# On the rate analysis of inexact augmented Lagrangian schemes for convex optimization problems with misspecified constraints

arXiv: https://arxiv.org/abs/1510.00490 · HTML: https://mlsanity.com/paper/1510.00490/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2015.

## Built on by (most cited first, 1 of 1)

- [Simultaneous Learning and Optimization via Misspecified Saddle Point Problems](https://mlsanity.com/paper/2510.05241/) (2025)

## Abstract (arXiv)

We consider a misspecified optimization problem that requires minimizing of a convex function f(x;θ^*) in x over a constraint set represented by h(x;θ^*)≤ 0, where θ^* is an unknown (or misspecified) vector of parameters. Suppose θ^* can be learnt by a distinct process that generates a sequence of estimators θk, each of which is an increasingly accurate approximation of θ^*. We develop a first-order augmented Lagrangian scheme for computing an optimal solution x^* while simultaneously learning θ^*.