How to Install and Uninstall ceres-solver.i686 Package on Fedora 38

Last updated: October 05,2024

1. Install "ceres-solver.i686" package

In this section, we are going to explain the necessary steps to install ceres-solver.i686 on Fedora 38


    
        
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                sudo dnf update
            
                    
    
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            $
        

        
            
                sudo dnf install
            
                        
                ceres-solver.i686
            
                    
    
    Copied

2. Uninstall "ceres-solver.i686" package

In this section, we are going to explain the necessary steps to uninstall ceres-solver.i686 on Fedora 38:


    
        
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                sudo dnf remove
            
                        
                ceres-solver.i686
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo dnf autoremove
            
                    
    
    Copied

3. Information about the ceres-solver.i686 package on Fedora 38

Last metadata expiration check: 0:27:40 ago on Sun Mar 17 04:59:58 2024.
Available Packages
Name : ceres-solver
Version : 2.1.0
Release : 5.fc38
Architecture : i686
Size : 755 k
Source : ceres-solver-2.1.0-5.fc38.src.rpm
Repository : fedora
Summary : A non-linear least squares minimizer
URL : http://ceres-solver.org/
License : BSD
Description :
: Ceres Solver is an open source C++ library for modeling and solving
: large, complicated optimization problems. It is a feature rich, mature
: and performant library which has been used in production at Google
: since 2010. Notable use of Ceres Solver is for the image alignment in
: Google Maps and for vehicle pose in Google Street View. Ceres Solver
: can solve two kinds of problems.
:
: 1. Non-linear Least Squares problems with bounds constraints.
: 2. General unconstrained optimization problems.
:
: Features include:
:
: - A friendly API: build your objective function one term at a time
: - Automatic and numeric differentiation
: - Robust loss functions
: - Local parameterizations
: - Threaded Jacobian evaluators and linear solvers
: - Trust region solvers with non-monotonic steps (Levenberg-Marquardt and
: Dogleg (Powell & Subspace))
: - Line search solvers (L-BFGS and Nonlinear CG)
: - Dense QR and Cholesky factorization (using Eigen) for small problems
: - Sparse Cholesky factorization (using SuiteSparse) for large sparse problems
: - Specialized solvers for bundle adjustment problems in computer vision
: - Iterative linear solvers for general sparse and bundle adjustment problems
: - Runs on Linux, Windows, Mac OS X, Android, and iOS