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Efficient QUBO transformation for Higher Degree Pseudo Boolean Functions
Quadratic Unconstrained Binary Optimization (QUBO) is recognized as a unifying framework for modeling a wide range of problems.
Robust Optimization of Unconstrained Binary Quadratic Problems
In this paper we focus on the unconstrained binary quadratic optimization model, maximize x^t Qx, x binary, and consider the problem of identifying optimal solutions that are robust with respect to perturbations in the Q matrix.. We are motivated to find robust, or stable, solutions because of the uncertainty inherent in the big data origins of Q and limitations in computer numerical precision, particularly in a new class of quantum annealing computers.
Logical and Inequality Implications for Reducing the Size and Complexity of Quadratic Unconstrained Binary Optimization Problems
The quadratic unconstrained binary optimization (QUBO) problem arises in diverse optimization applications ranging from Ising spin problems to classical problems in graph theory and binary discrete optimization.
Integrating tabu search and VLSN search to develop enhanced algorithms: A case study using bipartite boolean quadratic programs
The bipartite boolean quadratic programming problem (BBQP) is a generalization of the well studied boolean quadratic programming problem.
