We survey and summarize the literature on the computational aspects of neural network
models by presenting a detailed taxonomy of the various models according to their …

Delineating the tremendous growth in this area, the Handbook of Approximation Algorithms
and Metaheuristics covers fundamental, theoretical topics as well as advanced, practical …

Many algorithms for NP-hard optimization problems find solutions that are locally optimal, in
the sense that the solutions cannot be improved by a polynomially computable perturbation …

Neural networks usually work adequately on small problems but can run into trouble when
they are scaled up to problems involving large amounts of input data. Circuit Complexity and …

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Ä®·±µ ²·±¶ Ä µ®¬ ²» ¾·® Ä·±²¬®» ½ ¾ ² µ ²·»±µ ³®» ½ ¾ ³ Å· ² µ±µ ³ Ä· Ä» ¼ ²¬ ½» º ³ …

Each real polynomial r (x1, x2,..., xn) defines an ordered partition (Z,, P,, N₁) of R" where
Z,(resp. P,, N,) is the set of points on which the polynomial is zero (resp. positive, negative) …

We prove a number of complexity results on the computational paradigm of local op- timality.
Our main results are thes Page 1 ON THE COMPLEXITY OF LOCAL SEARCH (Extended …

The problem of simulation of automata by neural networks is investigated. In the case of
discrete networks with polynomially bounded weights, the optimal lower and upper bounds …

We show that the permanent cannot be computed by uniform constantdepth threshold
circuits of size T (n), for any function T such that for all k, T k (n)= o (2n). More generally, we …

In this paper, we study the quantum learnability of constant-depth classical circuits under the
uniform distribution and in the distribution-independent framework of probably …