def grad_f(x): x1, x2 = x grad_x1 = 4*x1 + 2*x2 + 1 grad_x2 = 2*x2 + 2*x1 – 1 return np.array([grad_x1, grad_x2]) This function calculates the gradient vector of the function f(x1,x2) by computing the ...

Biased Gradient Squared Descent (BGSD) is a saddle point finding method I created, implemented, and explored. I implemented BGSD within the Transition State Library for ASE (TSASE), a python package ...

Abstract: This paper is concerned with the problem of finding a quadratic common Lyapunov function for a large family of stable linear systems. We present gradient iteration algorithms which give ...

Any straight line graph has a constant gradient, which is calculated by the change in 𝑦 divided by the change in 𝑥, along any section of the graph. The gradient is measuring the steepness of the ...

Abstract: In the domain of soft tensegrity robot, the self-equilibrium tensegrity structure is vital for the further analysis of robot's locomotion. Furthermore, form-finding is an important step for ...