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Model selection and optimization experiment of a field trial with agricultural data using Python

Bengtsson, Lukas LU (2018) In Master's Theses in Mathematical Sciences NUMK01 20172
Mathematics (Faculty of Engineering)
Abstract
Potato cultivation is vast for many agricultures in southern Sweden. What many might not be aware of, is how much pesticides which are used in the cultivation process. In "Late blight prediction and analysis"[1] predictions of late-blight attacks was modelled on potato-corps using Elastic Net among other methods. This thesis is an algorithmic complement describing Alternate Direction Method of Multipliers (ADMM) and its use for efficient optimization of Elastic Net.

Initially a firm foundation is lied introducing the primal- respectively dual-problem and how the relation can be used to define optimization methods. The finale of those is ADMM. Its predecessors are also properly introduced in this thesis.

Then using the same data as... (More)
Potato cultivation is vast for many agricultures in southern Sweden. What many might not be aware of, is how much pesticides which are used in the cultivation process. In "Late blight prediction and analysis"[1] predictions of late-blight attacks was modelled on potato-corps using Elastic Net among other methods. This thesis is an algorithmic complement describing Alternate Direction Method of Multipliers (ADMM) and its use for efficient optimization of Elastic Net.

Initially a firm foundation is lied introducing the primal- respectively dual-problem and how the relation can be used to define optimization methods. The finale of those is ADMM. Its predecessors are also properly introduced in this thesis.

Then using the same data as in prediction and analysis" [1], an experiment looking into parameter effects in both ADMM and Elastic Net is conducted. Some intuitions are confirmed, such as penalty effects and similar. Notably is the robustness of ADMM, despite this it can be more or less effective. Example wise it was found that some control parameters in ADMM and Elastic Net has a firm relations and should be properly chosen.

[1] L. Bengtsson, "Late blight prediction and analysis", 2017. Student Paper. (Less)
Popular Abstract
Potato cultivation is vast for many agricultures in southern Sweden. What many might not be aware of, is how much pesticides which are used in the cultivation process. In "Late blight prediction and analysis"[1] predictions of late-blight attacks was modelled on potato-corps using Elastic Net among other methods. This thesis is an algorithmic complement describing Alternate Direction Method of Multipliers (ADMM) and its use for efficient optimization of Elastic Net.

Initially a firm foundation is lied introducing the primal- respectively dual-problem and how the relation can be used to define optimization methods. The finale of those is ADMM. Its predecessors are also properly introduced in this thesis.

Then using the same data as... (More)
Potato cultivation is vast for many agricultures in southern Sweden. What many might not be aware of, is how much pesticides which are used in the cultivation process. In "Late blight prediction and analysis"[1] predictions of late-blight attacks was modelled on potato-corps using Elastic Net among other methods. This thesis is an algorithmic complement describing Alternate Direction Method of Multipliers (ADMM) and its use for efficient optimization of Elastic Net.

Initially a firm foundation is lied introducing the primal- respectively dual-problem and how the relation can be used to define optimization methods. The finale of those is ADMM. Its predecessors are also properly introduced in this thesis.

Then using the same data as in prediction and analysis" [1], an experiment looking into parameter effects in both ADMM and Elastic Net is conducted. Some intuitions are confirmed, such as penalty effects and similar. Notably is the robustness of ADMM, despite this it can be more or less effective. Example wise it was found that some control parameters in ADMM and Elastic Net has a firm relations and should be properly chosen.

[1] L. Bengtsson, "Late blight prediction and analysis", 2017. Student Paper. (Less)
Please use this url to cite or link to this publication:
author
Bengtsson, Lukas LU
supervisor
organization
course
NUMK01 20172
year
type
M2 - Bachelor Degree
subject
keywords
ADMM, Elastic Net, Model selection, optimization
publication/series
Master's Theses in Mathematical Sciences
report number
LUNFMS-3072-2017
ISSN
1404-6342
other publication id
2017:E47
language
English
id
8938267
date added to LUP
2018-06-07 17:58:19
date last changed
2020-05-29 19:32:11
@misc{8938267,
  abstract     = {{Potato cultivation is vast for many agricultures in southern Sweden. What many might not be aware of, is how much pesticides which are used in the cultivation process. In "Late blight prediction and analysis"[1] predictions of late-blight attacks was modelled on potato-corps using Elastic Net among other methods. This thesis is an algorithmic complement describing Alternate Direction Method of Multipliers (ADMM) and its use for efficient optimization of Elastic Net.

Initially a firm foundation is lied introducing the primal- respectively dual-problem and how the relation can be used to define optimization methods. The finale of those is ADMM. Its predecessors are also properly introduced in this thesis. 

Then using the same data as in prediction and analysis" [1], an experiment looking into parameter effects in both ADMM and Elastic Net is conducted. Some intuitions are confirmed, such as penalty effects and similar. Notably is the robustness of ADMM, despite this it can be more or less effective. Example wise it was found that some control parameters in ADMM and Elastic Net has a firm relations and should be properly chosen.

[1] L. Bengtsson, "Late blight prediction and analysis", 2017. Student Paper.}},
  author       = {{Bengtsson, Lukas}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Model selection and optimization experiment of a field trial with agricultural data using Python}},
  year         = {{2018}},
}