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README.md

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-Edit this `README.md` markdown file to author your organization card.
+<p align="center">
+<a href="https://light.princeton.edu/publication/delta_prox/">
+    <img src="logo.svg" alt="Delta Prox" width="16.5%">
+    </a> &ensp; 
+</p>
+
+
+<p align="center">
+Differentiable Proximal Algorithm Modeling for Large-Scale Optimization
+</p>
+
+<p align="center">
+<a href="https://light.princeton.edu/publication/delta_prox/">Paper</a> |
+<a href="https://deltaprox.readthedocs.io/">Docs</a> |
+<a href="https://github.com/princeton-computational-imaging/Delta-Prox/tree/main/notebooks">Tutorials</a> |
+<a href="https://github.com/princeton-computational-imaging/Delta-Prox/tree/main/examples">Examples</a> 
+</p>
+
+
+> ∇-Prox is a domain-specific language (DSL) and compiler that transforms optimization problems into differentiable proximal solvers. Departing from handwriting these solvers and differentiating via autograd, ∇-Prox requires only a few lines of code to define a solver that can be *specialized based on user requirements w.r.t memory constraints or training budget* by optimized algorithm unrolling, deep equilibrium learning, and deep reinforcement learning. ∇-Prox makes it easier to prototype different learning-based bi-level optimization problems for a diverse range of applications. We compare our framework against existing methods with naive implementations. ∇-Prox is significantly more compact in terms of lines of code and compares favorably in memory consumption in applications across domains.