Abstract Convolutional neural networks (CNNs) have shown good performance in many practical applications. However, their high computational and storage requirements make them difficult to deploy on resource-constrained devices. To address this issue, paper, we propose a novel iterative structured pruning algorithm for CNNs based the recursive least squares (RLS) optimization. Our combines inver...

Bove and Capretta have presented a method to deal with partial and general recursive functions in constructive type theory which relies on an inductive characterisation of the domains of the functions. The method separates the logical and the computational aspects of an algorithm, and facilitates the formal verification of the functions being defined. For nested recursive functions, the method ...

We describe an innovative method for proving total correctness of tail recursive programs having a specific structure, namely programs in which an auxiliary tail recursive function is driven by a main nonrecursive function, and only the specification of the main function is provided. The specification of the auxiliary function is obtained almost fully automatically by solving coupled linear rec...

We bring together the theories of duality and dynamic programming. We show that the dual of an additively separable dynamic optimization problem can be recursively decomposed using summaries of past Lagrange multipliers as state variables. Analogous to the Bellman decomposition of the primal problem, we prove equality of values and solution sets for recursive and sequential dual problems. In no...