tenGP

tenGP is a software library designed for gradient projection optimization. It provides an implementation of the gradient projection algorithm, a method used to solve constrained optimization problems. The core idea behind gradient projection is to iteratively move in the direction of the negative gradient of the objective function, but if a step would violate the constraints, the projection step moves the solution back onto the feasible set. This makes it suitable for problems where the optimal solution is known to lie within a specific, often convex, region.

The library is typically used in machine learning and other areas where optimization with linear or simple convex constraints is required. It aims to offer a straightforward and efficient way to implement this optimization technique without requiring users to build the projection logic from scratch. tenGP might be implemented in various programming languages, often with a focus on performance for numerical computations. Its utility lies in its ability to handle problems that cannot be solved by unconstrained optimization methods directly. The library's documentation would usually detail the types of constraints it supports and provide examples of its application.