Primal and dual predicted decrease approximation methods.
- Published in:
- Mathematical Programming, 2018, v. 167, n. 1, p. 37, doi. 10.1007/s10107-017-1108-9
- By:
- Publication type:
- Article
Works matching IS 00255610 AND DT 2018 AND VI 167 AND IP 1
Results: 8
1
2
Consistent learning by composite proximal thresholding.
- Published in:
- Mathematical Programming, 2018, v. 167, n. 1, p. 99, doi. 10.1007/s10107-017-1133-8
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- Publication type:
- Article
3
Max-norm optimization for robust matrix recovery.
- Published in:
- Mathematical Programming, 2018, v. 167, n. 1, p. 5, doi. 10.1007/s10107-017-1159-y
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- Publication type:
- Article
4
Near-optimal stochastic approximation for online principal component estimation.
- Published in:
- Mathematical Programming, 2018, v. 167, n. 1, p. 75, doi. 10.1007/s10107-017-1182-z
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- Publication type:
- Article
5
Interpreting latent variables in factor models via convex optimization.
- Published in:
- Mathematical Programming, 2018, v. 167, n. 1, p. 129, doi. 10.1007/s10107-017-1187-7
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- Publication type:
- Article
6
Data-driven inverse optimization with imperfect information.
- Published in:
- Mathematical Programming, 2018, v. 167, n. 1, p. 191, doi. 10.1007/s10107-017-1216-6
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- Publication type:
- Article
7
Special issue: Optimization models and algorithms for data science.
- Published in:
- Mathematical Programming, 2018, v. 167, n. 1, p. 1, doi. 10.1007/s10107-017-1217-5
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- Publication type:
- Article
8
Wait-and-judge scenario optimization.
- Published in:
- Mathematical Programming, 2018, v. 167, n. 1, p. 155, doi. 10.1007/s10107-016-1056-9
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- Publication type:
- Article