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Mathematical libraries for solving least-squares and nonlinear optimization problems.
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SciPy is a scientific computing library for Python that provides a comprehensive collection of mathematical algorithms and numerical tools for research and engineering. It functions as a high-performance numerical analysis framework, bridging high-level Python code with compiled C and Fortran routines to execute complex computations at hardware speeds. The library is built upon array-based data structures that utilize strided memory layouts to enable efficient data manipulation and slicing. By employing vectorized operation dispatch and linking to optimized hardware-specific linear algebra li
Python-based optimization functions for least-squares problems.
dlib is a C++ machine learning toolkit and data analysis framework. It provides a collection of algorithms and utilities for building predictive modeling applications and performing statistical analysis on large datasets within native C++ environments. The project functions as a binding library that wraps low-level C++ machine learning algorithms into high-level Python scripting interfaces. This allows for the integration of high-performance native implementations with Python for machine learning development. The framework covers the implementation of predictive models, the execution of mach
Toolkit providing functions for solving least-squares problems.
Ceres Solver este o bibliotecă C++ pentru optimizare numerică, specializată în probleme de cele mai mici pătrate neliniare și optimizare neconstrânsă. Servește ca un framework pentru diferențiere automată și potrivire robustă a curbelor, oferind instrumente pentru a rezolva modele matematice la scară largă. Biblioteca se distinge prin capabilitățile sale de bundle adjustment, care exploatează structurile de matrice rare pentru a rafina punctele scenei 3D și parametrii camerei. Utilizează diferențierea automată cu numere duale pentru a calcula derivatele funcțiilor de cost, eliminând nevoia de derivare manuală a Jacobianului. Proiectul acoperă o gamă largă de capabilități de optimizare, inclusiv constrângeri de varietate pentru spații non-euclidiene, funcții de pierdere robuste pentru a atenua valorile aberante și rezolvarea sistemelor liniare dense și rare. Oferă, de asemenea, utilitare pentru conversia reprezentării rotației, interpolarea datelor tabelate și estimarea covarianței parametrilor. Configurațiile de build sunt disponibile pentru ținte Android și iOS pentru a susține optimizarea hardware-ului mobil.
C++ library for large-scale nonlinear optimization problems.
Drake is a robotics simulation framework and control system modeling tool used for designing, simulating, and verifying the dynamics of complex robotic systems. It functions as a multibody dynamics simulator and a mathematical optimization library, providing a suite of algorithms for trajectory optimization and the simulation of articulated robots. The framework is distinguished by its block-diagram system for composing dynamical subsystems and its ability to formulate and solve diverse mathematical programs, including linear, quadratic, and nonconvex nonlinear problems. It supports specializ
Offers a toolkit for formulating and solving linear, quadratic, and nonlinear optimization problems for trajectory planning.
GTSAM is a library of C++ classes that implement smoothing and mapping (SAM) in robotics and vision, using factor graphs and Bayes networks as the underlying computing paradigm rather than sparse matrices.
Library for factor graphs and optimization in robotics and vision.
g2o: A General Framework for Graph Optimization
Framework for optimizing graph-based nonlinear error functions.
Symbolic computation and nonlinear optimization for robotics.
LMfit-py
Python package extending optimization methods for least-squares.