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The fastest open-source C++17 optimization library for unconstrained nonlinear problems. Header-only, single dependency. Highest reliability, composable function expressions, unconstrained and constrained solvers.
➗ This work presents a thorough analysis of a quadratic optimization model, confirming convexity, coercivity, and the exact global minimum. It compares gradient descent and Newton’s method, highlighting Newton’s superior efficiency when the Hessian is invertible.
This is a repository associated with the chapter book "Towards optimal sampling for learning sparse approximations in high dimensions" by Ben Adcock, Juan M. Cardenas, Nick Dexter and Sebastian Moraga to be published by Springer in late 2021, available at https://arxiv.org/abs/2202.02360
These are my university projects on the discipline "Numerical methods". I implemented various algorithms of numeric calculations such as: interpolation, approximation, integral calculation, solving equations, solving systems of linear algebraic equations (SLAE), minimization of quadratic function
Given a network, equipped with a specific total and strict order, we get the minimal "round trip" traversal length. It always converges, at quadratic cost.