The solver benchmark (GPU_Optimization/cuopt_benchmark, November 2026) showed
that the integer mean-CVaR MILP is an LP in disguise: HiGHS closes it at the
root node, only the tail scenario rows bind, and the cardinality constraint is
the only hard combinatorics. quantcore.optimization.large_scale exploits that:
LP relaxation by tail-row generation (evaluated at the continuous point, its
objective a valid bound), screening to the top names under a cardinality limit,
a boxed round-and-repair MILP, certification against the bound with escalation
to the integer row generation otherwise. Solver choice by size (HiGHS below
16 000 rows, cuOpt above).
Request option `strategy` ('auto' | 'milp' | 'large_scale'; auto from 200
candidates or 32 768 scenarios) in the service, the API schema and both CLIs.
Settings from the benchmark: cuOpt presolve off (its asynchronous GPU heuristic
otherwise holds every MILP for 5-25 s), dual simplex for pure LPs (concurrent
returns PDLP's inexact point on the L40S), 50 ms polling. rowgen takes an
initial working set.
Measured with HiGHS (M4 Max): N=300 S=4096 5.2 s -> 0.7 s, N=300 S=16384
45 s -> 2.9 s, N=750 S=16384 15.6 s -> 7.4 s, K=40 of 750 24.6 s -> 7.3 s,
objectives within the 1e-4 gap. Tests in tests/test_large_scale.py; benchmark
script scripts/experiments/large_scale.py; docs updated.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
The portfolio data, risk and mean-CVaR optimisation service, prepared for
students who run it on their own DGX Spark without root, Docker or Tailscale.
- deploy/apptainer/qc.sh: install, start/stop/status/logs, cli/api/python,
data import/export and Yahoo setup/update. cuOpt and quantcore run as two
Apptainer instances of the user and talk over 127.0.0.1; the API and the
test bench listen on 127.0.0.1 only. Builds quantcore.sif without root, or
falls back to a plain Python image plus a virtualenv when the Spark allows
no unprivileged builds. Detects taken ports and suggests free ones.
- deploy/apptainer/quantcore.def + constraints.txt: the image, with the
library versions of the verified production container pinned.
- cuOpt under Apptainer: NVIDIA's image sets HOME=/opt/cuopt, read-only in a
SIF, so the solver died on its first job; qc.sh gives it a writable home.
- solvers: a cuOpt that disappears during a solve now falls back to HiGHS
instead of answering 500 (requests' ConnectionError escaped the fallback).
- client: idempotent GETs retry a dropped keep-alive connection, which made
`quantcore-api update --wait` fail when a poll met uvicorn's 5 s timeout.
- config: cuOpt defaults to http://127.0.0.1:5000 and `quantcore serve` to
127.0.0.1; the Docker images keep passing their own values.
- docs/SPARK_APPTAINER.md: the students' guide; README, USAGE, ARCHITECTURE
and SPARK_DEPLOYMENT point to it.
Tested on a DGX Spark (GB10, Apptainer 1.5.4, unprivileged user) in both
modes: selftest on every endpoint, optimisation on cuOpt, Yahoo update,
port conflicts; 139 tests pass in the container against the live cuOpt.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
