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In this presentation, the topic of robust beamforming is studied. We devise the minimum dispersion criterion which extends the minimum variance criterion from l2‐norm to lp‐norm. Formulations with different linear and nonlinear constraints are examined. The proposed framework generalizes existing approaches including the Capon and linearly constrained minimum variance beamformers as well as the method based on worst-case performance optimization. Computationally attractive algorithm realizations are also developed.

We consider an oligopoly dynamic pricing problem where the demand model is unknown and the sellers have different marginal costs. We formulate the problem as a repeated game with incomplete information. We develop a dynamic pricing strategy that leads to a Pareto-efficient and subgame-perfect equilibrium and offers a bounded regret over an infinite horizon, where regret is defined as the expected cumulative profit loss as compared to the ideal scenario with a known demand model.