Pr review quick wins (#143)

* Update diff_engine_core to v0.1.1 (#142)

Includes:
- Fix Windows build (#41)
- Remove Python bindings (moved to DNLP repository) (#40)

Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>

* Address PR cvxpy#3108 review: quick fixes

- Fix indentation in ipopt_nlpif.py cyipopt.Problem() constructor
- Fix typo in problem.py docstring ("value ofro" -> "value of")
- Rename Dnlp2Smooth to DNLP2Smooth for consistency with DGP2DCP/DQCP2DCP
- Fix pnorm ESR condition: p >= 1 (was p > 1)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>

Author: Transurgeon

CLAUDE.md

@@ -1,3 +1,7 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
 # CVXPY Development Guide
 
 ## Quick Reference
@@ -262,7 +266,7 @@ pytest cvxpy/tests/nlp_tests/
 
 **Reduction chain for DNLP:**
 ```
-Problem → CvxAttr2Constr → Dnlp2Smooth → NLPSolver (IPOPT/Knitro)
+Problem → CvxAttr2Constr → DNLP2Smooth → NLPSolver (IPOPT/Knitro)
 ```
 
 **Key components:**

cvxpy/atoms/pnorm.py

@@ -179,7 +179,7 @@ def is_atom_concave(self) -> bool:
     def is_atom_esr(self) -> bool:
         """Is the atom esr?
         """
-        return self.p > 1
+        return self.p >= 1
 
     def is_atom_hsr(self) -> bool:
         """Is the atom hsr?

cvxpy/problems/problem.py

@@ -37,7 +37,7 @@
 from cvxpy.reductions import InverseData
 from cvxpy.reductions.chain import Chain
 from cvxpy.reductions.cvx_attr2constr import CvxAttr2Constr
-from cvxpy.reductions.dnlp2smooth.dnlp2smooth import Dnlp2Smooth
+from cvxpy.reductions.dnlp2smooth.dnlp2smooth import DNLP2Smooth
 from cvxpy.reductions.dqcp2dcp import dqcp2dcp
 from cvxpy.reductions.eval_params import EvalParams
 from cvxpy.reductions.flip_objective import FlipObjective
@@ -1235,7 +1235,7 @@ def _solve(self,
                 reductions = [FlipObjective()]
             else:
                 reductions = []
-            reductions = reductions + [CvxAttr2Constr(reduce_bounds=False), Dnlp2Smooth()]
+            reductions = reductions + [CvxAttr2Constr(reduce_bounds=False), DNLP2Smooth()]
             # instantiate based on user provided solver
             # (default to Ipopt)
             if solver is s.IPOPT or solver is None:
@@ -1625,7 +1625,7 @@ def unpack(self, solution) -> None:
     def unpack_results(self, solution, chain: SolvingChain, inverse_data) -> None:
         """Updates the problem state given the solver results.
 
-        Updates problem.status, problem.value and value ofro
+        Updates problem.status, problem.value and value of
         primal and dual variables.
 
         Arguments

cvxpy/reductions/dnlp2smooth/dnlp2smooth.py

@@ -24,7 +24,7 @@
 from cvxpy.reductions.inverse_data import InverseData
 
 
-class Dnlp2Smooth(Canonicalization):
+class DNLP2Smooth(Canonicalization):
     """
     Reduce a disciplined nonlinear program to an equivalent smooth program
 

cvxpy/reductions/solvers/nlp_solvers/ipopt_nlpif.py

@@ -154,13 +154,13 @@ def solve_via_data(self, data, warm_start: bool, verbose: bool, solver_opts, sol
                           verbose=verbose, use_hessian=use_hessian)
 
         nlp = cyipopt.Problem(
-        n=len(data["x0"]),
-        m=len(data["cl"]),
-        problem_obj=oracles,
-        lb=data["lb"],
-        ub=data["ub"],
-        cl=data["cl"],
-        cu=data["cu"],
+            n=len(data["x0"]),
+            m=len(data["cl"]),
+            problem_obj=oracles,
+            lb=data["lb"],
+            ub=data["ub"],
+            cl=data["cl"],
+            cu=data["cu"],
         )
         # Set default IPOPT options, but use solver_opts if provided
         default_options = {

cvxpy/reductions/solvers/nlp_solvers/nlp_solver.py

@@ -275,13 +275,13 @@ def __init__(self, problem):
         problem : cvxpy.Problem
             The CVXPY problem to check derivatives for.
         """
-        from cvxpy.reductions.dnlp2smooth.dnlp2smooth import Dnlp2Smooth
+        from cvxpy.reductions.dnlp2smooth.dnlp2smooth import DNLP2Smooth
         from cvxpy.reductions.solvers.nlp_solvers.diff_engine import C_problem
 
         self.original_problem = problem
 
-        # Apply Dnlp2Smooth to get canonicalized problem
-        canon = Dnlp2Smooth().apply(problem)
+        # Apply DNLP2Smooth to get canonicalized problem
+        canon = DNLP2Smooth().apply(problem)
         self.canonicalized_problem = canon[0]
 
         # Construct the C version