Such algorithms represent the state-of-the-art in (large-scale) topology optimization when multiple constraints are present; an important example being the method of moving asymptotes (MMA).
Explicit analyses of power capability with multiple constraints are elaborated, specifically the state-of-energy is considered in power capability assessment.
Even when this information is provided in an annotated format, the problems of ordering, structuring and animating sequences of images, and producing natural language descriptions that correspond to those images within multiple constraints, are each individually difficult tasks.
Solution methods for single chance constraints in optimal power flow problems have been considered in the literature, ensuring single constraints are satisfied with a prescribed probability; however, joint chance constraints, ensuring multiple constraints are simultaneously satisfied, have predominantly been solved via scenario-based approaches or by utilizing Boole's inequality as an upper bound.
Adaptive backstepping control combined with a novel dynamic control allocation scheme is developed for attitude tracking control of a rigid spacecraft, in which multiple constraints are simultaneously considered, such as actuator uncertainties including torque magnitude deviation and misalignment, actuator constraints like saturation, external disturbances and even constrained energy consumption.
Multiple constraints are used to limit both the intra-surface distances between adjacent pixels in each direction (Δ x and Δ z, for the x and z directions, respectively) and the inter-surface distances (δ l and δ u, representing the minimum and maximum distance between surfaces).
To find a feasible path satisfying multiple constraints is NP-complete.
The authors ventured that for larger proteins, multiple constraints were likely to be required in order to obtain accurate results.
Using multiple constraints was then proposed, i.e. equalising more than two levels of one or more variables.
Problems with multiple probabilistic constraints are also discussed.
We construct a model of optimal unemployment insurance where multiple incentive constraints are easily handled.
