497. Blockwise DC Programming
Invited abstract in session TC-6: Structured nonsmooth optimization -- Part I, stream Nonsmooth and nonconvex optimization.
Tuesday, 14:00-16:00Room: B100/7013
Authors (first author is the speaker)
| 1. | Hoomaan Maskan
|
| Umeå University | |
| 2. | Pouria Fatemi
|
| Technical University of Munich | |
| 3. | Alp Yurtsever
|
| Umeå University | |
| 4. | Suvrit Sra
|
| Massachusetts Institute of Technology |
Abstract
Difference of convex (DC) programs are a class of structured non-convex problems. Due to their general form, many applications find their abstractions through the lens of DC programming. In this work, we target the class of blockwise DC (BDC) functions. Careful formulation reveals the broad applicability of these programs in, for example, training deep neural networks. We propose blockwise DC algorithms (BDCA) to treat these problems under various conditions, such as generalized smoothness and stochastic gradients.
Keywords
- First-order optimization
- Non-smooth optimization
Status: accepted
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