We consider a space-time variational formulation of a PDE-constrained optimal control problem with box constraints on the control and a parabolic PDE with Robin boundary conditions. In this setting, the optimal control problem reduces to an optimization problem for which we derive necessary and sufficient optimality conditions. We propose to utilize a well-posed inf-sup stable framework of the PDE in appropriate Lebesgue-Bochner spaces. Next, we introduce a conforming simultaneous space-time (tensorproduct) discretization in these Lebesgue-Bochner spaces. Using finite elements in space and piecewise linear functions in time, this setting is known to be equivalent to a Crank-Nicolson time stepping scheme for parabolic problems. The optimization problem is solved by a projected gradient method. We show numerical comparisons for problems in 1d, 2d and 3d in space. It is shown that the classical semi-discrete primal-dual setting is more efficient for small problem sizes and moderate accuracy. However, the simultaneous space-time discretization shows good stability properties and even outperforms the classical approach as the dimension in space and/or the desired accuracy increases.
翻译:我们考虑的是受PDE限制的最佳控制问题的时空变式配方,对控件加以限制,对Robin边界条件进行抛射式PDE 。在这种环境下,最佳控制问题被降为优化问题,因此我们得出必要和充分的最佳条件。我们提议在适当的Lebesgue-Bochner空间使用PDE 的精密的内在稳定框架。接下来,我们在这些Lebesgue-Bochner空间引入一个同步的时空时空分解(超低产品)同步。在空间使用有限的元素和时空线函数,这一设置已知相当于对parbolic问题采用Crank-Nicolson时间阶梯度计划。优化问题由预测的梯度方法解决。我们对1d、2d和3d的空间问题进行了数字比较。我们发现,典型的半分位原始环境对于小问题大小和中度精确度比较更有效。然而,同时使用空间分解时,空间分解时显示良好的稳定性,甚至超越了空间和空间尺寸所期望的典型方法的精确度。