To mitigate the NLOS effects, an unconstrained nonlinear optimization approach is utilized to process time-of-arrival (TOA) and received signal strength (RSSI) in the location system. Path loss models ...

Time difference of arrival (TDOA) passive localization is a kind of signal source localization method. There are two types of solution methods, the drawing methods and the calculating methods. The ...

Conjugate gradient methods form a class of iterative algorithms that are highly effective for solving large‐scale unconstrained optimisation problems. They achieve efficiency by constructing ...

The package CG+ is a Conjugate Gradient code for solving large-scale, unconstrained, nonlinear optimization problems. CG+ implements three different versions of the Conjugate Gradient method: the ...

This course offers an introduction to mathematical nonlinear optimization with applications in data science. The theoretical foundation and the fundamental algorithms for nonlinear optimization are ...

A new two-level subspace method is proposed for solving the general unconstrained minimization formulations discretized from infinite-dimensional optimization problems. At each iteration, the ...

Optimization Algorithms: Steepest Descent & Newton's Method This repository contains Python implementations of common unconstrained optimization algorithms: Steepest Descent and Newton's Method, along ...

Description: Survey of computational tools for solving constrained and unconstrained nonlinear optimization problems. Emphasis on algorithmic strategies and characteristic structures of nonlinear ...

When solving the general smooth nonlinear and possibly nonconvex optimization problem involving equality and/or inequality constraints, an approximate first-order critical point of accuracy ϵ can be ...