A well known theorem [3] in the theory of ordinary convex functions states that: A necessary and sufficient condition in order that the function be convex is that there is at least one line of support ...

However, when specialized to quadratic function, conjugate gradient is optimal in a strong sense among function-gradient methods. Therefore, there is seemingly a gap in the menu of available ...

This example is based on Dynamical System Modeling Using Neural ODE and applies convex constraints on both the neural network underpinning the dynamics of the physical system, as well as the ODE ...

The Convex-Function(x) staking platform allows users to trustlessly stake positions on the Function(x) Gauge system while borrowing Convex's boosting power via veFXN. The Convex system creates unique ...

For example, convex hull algorithms can help to solve linear programming problems by finding the vertices of the feasible region, which are potential optimal solutions.

We introduce a property of Banach spaces, called uniform convex-transitivity, which falls between almost transitivity and convex transitivity. We will provide examples of uniformly convex-transitive ...

Matrix inequalities and convex functions constitute a central theme in modern mathematical analysis, with far‐reaching implications across numerical analysis, optimisation, quantum information ...