# Changelog
## [0.6.2] - 2026-04-12
### Added
- **Reorganized benchmarks under `benchmarks/`** with one self-contained
subdirectory per suite: `hs/`, `cutest/`, `electrolyte/`, `grid/` (renamed
from `opf/`), `cho/`, `large_scale/`, `gas/`, and the new `water/`. Each
suite has its own README and per-suite report. The composite report at
`benchmarks/BENCHMARK_REPORT.md` aggregates HS + CUTEst + electrolyte +
grid + CHO + large-scale; the gas and water suites are standalone (AMPL
`.nl` interface, per-problem `.sol` output).
- **`benchmarks/water/`**: 6 water distribution network design NLPs from
MINLPLib (Hazen-Williams head-loss formulation). Solved via the AMPL
interface; ripopt matches the best-known primal bound for `water.nl`
(963.13) where Ipopt converges to a different local minimum (1001.16).
- **`benchmarks/gas/`**: 4 gas pipeline NLPs from PDE-discretized Euler
equations on pipe networks (gaslib11/40, steady/dynamic). Solved via the
AMPL interface.
- **Loqo mu oracle** (`mu_oracle = "loqo"`) enabled by default, matching Ipopt's
`mu_oracle=quality-function` strategy. Uses centrality measure
`xi = min(z*s)/avg_compl` to set centering parameter `sigma`, preventing
premature mu decrease from highly infeasible starting points. On gaslib11_steady:
164→134 iters, NLP restorations 1→0, mode switches 24→8.
- **Sparse Gauss-Newton restoration**: sparse `J*J^T` factorization for GN restoration
when m > 500, removing the dense Bunch-Kaufman bottleneck (6s/step → 0.02s for
gas pipeline NLPs). Sparse LS multiplier estimates for post-restoration `y`
initialization.
- **Ruiz equilibration KKT scaling** matching MUMPS `SimScale` schedule
(KEEP(52)=7 for SYM=2): 1 inf-norm iteration + 3 one-norm iterations. Activated
on demand when backward error is poor. Added `row_abs_sum()` to
`DenseSymmetric`, `SparseSymmetric`, and `KktMatrix`.
- **Pretend-singular fallback** using Ipopt's normwise residual ratio (threshold
1e-5). When iterative refinement cannot reach target accuracy, tries `delta_c`
first (`PerturbForSingularity`), then `delta_w`.
- **Adaptive mu dual infeasibility safeguard**: prevents mu from collapsing to
1e-11 while dual infeasibility remains large. Adds `du_floor` to barrier error
and dual-infeasibility stagnation detection.
- **Structural degeneracy detection**: after 3 consecutive iterations requiring
`delta_w > 0`, skips the unperturbed factorization trial on subsequent iterations.
- **Dense BK `increase_quality()`**: configurable pivot threshold with escalation
0.64 → 0.8 → 0.95 → 1.0.
- **KKT factorization diagnostics** (dim, nnz, wall time) at `print_level ≥ 5`.
Line-search rejection details at `print_level ≥ 7`.
### Changed
- **PretendSingular chain now matches Ipopt's `PDFullSpaceSolver`**:
`solve → refine(fail) → IncreaseQuality → re-solve → perturbation`.
Previously perturbation was applied before `IncreaseQuality`.
- **Iterative refinement**: max steps raised from 3–5 to 5–10 (matching Ipopt
default). Stagnation detection extended to the non-IC path with a relaxed
factor (1−1e−6 vs Ipopt's 1−1e−9).
- **Default sparse threshold** unchanged (n+m ≥ 110) but sparse GN restoration
now activates at m > 500 even when the outer IPM is dense.
- Workspace version bumped — `ripopt` → 0.6.2, `rmumps` → 0.1.1.
- `ref/` directory and `pyomo-ripopt/build/` now ignored in git.
### Fixed
- **Fallback-result regression (`is_strictly_better`)**: when the main IPM
reports `NumericalError` at a feasible iterate with a meaningful objective,
a fallback solver that converges to a **worse** local minimum no longer
silently replaces the main-IPM result. The comparator now requires either
strict objective improvement (with primal feasibility ≤ 1e-4) or that the
current result has no usable objective. This was the root cause of the
`c_api_hs071_basic`, `c_api_hs071_multiplier_extraction`,
`c_api_null_output_params`, `test_hs071_sensitivity_vs_finite_differences`,
and `test_sensitivity_linear_prediction_accuracy` regressions introduced by
the recent Loqo oracle / KKT quality-chain work.
- **Phosphoric-acid electrolyte test** (`electrolyte_05_phosphoric_acid`)
pinned to `mu_oracle_quality_function=false`. The Loqo oracle steers the
solver into a chemically-wrong local minimum (pH ≈ 11.84) of the Gibbs
free-energy surface; the pre-Loqo default converges to the correct basin
(pH ≈ 2.25). Documented in the test comment.
- **All compiler warnings** cleaned up in `src/ipm.rs`, `rmumps/src/frontal.rs`,
and the adversary example suite.
- **13 adversary example files** updated to the current `NlpProblem` trait
signature (removed `_new_x: bool` parameters and `-> bool` return types).
- `tests/large_scale_benchmark.rs` unused import cleaned up.
### Performance
- Fresh benchmark results (2026-04-11 on Apple Mac Mini, aarch64-apple-darwin):
- **HS suite**: ripopt 115/120 (95.8%), Ipopt 116/120 (96.7%) — nearly tied.
14.0× geometric mean speedup on 113 commonly solved, median 15.1×,
ripopt faster on 111/113 (98%).
- **CUTEst suite**: ripopt 553/727 (76.1%), Ipopt 561/727 (77.2%). 8.0× geometric
mean speedup on 513 commonly solved, median 18.8×, ripopt faster on 415/513 (81%).
ripopt-only solves: 40; Ipopt-only solves: 48.
- **Electrolyte thermodynamics**: ripopt 13/13 (100%), Ipopt 12/13, 20.8× geo mean.
- **Grid (AC OPF)**: 4/4 for both, Ipopt faster (0.2× geo mean).
- gas pipeline NLPs: sparse GN restoration reduces per-step cost 300× on m > 500.
### Notes
- The fresh benchmark shows a small shift in both solvers' solve counts vs. the
0.6.1 numbers reported in the prior CHANGELOG. This reflects run-to-run
floating-point sensitivity on borderline problems combined with the quality-chain
changes; the dominant failure modes (NumericalError, LocalInfeasibility) are
unchanged.
## [0.6.1] - 2026-04-05
### Fixed
- **Dual infeasibility stall on exp/log objectives (#7)**: Added z_opt fallback in the convergence check. When the kappa_sigma safeguard corrupts iterative bound multipliers (z_l, z_u), the unscaled convergence gate now accepts z_opt with component-wise scaling as an alternative, provided z_opt complementarity also passes. This prevents the solver from wasting thousands of iterations on problems where z_opt confirms the point is optimal but iterative z is stuck.
### Changed
- Exclude `adversary/`, `.crucible/`, and `research/` directories from crates.io package
## [0.6.0] - 2026-03-28
### Added
- **Julia/JuMP interface (`Ripopt.jl`)**: MathOptInterface wrapper enabling `Model(Ripopt.Optimizer)` with full JuMP support. Implements the incremental MOI interface (`supports_incremental_interface`, `copy_to`, variable bounds, `NLPBlock`, `Silent`, `TimeLimitSec`, `RawOptimizerAttribute`). Works on ARM/AArch64 (Apple Silicon) — all C callbacks are module-level functions, not closures, avoiding the trampoline requirement.
- **Julia example scripts**: `jump_hs071.jl`, `jump_rosenbrock.jl`, `c_wrapper_hs071.jl` in `Ripopt.jl/examples/`
- **Julia tutorial notebook**: `Ripopt.jl/examples/ripopt_jump_tutorial.ipynb` covering 6 examples (Rosenbrock, HS071, options, maximization, Ipopt comparison, economic dispatch)
- **rmumps genuine delayed pivoting**: CB (Bunch-Bunch) pivot search with look-ahead and delayed pivot acceptance
- **rmumps NEMIN amalgamation**: supernode amalgamation with minimum node size threshold (skipped for n ≥ 10000 where overhead exceeds benefit)
- System information output in benchmark scripts
### Fixed
- **`Ripopt.jl` precompilation**: `const libripopt` replaced with mutable global + `__init__()` so `RIPOPT_LIBRARY_PATH` is read at load time; the precompiled image is no longer tied to a specific build path
- **`Ripopt.jl` `copy_to`**: added `MOI.supports_incremental_interface` and `MOI.copy_to` — required for JuMP to transfer the model to the optimizer
- **`Ripopt.jl` option dispatch**: `Integer` check now precedes `Real` check so integer-valued options (e.g. `max_iter=500`) are routed to `AddRipoptIntOption` instead of `AddRipoptNumOption`
- **`Ripopt.jl` ARM `@cfunction`**: inner callback functions lifted to module level and `$` sigil removed; closure-backed cfunctions are not supported on AArch64
- **`Ripopt.jl` `_dummy_eval_h` ordering**: moved before `CreateRipoptProblem` — compile-time `@cfunction` requires the name to exist at lowering time
- **`Ripopt.jl` callback type widening**: `CreateRipoptProblem` now accepts `Union{Base.CFunction, Ptr{Cvoid}}` for all callback arguments
- **rmumps `factor_csc` threshold pivoting**: fixed correctness bug; reduced threshold to 1e-6
- **rmumps Ruiz scaling**: reverted KKT-aware variant back to standard Ruiz scaling (KKT variant caused CUTEst regressions)
- **CUTEst regression recovery**: recovered 552 → 569/727 Optimal after `nlp_lower_bound_inf` threshold changes caused regressions
- Fixed all compiler warnings in ripopt and rmumps
### Changed
- rmumps matching-based scaling and KKT matching ordering (added then reverted — standard defaults are more robust across problem families)
- rmumps NEMIN amalgamation skipped for n ≥ 10000 (amalgamation overhead exceeds benefit for large systems)
- KKT system improvements for medium-scale equality-constrained problems
### Performance
- CUTEst suite: **569/727** Optimal (up from 516/727 at v0.5.0 release, recovered from 552 after regression)
- HS suite: **118/120** Optimal, ripopt surpasses Ipopt (116/120)
- rmumps: genuine delayed pivoting reduces fill-in on indefinite systems
## [0.5.0] - 2026-03-16
### Breaking Changes
- **Removed `SolveStatus::Acceptable`**: problems that previously returned Acceptable now return either `Optimal` or `NumericalError`. This gives honest reporting — a solve either meets full tolerances or it doesn't. HS suite: 113/120 (was 119/120 with Acceptable); CUTEst: 516/727 (was 596/727).
### Added
- **Domain-specific benchmarks** integrated into `make benchmark`:
- Electrolyte thermodynamics (13 problems): ripopt 13/13 (100%), Ipopt 12/13 (92.3%), 23.7x geo mean speedup
- AC Optimal Power Flow (4 problems): ripopt 4/4, Ipopt 4/4
- CHO parameter estimation (1 large-scale NLP, n=21,672, m=21,660): benchmark infrastructure for .nl file problems
- New Makefile targets: `electrolyte-run`, `opf-run`, `cho-run`
- JSON output from all domain benchmarks for unified reporting
- Domain benchmark sections in `benchmark_report.py` and BENCHMARK_REPORT.md
- `cho_benchmark.rs` example: benchmarks ripopt vs Ipopt on the CHO .nl problem
### Changed
- Convergence polishing: sigma in quality function, delayed mode switch, looser z_opt gate
- Conservative IPM retry added to diagnostic-driven fallback cascade
- NLP restoration: alternative sparse solver retry and relaxed timeout
- Removed overfitting heuristics: adaptive damping, backward-error refinement, scaled dual inf, cost-based fallbacks
- Tests require `Optimal` status; known solver limitations marked as `#[ignore]`
- Manuscript, supporting information, and README updated with current benchmark numbers
### Performance
- HS suite: 113/120 Optimal, geometric mean speedup 16.8x (on 111 commonly-solved)
- CUTEst suite: 516/727 Optimal, geometric mean speedup 11.2x (on 487 commonly-solved)
- Electrolyte suite: 13/13 solved, 23.7x geometric mean speedup vs Ipopt
- Recovered CRESC50 and DISCS via alternative sparse solver fallback
- Recovered MGH10LS via full iteration budget for unconstrained conservative retry
## [0.4.0] - 2026-03-15
### Added
- **Mehrotra predictor-corrector** enabled by default (`mehrotra_pc=true`), with Gondzio centrality corrections (`gondzio_mcc_max=3`) for better centering and fewer iterations
- **Dense condensed KKT for tall-narrow problems**: when m >> n and n <= 100, uses an n x n dense solve instead of (n+m) x (n+m) sparse factorization (up to 845x faster on problems like EXPFITC)
- **SuiteSparse AMD ordering** for rmumps: replaced custom O(n^2) AMD with the `amd` crate, fixing 10s+ ordering times on 40K+ dimensional systems
- **Early stall detection** (`early_stall_timeout=10.0`): bails out fast when stuck in early iterations to trigger fallback strategies
- **CLI `--help` flag**: lists all 50+ solver options organized by category with types, defaults, and descriptions
- **Quality function mu oracle** (disabled by default, `mu_oracle_quality_function`): evaluates barrier KKT error for candidate mu values
- **Large-scale benchmark with Ipopt comparison**: both solvers receive the same NlpProblem via Rust trait, up to 100K variables
- **faer** sparse solver as optional feature (default enabled alongside rmumps)
### Changed
- Default sparse direct solver switched back to rmumps (from faer) after fixing AMD ordering — rmumps multifrontal factorization is 5x faster than faer SparseLdl
- Mehrotra centering parameter (sigma) now feeds into cross-iteration mu update via geometric blend
- Sufficient progress check tightened (`refs_red_fact`: 0.9999 -> 0.999) for earlier Free-to-Fixed mode switching
- Wall-time check runs every iteration in early phase (was every 10)
- Skip expensive NLP restoration when approaching early stall timeout
### Fixed
- Compiler warnings: unused assignment (`tried_compl_polish`), dead code (`HsProblemEntry`)
- Missing fields in `HsSolveResult` for ipopt_native benchmark macro
- Sparse condensed Schur complement with near-full bandwidth (> n/2) now falls back to augmented KKT instead of attempting dense-equivalent sparse factorization
### Performance
- SparseQP 100K: 25.4s -> 4.9s (SuiteSparse AMD + rmumps vs faer)
- EXPFITC (n=5, m=502): 10.1s -> 0.012s (dense condensed path)
- OET3 (n=4, m=1002): 1.4s -> 0.009s (dense condensed path)
- cho_parmest (n=21672, m=21660): first factorization 10.9s -> 0.066s (faer AMD)
- ACOPR14: 60s timeout -> 0.4s (early stall detection + fallback)
- CUTEst geometric mean speedup vs Ipopt: 7.8x -> 9.1x
- HS suite geometric mean speedup vs Ipopt: 15.0x -> 16.5x
## [0.3.0] - 2026-02-14
Initial public release with full IPM implementation, CUTEst/HS benchmarks, C API, AMPL interface, and Pyomo integration.