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Optimal Parameter Selection and Acceleration in ADMM for Multi-stage Stochastic Convex Quadratic Programs
Optimal Parameter Selection and Acceleration in ADMM for Multi-stage Stochastic Convex Quadratic Programs
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机译:多阶段随机凸二次程序在ADMM中的最佳参数选择和加速
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摘要
A method solves a stochastic quadratic program (StQP) for a convex set with a set of general linear equalities and inequalities by an alternating direction method of multipliers (ADMM). The method determines an optimal solution, or certifies that no solution exists. The method optimizes a step size β for the ADMM. The method is accelerated using a conjugate gradient (CG) method. The StMPC problem is decomposed into two blocks. The first block corresponds to an equality constrained QP, and the second block corresponds to a projection onto the StMPC inequalities and anticipativity constraints. The StMPC problem can be decomposed into a set of time step problems, and then iterated between the time step problems to solve the decoupled problems until convergence.
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