The Quadratic Unconstrained Binary Optimization (QUBO) modeling and solution framework is required for quantum and digital annealers whose goal is the optimization of a well defined metric, the objective function. However , diverse suboptimal solutions may be preferred over harder to implement strict optimal ones. In addition, the decision-maker usually has insightsthat are not always efficiently translated into the optimization model, such as acceptable target , interval or range values. Multi-criteria decision making is an example of involving the user in the decision process .  In this paper, we present two variants of goal-seeking QUBO that minimize the deviation from the goal through a tabu-search based greedy one-flip heuristic . Experimental results illustrate the efficacy of the proposed approach over Constraint Programming for quickly finding a satisficing set of solutions.
The Quadratic Unconstrained Binary Optimization (QUBO) modeling and solution framework is a requirement for quantum and digital annealers . However optimality for QUBO problems of any practical size is extremely difficult to achieve. In order to incorporate the problem-specific insights, a diverse set of solutions meeting an acceptable target metric or goal is the preference in high level decision making. In this paper, we present two alternatives for goal-seeking QUBO for minimizing the deviation from a given target as well as a range of values around a target . Experimental results illustrate the efficacy of the proposed approach over Constraint Programming for quickly finding a satisficing set of solutions.
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required
a requirement
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whose goal is the optimization of a well defined metric, the objective function. However , diverse suboptimal solutions may be preferred over harder to implement strict optimal ones. In addition, the decision-maker usually has insightsthat are not always efficiently translated into the optimization model, such as acceptable target , interval or range values. Multi-criteria decision making is an example of involving the user in the decision process
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However optimality for QUBO problems of any practical size is extremely difficult to achieve. In order to incorporate the problem-specific insights, a diverse set of solutions meeting an acceptable target metric or goal is the preference in high level decision making.
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variants of
alternatives for
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that minimize
for minimizing
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the goal through a tabu-search based greedy one-flip heuristic
a given target as well as a range of values around a target