In this article, a generalized optimality criteria method is proposed for topology optimization with arbitrary objective function and multiple inequality constraints. This algorithm uses sensitivity information to update both the Lagrange multipliers design variables. Different from conventional method, does not satisfy constraints at every iteration. Rather, it improves variables such that are...

Sequential optimality conditions provide adequate theoretical tools to justify stopping criteria for nonlinear programming solvers. Here, nonsmooth approximate gradient projection and complementary approximate Karush-Kuhn-Tucker conditions are presented. These sequential optimality conditions are satisfied by local minimizers of optimization problems independently of the fulfillment of constrai...

A recent study of early dinosaur evolution using equal-weights parsimony recovered a scheme of dinosaur interrelationships and classification that differed from historical consensus in a single, but significant, respect; Ornithischia and Saurischia were not recovered as monophyletic sister-taxa, but rather Ornithischia and Theropoda formed a novel clade named Ornithoscelida. However, these anal...

Sridhar Mahadevan Department of Computer Science and Engineering University of South Florida Tampa, Florida 33620 [email protected] Abstract Embedded autonomous agents, such as robots or softbots, are faced with solving sequential decision problems. Reinforcement learning (RL) is a particular approach to sequential decision problems, in which the agent-environment interaction is modeled as ...

We briefly introduce and discuss some criteria for optimal decisions. For the majority of this module, we consider a person’s utility for different options (or ‘rewards’ or ‘actions’) and, with uncertainty about the reward which will follow a specific decision, we aim at maximisation of the expected utility, which is actually the utility of the optimal decision (note that a decision is a gamble...