Lossy compression of matrices by black box optimisation of mixed integer nonlinear programming
Abstract In edge computing, suppressing data size is a challenge for machine learning models that perform complex tasks such as autonomous driving, in which computational resources (speed, memory size and power) are limited. Efficient lossy compression of matrix data has been introduced by decomposi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-19763-8 |
| _version_ | 1797994916109877248 |
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| author | Tadashi Kadowaki Mitsuru Ambai |
| author_facet | Tadashi Kadowaki Mitsuru Ambai |
| author_sort | Tadashi Kadowaki |
| collection | DOAJ |
| description | Abstract In edge computing, suppressing data size is a challenge for machine learning models that perform complex tasks such as autonomous driving, in which computational resources (speed, memory size and power) are limited. Efficient lossy compression of matrix data has been introduced by decomposing it into the product of an integer and real matrices. However, its optimisation is difficult as it requires simultaneous optimisation of an integer and real variables. In this paper, we improve this optimisation by utilising recently developed black-box optimisation (BBO) algorithms with an Ising solver for binary variables. In addition, the algorithm can be used to solve mixed-integer programming problems that are linear and non-linear in terms of real and integer variables, respectively. The differences between the choice of Ising solvers (simulated annealing, quantum annealing and simulated quenching) and the strategies of the BBO algorithms (BOCS, FMQA and their variations) are discussed for further development of the BBO techniques. |
| first_indexed | 2024-04-11T09:52:10Z |
| format | Article |
| id | doaj.art-a56e38346dd94edba53ef6568e0bfacb |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T09:52:10Z |
| publishDate | 2022-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-a56e38346dd94edba53ef6568e0bfacb2022-12-22T04:30:45ZengNature PortfolioScientific Reports2045-23222022-09-0112111010.1038/s41598-022-19763-8Lossy compression of matrices by black box optimisation of mixed integer nonlinear programmingTadashi Kadowaki0Mitsuru Ambai1DENSO CORPORATIONDENSO IT LaboratoryAbstract In edge computing, suppressing data size is a challenge for machine learning models that perform complex tasks such as autonomous driving, in which computational resources (speed, memory size and power) are limited. Efficient lossy compression of matrix data has been introduced by decomposing it into the product of an integer and real matrices. However, its optimisation is difficult as it requires simultaneous optimisation of an integer and real variables. In this paper, we improve this optimisation by utilising recently developed black-box optimisation (BBO) algorithms with an Ising solver for binary variables. In addition, the algorithm can be used to solve mixed-integer programming problems that are linear and non-linear in terms of real and integer variables, respectively. The differences between the choice of Ising solvers (simulated annealing, quantum annealing and simulated quenching) and the strategies of the BBO algorithms (BOCS, FMQA and their variations) are discussed for further development of the BBO techniques.https://doi.org/10.1038/s41598-022-19763-8 |
| spellingShingle | Tadashi Kadowaki Mitsuru Ambai Lossy compression of matrices by black box optimisation of mixed integer nonlinear programming Scientific Reports |
| title | Lossy compression of matrices by black box optimisation of mixed integer nonlinear programming |
| title_full | Lossy compression of matrices by black box optimisation of mixed integer nonlinear programming |
| title_fullStr | Lossy compression of matrices by black box optimisation of mixed integer nonlinear programming |
| title_full_unstemmed | Lossy compression of matrices by black box optimisation of mixed integer nonlinear programming |
| title_short | Lossy compression of matrices by black box optimisation of mixed integer nonlinear programming |
| title_sort | lossy compression of matrices by black box optimisation of mixed integer nonlinear programming |
| url | https://doi.org/10.1038/s41598-022-19763-8 |
| work_keys_str_mv | AT tadashikadowaki lossycompressionofmatricesbyblackboxoptimisationofmixedintegernonlinearprogramming AT mitsuruambai lossycompressionofmatricesbyblackboxoptimisationofmixedintegernonlinearprogramming |