Factor-revealing linear programs (LPs) and policy-revealing LPs arise in various contexts of algorithm design and analysis. They are commonly used techniques for analyzing the performance of approximation and online algorithms, especially when direct performance evaluation is challenging. The main idea is to characterize the worst-case performance as a family of LPs parameterized by an integer $n \ge 1$, representing the size of the input instance. To obtain the best possible bounds on the target ratio (e.g., approximation or competitive ratios), we often need to determine the optimal objective value (and the corresponding optimal solution) of a family of LPs as $n \to \infty$. One common method, called the Primal-Dual approach, involves examining the constraint structure in the primal and dual programs, then developing feasible analytical solutions to both that achieve equal or nearly equal objective values. Another approach, known as \emph{strongly factor-revealing LPs}, similarly requires careful investigation of the constraint structure in the primal program. In summary, both methods rely on \emph{instance-specific techniques}, which is difficult to generalize from one instance to another. In this paper, we introduce a general variational-calculus approach that enables us to analytically study the optimal value and solution to a family of LPs as their size approaches infinity. The main idea is to first reformulate the LP in the limit, as its size grows infinitely large, as a variational-calculus instance and then apply existing methods, such as the Euler-Lagrange equation and Lagrange multipliers, to solve it. We demonstrate the power of our approach through three case studies of online optimization problems and anticipate broader applications of this method.

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