Impacts of Constraints and Constraint Handling Strategies for Multi-Objective Mechanical Design Problems
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This work develops an effective distributed algorithm for the solution of stochastic optimization problems that involve partial coupling among both local constraints and local cost functions. While the collection of networked agents is interested in discov ...
The gasification of highly watered waste residues may help to reduce fossil fuels consumption, as well as emissions, all the while improving efficiency. The integration of supercritical gasification with solar energy may improve the performances of both ne ...
This paper investigates reverse auctions that involve continuous values of different types of goods, general nonconvex constraints, and second stage costs. We seek to design the payment rules and conditions under which coalitions of participants cannot inf ...
The discovery of patterns using a minimal set of assumptions constitutes a central challenge in the modeling of polyphonic music and complex streams in general. Skipgrams have been found to be a powerful model for capturing semi-local dependencies in seque ...
The merits of superstructure-free synthesis are demonstrated for bi-objective design of thermal power plants. The design of thermal power plants is complex and thus best solved by optimization. Common optimization methods require specification of a superst ...
The process industries are characterized by a large number of continuously operating plants, for which optimal operation is of economic and ecological importance.
Many industrial systems can be regarded as an arrangement of several subsystems,
where outp ...
We consider online convex optimizations in the bandit setting. The decision maker does not know the time- varying cost functions, or their gradients. At each time step, she observes the value of the cost function for her chosen action. The objective is to ...
This paper presents a coordinated primal-dual interior point (PDIP) method for solving structured convex linear and quadratic programs (LP-QP) in a distributed man- ner. The considered class of problems represents a multi-agent setting, where the aggregate ...
In this work, we consider distributed adaptive learning over multitask mean-square-error (MSE) networks where each agent is interested in estimating its own parameter vector, also called task, and where the tasks at neighboring agents are related according ...
We consider distributed multitask learning problems over a network of agents where each agent is interested in estimating its own parameter vector, also called task, and where the tasks at neighboring agents are related according to a set of linear equalit ...