The proximal Galerkin finite element method is a high-order, low iteration complexity, nonlinear numerical method that preserves the geometric and algebraic structure of bound constraints in infinite-dimensional function spaces. This paper introduces the proximal Galerkin method and applies it to solve free boundary problems, enforce discrete maximum principles, and develop scalable, mesh-independent algorithms for optimal design. The paper leads to a derivation of the latent variable proximal point (LVPP) algorithm: an unconditionally stable alternative to the interior point method. LVPP is an infinite-dimensional optimization algorithm that may be viewed as having an adaptive barrier function that is updated with a new informative prior at each (outer loop) optimization iteration. One of the main benefits of this algorithm is witnessed when analyzing the classical obstacle problem. Therein, we find that the original variational inequality can be replaced by a sequence of semilinear partial differential equations (PDEs) that are readily discretized and solved with, e.g., high-order finite elements. Throughout this work, we arrive at several unexpected contributions that may be of independent interest. These include (1) a semilinear PDE we refer to as the entropic Poisson equation; (2) an algebraic/geometric connection between high-order positivity-preserving discretizations and certain infinite-dimensional Lie groups; and (3) a gradient-based, bound-preserving algorithm for two-field density-based topology optimization. The complete latent variable proximal Galerkin methodology combines ideas from nonlinear programming, functional analysis, tropical algebra, and differential geometry and can potentially lead to new synergies among these areas as well as within variational and numerical analysis.

翻译：近端伽辽金有限元方法是一种高阶、低迭代复杂度的非线性数值方法，能够保持无穷维函数空间中界约束的几何与代数结构。本文介绍了近端伽辽金方法，并将其应用于求解自由边界问题、强制执行离散极值原理，以及开发适用于最优设计的可扩展、网格无关算法。本文推导了隐变量近端点（LVPP）算法：一种无条件稳定的内点法替代方案。LVPP是一种无穷维优化算法，可视为具有自适应障碍函数，该函数在每次（外循环）优化迭代时通过新的信息先验进行更新。该算法的主要优势之一体现在分析经典障碍问题时。在此过程中，我们发现原始变分不等式可用一系列半线性偏微分方程（PDE）替代，这些方程易于离散化并可通过高阶有限元等方法求解。本文还取得了若干可能具有独立意义的意外成果，包括：（1）一种称为熵泊松方程的半线性PDE；（2）高阶保正离散与特定无穷维李群之间的代数/几何联系；（3）一种基于梯度的保界算法，用于双场密度型拓扑优化。完整的隐变量近端伽辽金方法论融合了非线性规划、泛函分析、热带代数与微分几何的思想，有望在这些领域之间以及变分分析与数值分析中产生新的协同效应。