Regrets of proximal method of multipliers for online non-convex optimization with long term constraints
Regrets of proximal method of multipliers for online non-convex optimization with long term constraints
复制标题
用于具有长期约束的在线非凸优化的乘法器的近端方法的遗憾
DOI:
10.1007/s10898-022-01196-2
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发表时间:
2022-04
影响因子:
1.8
通讯作者:
Xiantao Xiao
中科院分区:
文献类型:
--
作者:
Liwei Zhang;Haoyang Liu;Xiantao Xiao
The online optimization problem with non-convex loss functions over a closed convex set, coupled with a set of inequality (possibly non-convex) constraints is a challenging online learning problem. A proximal method of multipliers with quadratic approximations (named as OPMM) is presented to solve this online non-convex optimization with long term constraints. Regrets of the violation of Karush-Kuhn-Tucker conditions of OPMM for solving online non-convex optimization problems are analyzed. Under mild conditions, it is shown that this algorithm exhibitsLagrangian gradient violation regret,constraint violation regret andcomplementarity residual regret if parameters in the algorithm are properly chosen, whereTdenotes the number of time periods. For the case that the objective is a convex quadratic function, we demonstrate that the regret of the objective reduction can be established even the feasible set is non-convex. For the case when the constraint functions are convex, if the solution of the subproblem in OPMM is obtained by solving its dual, OPMM is proved to be an implementable projection method for solving the online non-convex optimization problem.
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DOI:
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发表时间:
2017-08
期刊:
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影响因子:
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ArXiv
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影响因子:
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期刊:
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DOI:
10.1145/1961189.1961200
发表时间:
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期刊:
ACM Trans. Intell. Syst. Technol.
影响因子:
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通讯作者:
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