This code defines an optimization problem with a quadratic objective function f(x) represented by a quadratic form 0.5 * x^T A x + b^T x, linear constraints g(x) represented by Cx - d and quadratic ...

The double dogleg optimization technique works well for medium to moderately large optimization problems where the objective function and the gradient are much faster to compute than the Hessian. The ...

This project focuses on formulating and solving a quadratic optimization problem using the Gurobi solver in MATLAB. The aim is to maximize a quadratic objective function subject to specific ...

We consider the NP-hard combinatorial optimization problem of minimizing arbitrary quadratic forms over the {0, 1 } (Boolean) lattice. While polynomial-time approximation algorithms do exist for such ...

One such example is low-rank matrix sensing under restricted isometry properties (RIP). It can be formulated as a minimization problem for a quadratic function on the Riemannian manifold of low-rank ...

In this paper, a modified version of the Classical Lagrange Multiplier method is developed for convex quadratic optimization problems. The method, which is evolved from the first order derivative test ...

Abstract: A Merton's type portfolio optimization problem with quadratic utility function and transaction costs in finite-horizon case is considered in this paper. One case for a particular class of ...

We use exact enumeration to characterize the solutions of quadratic unconstrained binary optimization problems of less than 21 variables in terms of their distributions of Hamming distances to ...