# The Uno (Unifying Nonlinear Optimization) solver **URL:** https://discourse.julialang.org/t/the-uno-unifying-nonlinear-optimization-solver/115883 **Category:** Optimization (Mathematical) **Tags:** nonlinear-optimizati, nonconvex-optimizati, nonlinear-programmin, sqp **Created:** [June 19, 2024, 8:23pm UTC](https://discourse.julialang.org/t/the-uno-unifying-nonlinear-optimization-solver/115883 "2024-06-19T20:23:45Z") **Posts on this page:** 1 **Showing post:** 21

### Author: ![cvanaret](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/cvanaret/32/11594_2.png) [@cvanaret](https://discourse.julialang.org/u/cvanaret) #### Post date: [July 7, 2025, 1:20pm UTC](https://discourse.julialang.org/t/the-uno-unifying-nonlinear-optimization-solver/115883/21 "2025-07-07T13:20:23Z")

**[Uno v2.0.0](https://github.com/cvanaret/Uno) is out!** Already available in your favorite language: [Uno\_jll](https://github.com/JuliaBinaryWrappers/Uno_jll.jl). The major changes are: - a more powerful unification framework with ingredients such as _Hessian model_ (exact, identity, zero), _regularization strategy_ (primal, primal-dual, none) and _inequality handling method_ (inequality constrained, interior-point). ![wheel](https://global.discourse-cdn.com/julialang/original/3X/3/2/324027556c2159194ad77ffffdefdb4bfec73d50.png) - the null space active-set QP solver [BQPD](https://www.mcs.anl.gov/~leyffer/solvers.html) is now available as [precompiled binaries](https://github.com/leyffer/BQPD_jll.jl/releases) and a binary package [BQPD\_jll](https://juliahub.com/ui/Packages/General/BQPD_jll) 🥳 This means we can now use the `filtersqp` preset (trust-region filter SQP method) within `Uno_jll`: ```julia julia> using JuMP, AmplNLWriter, Uno_jll julia> options = String["preset=filtersqp", "QP_solver=BQPD"]; julia> model = Model(() -> AmplNLWriter.Optimizer(Uno_jll.amplexe, options)); julia> @variable(model, x <= 0.5, start = -2); julia> @variable(model, y, start = 1); julia> @objective(model, Min, 100 * (y - x^2)^2 + (1 - x)^2); julia> @constraint(model, x*y >= 1); julia> @constraint(model, x + y^2 >= 0); julia> optimize!(model) Original model /tmp/jl_gTmcsU/model.nl 2 variables, 2 constraints (0 equality, 2 inequality) Reformulated model /tmp/jl_gTmcsU/model.nl 2 variables, 2 constraints (0 equality, 2 inequality) Used overwritten options: - QP_solver = BQPD - TR_min_radius = 1e-8 - TR_radius = 10 - constraint_relaxation_strategy = feasibility_restoration - filter_type = standard - globalization_mechanism = TR - globalization_strategy = fletcher_filter_method - hessian_model = exact - inequality_handling_method = inequality_constrained - l1_constraint_violation_coefficient = 1. - loose_tolerance = 1e-6 - progress_norm = L1 - protect_actual_reduction_against_roundoff = no - regularization_strategy = none - residual_norm = L2 - switch_to_optimality_requires_linearized_feasibility = yes - tolerance = 1e-6 ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── iter TR iter TR radius phase step norm objective primal feas stationarity complementarity status ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 0 - 1.0000e+01 OPT - 9.0900e+02 4.0000e+00 2.4797e+03 0.0000e+00 initial point 1 1 1.0000e+01 OPT 2.0000e+00 2.6000e+01 1.0000e+00 5.5713e+03 2.8887e+03 ✔ (h-type) 2 1 1.0000e+01 OPT - - - - - infeasible subproblem 2 1 1.0000e+01 FEAS 7.5000e-01 2.5250e+01 1.1250e+00 - - ✘ (restoration) - 2 3.7500e-01 FEAS 3.7500e-01 4.1504e-01 9.5312e-01 3.9528e-01 0.0000e+00 ✔ (restoration) 3 1 7.5000e-01 FEAS 7.5000e-01 3.9312e+01 5.6250e-01 5.0000e-01 0.0000e+00 ✔ (restoration) 4 1 1.5000e+00 FEAS 1.1250e+00 3.0650e+02 0.0000e+00 1.6654e+04 1.4508e+04 ✔ (restoration) 5 1 1.5000e+00 OPT 0.0000e+00 3.0650e+02 0.0000e+00 0.0000e+00 0.0000e+00 0 primal step ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── iter TR iter TR radius phase step norm objective primal feas stationarity complementarity status ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── Uno 2.0.0 (TR Fletcher-filter restoration inequality-constrained method with exact Hessian and no regularization) Mon Jul 7 15:13:07 2025 ──────────────────────────────────────── Optimization status: Success Iterate status: Feasible KKT point Objective value: 306.5 Primal feasibility: 0 ┌ Stationarity residual: 0 └ Complementarity residual: 0 ┌ Feasibility stationarity residual: 0 └ Feasibility complementarity residual: 0 ┌ Infeasibility measure: 0 │ Objective measure: 306.5 └ Auxiliary measure: 0 CPU time: 0.015675s Iterations: 5 Objective evaluations: 3 Constraints evaluations: 7 Objective gradient evaluations: 6 Jacobian evaluations: 6 Hessian evaluations: 6 Number of subproblems solved: 7 ``` cc @cgeoga A comprehensive description of the changes from v1.3.0 can be found [here](https://github.com/cvanaret/Uno/releases/tag/v2.0.0). I’m actively working on the following features: - L-BFGS Hessian approximation; - SLP-EQP method; - a barrier method that better exploits the structure of the original problem; - an exponential barrier method, an alternative to IPOPT’s log barrier method; - Python bindings. If you’re attending the ICCOPT 2025 conference in two weeks, don’t miss [our session on Recent advances in open-source continuous solvers](https://iccopt2025usc.sched.com/event/1dqmu/parallel-sessions-4v-recent-advances-in-open-source-continuous-solvers) with talks about the HiGHS, acados and Uno solvers. --- _[View the full topic](https://discourse.julialang.org/t/the-uno-unifying-nonlinear-optimization-solver/115883)._