This work proposes a human-in-the-loop workflow for interactive theorem proving and discovery with LLMs, which retains control over problem formulation and assumptions, while the model searches for proofs or contradictions, proposes candidate properties and theorems, and helps construct structures and parameters that s...
Chen-Yi Li, Zhi-Jian Lai, Dong An et al.· Journal of Scientific Comput...· 4 citations
A unified and comprehensive perspective on constructing augmented Lagrangian functions (based on the Hestenes–Powell–Rockafellar augmented Lagrangian) for various optimization problems, including nonlinear programming and convex and nonconvex composite programming.
This work proposes a nonlinear-residual linearized augmented Lagrangian method (NR-LALM) that replaces this subproblem by a regularized Gauss-Newton-type step while retaining the classical multiplier update based on the nonlinear constraint residual.
Ben-Qi Liu, Kangkang Deng, Zichen Wang et al.· 0 citations
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