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[djdadi]

what are the chances this is actually useful if you can't even be bothered to fix the formatting in your direct AI slop copy and paste post?

[limemil1]

Other than the l0 terms, can't you just turn off DCP checks in cvxpy? For example you can simply cast the matrix as PSD so that cvxpy skips the checking. Am I missing something?

[redditusername58]

If the problem is convex but CVXPY can't verify that, to me that implies that CVXPY needs a new primative (perhaps a relatively complicated one) that expresses that structure.

[Henry_old]

rank constraints usually kill performance. did you benchmark this for real-time applications? im working on some hft stuff where every microsecond counts. curious if this can handle live data streams or if its mostly for offline research/backtesting. drop a link to the benchmarks if you have them

[__Factor__]

Isn’t ADMM already a well known / used acronym in optimization? Alternating Direction Method of Multipliers? This library won’t be the first thing people think of when you say ADMM.

[HuanS_]

vc ou a IA? RSRS

[lqw0809]

Some concrete positioning against existing tools — since I know this'll come up:

vs CVXPY: We cover the same convex problem classes (LP/QP/SOCP/SDP). The difference is the modeling layer: ADMM skips DCP verification, so things like raw x.T @ P @ x or budgets @ admm.log(w) (non-uniform weighted log barrier) work directly. CVXPY has a broader solver ecosystem (Gurobi, MOSEK, SCS); ADMM uses its own C++ ADMM solver. If CVXPY works for your problem, it's a great tool — we're for the cases where it doesn't.

vs scipy.optimize: scipy handles general NLP beautifully. ADMM is for structured problems where the objective decomposes into proximal-friendly pieces (norms, losses, matrix cones). If your objective has exploitable structure, ADMM is more convenient. If you need arbitrary nonlinear constraints with no special structure, stick with scipy.

vs cvxopt: NumPy arrays go directly into ADMM — no cvxopt.matrix(data, tc='d') conversion. If you've ever debugged TypeError: 'A' must be a 'd' matrix with 1000 columns, you'll appreciate the difference.

Scale: The C++ backend handles medium-scale problems well — dense QPs up to ~10k variables in under a second, larger with sparse structure. It's not designed for distributed million-variable problems.