Most optimization algorithms are based on quadratic approximations to nonlinear functions. You should try to avoid the use of funcions that cannot be properly approximated by quadratic functions. Try ...

1. Introduction Discrete optimization problems have ubiquitous applications in various fields and, in particular, many NP-hard combinatorial optimization problems can be mapped to a quadratic Ising ...

The standard quadratic optimization problem (StQP) refers to the problem of minimizing a quadratic form over the standard simplex. Such a problem arises from numerous applications and is known to be ...

This paper is devoted to studying a type of inverse second-order cone quadratic programming problems, in which the parameters in both the objective function and the constraint set of a given ...

V6 is three to five times faster than V5, and can solve problems as large as n = 502 (502 qubits). Conclusions and future work This paper reports on the first experiments we know of to compare ...

A new gradient-based neural network is constructed on the basis of the duality theory, optimization theory, convex analysis theory, Lyapunov stability theory, and LaSalle invariance principle to solve ...