Ceres Solver is a production-grade C++ library for solving large-scale non-linear least squares problems, widely used in computer vision (bundle adjustment, SLAM) and robotics. It provides a flexible API where users define cost functions (analytical, automatic, or numerical differentiation), residual blocks, and parameter manifolds (Euclidean, quaternion, sphere, etc.), then solve via trust-region or line-search methods backed by a variety of sparse/dense linear solvers (Eigen, SuiteSparse, CUDA, Apple Accelerate). The architecture is modular: a core minimizer orchestrates strategy objects (Levenberg-Marquardt, Dogleg, BFGS), while evaluators and Jacobian writers support block-sparse and dynamic sparsity. The library also includes utilities like thread pools, concurrent queues, and graph algorithms for visibility-based preconditioning. Despite a complex build matrix with optional backends, the code is well-structured, heavily tested, and actively maintained.
Uno is a modular C++ solver for nonlinearly constrained optimization (NLP, QP, LP). It implements a unification framework that combines interchangeable algorithmic ingredients—constraint relaxation strategies, inequality handling methods, Hessian models (exact, quasi-Newton, identity, zero), inertia correction, subproblem solvers (QP/LP), and globalization mechanisms (line search, trust region, filter, funnel). The solver supports both sequential quadratic programming (SQP) and interior-point methods, and can be configured via presets that mimic established solvers like filterSQP and IPOPT. The architecture is a library with a main orchestration loop in `Uno.cpp`. The user defines the optimization problem through callback functions (objective, gradient, Jacobian, Hessian) via a C API, which is then wrapped by interfaces for AMPL, Fortran, Julia, and Python. Internally, the solver builds a regularized KKT system, delegates subproblem solves to external libraries (HiGHS, BQPD, HSL MA27/57/86, MUMPS, SPRAL), and applies globalization to ensure convergence. The codebase is heavily templated and uses expression templates for linear algebra, factory patterns for component selection, and runtime dynamic loading for optional dependencies. Key entry points include `uno_optimize()` (C API), `UnoSolverWrapper::optimize()` (Python), and the AMPL executable `uno_ampl`. The solver is used in research and production environments via JuMP.jl, CasADi, pyOptSparse, and OpenMDAO. External dependencies include BLAS/LAPACK, HSL (proprietary), MUMPS, SPRAL, HiGHS, BQPD, and AMPL ASL. The project is actively maintained with extensive CI, cross-platform builds, and a growing ecosystem of language bindings.