A new alternating stochastic optimization method for multi-objective problems that lowers computational cost per iteration by block and objective alternation while recovering classical convergence rates under convex, non-convex, and PL conditions.
citation dossier
Beck and L
1Pith papers citing it
1reference links
math.OCtop field · 1 papers
UNVERDICTEDtop verdict bucket · 1 papers
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Pith has found this work in 1 reviewed paper. Its strongest current cluster is math.OC (1 papers). The largest review-status bucket among citing papers is UNVERDICTED (1 papers). For highly cited works, this page shows a dossier first and a bounded explorer second; it never tries to render every citing paper at once.
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math.OC 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Stochastic block coordinate and function alternation for multi-objective optimization and learning
A new alternating stochastic optimization method for multi-objective problems that lowers computational cost per iteration by block and objective alternation while recovering classical convergence rates under convex, non-convex, and PL conditions.