Correction to: Preface

Foundations of Computational Mathematics -     

Learning to prove: from examples to general statements

In this paper we describe a method for teaching students to prove some mathematical statements independently, by using specially designed auxiliary assignments. The assignments are designed as homework problems and can be adapted for online learning. We illustrate our method using examples from calculus and differential equations.     

Corrigendum to: Solitary Subgroups

An error in the proof of Theorem 6 in Kaplan and Levy's article of 2009 is pointed out, and a reference to a correct proof is given.     

Reply to Nathan: How to reconstruct the causal argument

Nicholas Nathan tries to resist the current version of the causal argument for sense-data in two ways. First he suggests that, on what he considers to be the correct re-construction of the argument, it equivocates on the sense of proximate cause. Second he defends a form of disjunctivism, by claiming that there might be an extra mechanism involved in producing veridical hallucination, that is not present in perception. I argue that Nathan’s reconstruction of the argument is not the appropriate one, and that, properly interpreted, the argument does not equivocate on proximate cause. Furthermore, I claim that his postulation of a modified mechanism for hallucinations is implausibly ad hoc.     

Correction to: Using Semi-Markov Chains to Solve Semi-Markov Processes

Methodology and Computing in Applied Probability - The original version of this article contained mistakes, and the author would like to correct them.     

Sampling to provide or to bound: With applications to fully dynamic graph algorithms

In dynamic graph algorithms the following provide-or-bound problem has to be solved quickly: Given a set S containing a subset R and a way of generating random elements from S testing for membership in R, either (i) provide an element of R, or (ii) give a (small) upper bound on the size of R that holds with high probability. We give an optimal algorithm for this problem. This algorithm improves the time per operation for various dynamic graph algorithms by a factor of O(log n). For example, it improves the time per update for fully dynamic connectivity from O(log³n) to O(log²n). © 1997 John Wiley & Sons, Inc. Random Struct. Alg., 11 , 369–379 (1997)