Alternating Direction Method of Multipliers for Machine Learning
Zhouchen Lin, Huan Li, Cong Fang
Informasi Umum
Kode
23.21.1545
Klasifikasi
006.31 - Machine Learning
Jenis
Buku - Elektronik (E-Book)
Subjek
Machine Learning
No. Rak
4
Dilihat
10 kali
Informasi Lainnya
Abstraksi
Machine learning heavily relies on optimization algorithms to solve its learning models. Constrained problems constitute a major type of optimization problem, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solve constrained problems, especially linearly constrained ones. Written by experts in machine learning and optimization, this is the first book providing a state-of-the-art review on ADMM under various scenarios, including deterministic and convex optimization, nonconvex optimization, stochastic optimization, and distributed optimization. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference book for users who are seeking a relatively universal algorithm for constrained problems. Graduate students or researchers can read it to grasp the frontiers of ADMM in machine learning in a short period of time.
Koleksi & Sirkulasi
Tersedia 1 dari total 1 Koleksi
Anda harus log in untuk mengakses flippingbook
Pengarang
| Nama | Zhouchen Lin, Huan Li, Cong Fang |
| Jenis | Perorangan |
| Penyunting | |
| Penerjemah |
Penerbit
| Nama | Springer Singapore |
| Kota | New York |
| Tahun | 2022 |
Sirkulasi
| Harga sewa | IDR 0,00 |
| Denda harian | IDR 0,00 |
| Jenis | Non-Sirkulasi |
