混合整数线性问题（MILP），MILP具有根节点和一个或多个全局界限; 预处理MILP，MILP与节点相关联; 建立学习阶段分支和切割过程的第一个门槛; 通过一个或多个处理器执行与所述MILP相关联的节点的学习阶段分支和剪切过程，其中执行所述学习阶段分支和剪切过程包括：评估与所述MILP相关联的节点，收集关于所述MILP的冲突信息 MILP，并确定是否达到了第一阈值; 响应于达到第一阈值，移除所有节点并恢复MILP的根节点; 并使用一个或多个处理器，使用恢复的根节点和收集的冲突信息来解决MILP。
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公开(公告)号：US12282807B1
公开(公告)日：2025-04-22
申请号：US19000691
申请日：2024-12-23
Applicant: SAS Institute Inc.
Inventor： Xindian Long , Liping Cai , Xingqi Du , Steven Eric Krueger , Joshua David Griffin , Yan Xu , Scott Russell Pope , Lawrence Edmund Lewis
Abstract: A system, method, and computer-program product includes receiving, by a controller node, a request to execute a client process associated with a first programming language and a plurality of threads; launching, by the controller node, a plurality of multi-language worker processes based on a number of threads associated with the client process; and instructing, by the controller node, the plurality of multi-language worker processes to execute the plurality of threads associated with the client process.
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公开(公告)号：US12271795B1
公开(公告)日：2025-04-08
申请号：US19000713
申请日：2024-12-24
Applicant: SAS Institute Inc.
Inventor： Xindian Long , Liping Cai , Xingqi Du , Steven Eric Krueger , Joshua David Griffin , Yan Xu , Scott Russell Pope , Lawrence Edmund Lewis
IPC: G06N20/00
Abstract: A system, method, and computer-program product includes selecting, by a controller node, a plurality of hyperparameter search points from a hyperparameter search space; instructing, by the controller node, one or more worker nodes to concurrently train a plurality of machine learning models for a target number of epochs using the plurality of hyperparameter search points; receiving, from the one or more worker nodes, a plurality of performance metrics that measure a performance of the plurality of machine learning models during the target number of epochs; and removing, by the controller node, one or more underperforming hyperparameter search points from the plurality of hyperparameter search points according to a pre-defined performance metric ranking criterion associated with the plurality of performance metrics.
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Patent Agency Ranking
公开(公告)号：US11093833B1
公开(公告)日：2021-08-17
申请号：US17081118
申请日：2020-10-27
Applicant: SAS Institute Inc.
Inventor： Steven Joseph Gardner , Joshua David Griffin , Yan Xu , Patrick Nathan Koch , Brett Alan Wujek , Oleg Borisovich Golovidov
Abstract: Tuned hyperparameter values are determined for training a machine learning model. When a selected hyperparameter configuration does not satisfy a linear constraint, if a projection of the selected hyperparameter configuration is included in a first cache that stores previously computed projections is determined. When the projection is included in the first cache, the projection is extracted from the first cache using the selected hyperparameter configuration, and the selected hyperparameter configuration is replaced with the extracted projection in the plurality of hyperparameter configurations. When the projection is not included in the first cache, a projection computation for the selected hyperparameter configuration is assigned to a session. A computed projection is received from the session for the selected hyperparameter configuration. The computed projection and the selected hyperparameter configuration are stored to the first cache, and the selected hyperparameter configuration is replaced with the computed projection.
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公开(公告)号：US11635988B1
公开(公告)日：2023-04-25
申请号：US17820952
申请日：2022-08-19
Applicant: SAS Institute Inc.
Inventor： Yan Gao , Joshua David Griffin , Yu-Min Lin , Yan Xu , Seyedalireza Yektamaram , Amod Anil Ankulkar , Aishwarya Sharma , Girish Vinayak Kolapkar , Kiran Devidas Bhole , Kushawah Yogender Singh , Jorge Manuel Gomes da Silva
Abstract: A computing device determines an optimal number of threads for a computer task. Execution of a computing task is controlled in a computing environment based on each task configuration included in a plurality of task configurations to determine an execution runtime value for each task configuration. An optimal number of threads value is determined for each set of task configurations having common values for a task parameter value, a dataset indicator, and a hardware indicator. The optimal number of threads value is an extremum value of an execution parameter value as a function of a number of threads value. A dataset parameter value is determined for a dataset. A hardware parameter value is determined as a characteristic of each distinct executing computing device in the computing environment. The optimal number of threads value for each set of task configurations is stored in a performance dataset in association with the common values.
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公开(公告)号：US11062219B1
公开(公告)日：2021-07-13
申请号：US17106488
申请日：2020-11-30
Applicant: SAS Institute Inc.
Inventor： Joshua David Griffin , Riadh Omheni , Yan Xu
Abstract: A computer solves a nonlinear optimization problem. An optimality check is performed for a current solution to an objective function that is a nonlinear equation with constraint functions on decision variables. When the performed optimality check indicates that the current solution is not an optimal solution, a barrier parameter value is updated, and a Lagrange multiplier value is updated for each constraint function based on a result of a complementarity slackness test. The current solution to the objective function is updated using a search direction vector determined by solving a primal-dual linear system that includes a dual variable for each constraint function and a step length value determined for each decision variable and for each dual variable. The operations are repeated until the optimality check indicates that the current solution is the optimal solution or a predefined number of iterations has been performed.
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公开(公告)号：US10956835B2
公开(公告)日：2021-03-23
申请号：US16297952
申请日：2019-03-11
Applicant: SAS Institute Inc.
Inventor： Rui Shi , Guixian Lin , Xiangqian Hu , Yan Xu
Abstract: A computing device compresses a gradient boosting tree predictive model. A gradient boosting tree predictive model is trained using a plurality of observation vectors. Each observation vector includes an explanatory variable value of an explanatory variable and a response variable value for a response variable. The gradient boosting tree predictive type model is trained to predict the response variable value of each observation vector based on a respective explanatory variable value of each observation vector. The trained gradient boosting tree predictive model is compressed using a compression model with a predefined penalty constant value and with a predefined array of coefficients to reduce a number of trees of the trained gradient boosting tree predictive model. The compression model minimizes a sparsity norm loss function. The compressed, trained gradient boosting tree predictive model is output for predicting a new response variable value from a new observation vector.
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公开(公告)号：US09684538B1
公开(公告)日：2017-06-20
申请号：US15341263
申请日：2016-11-02
Applicant: SAS Institute Inc.
Inventor： Jun Liu , Ruiwen Zhang , Yan Xu , Joshua Griffin
CPC classification number: G06F17/16
Abstract: A power method can be enhanced. For example, an electronic communication indicating a job to be performed can be received. A best rank-1 approximation of a matrix associated with the job can be determined using the power method. Each iteration of the power method can include determining a point that lies on a line passing through (i) a first value for a first singular vector from an immediately prior iteration of the power method; and (ii) a second value for the first singular vector from another prior iteration of the power method. Each iteration of the power method can also include determining, by performing the power method using the point, a current value for the first singular vector and a current value for a second singular vector for a current iteration of the power method. The job can then be performed using the best rank-1 approximation of the matrix.
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