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The optimization model considers a set of slack variables for guaranteeing feasibility.
This chapter investigates strategies for approaching the energy refurbishment of historic buildings, by proposing, beyond a general methodology aimed at guaranteeing feasibility of the design, its application to two cases studies in the context of South Italy, in a typical Mediterranean climate according to the Köppen classification.
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In the context of dynamic optimization, Sequential Quadratic Programming (SQP) methods, which guarantee feasibility of the solution at convergence, or feasible SQP methods, which guarantee feasibility throughout the optimization, have shown to be efficient in the solution of large-scale constrained NLPs.
Load shedding and penalizations are included in the formulation to guarantee feasibility.
PHR requires an acceptance/rejection mechanism along a bidirectional walk to guarantee feasibility.
It is then refined to guarantee feasibility of the parameterized optimization.
A more attractive problem formulation which always guarantees feasibility is based on SINR balancing with extra PU interference leakage constraints.
The constraints originating from the use of a visual servoing controller are incorporated into the trajectory learning phase, to guarantee feasibility of the generated plan for task execution.
While the proposed approach works to guarantee feasibility for the single-constraint case, early sub-optimal convergence is noted for cases with multiple constraints.
The nominal operating point is chosen to avoid high sensitivity to disturbances and to guarantee feasibility when operation and design parameters are uncertain.
The key is then to guarantee feasibility in real-time, which is achieved by the proposed algorithm through a warm-starting technique in combination with robust MPC design.
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