nhaliday + acm + local-global   9

Rank aggregation basics: Local Kemeny optimisation | David R. MacIver
This turns our problem from a global search to a local one: Basically we can start from any point in the search space and search locally by swapping adjacent pairs until we hit a minimum. This turns out to be quite easy to do. _We basically run insertion sort_: At step n we have the first n items in a locally Kemeny optimal order. Swap the n+1th item backwards until the majority think its predecessor is < it. This ensures all adjacent pairs are in the majority order, so swapping them would result in a greater than or equal K. This is of course an O(n^2) algorithm. In fact, the problem of merely finding a locally Kemeny optimal solution can be done in O(n log(n)) (for much the same reason as you can sort better than insertion sort). You just take the directed graph of majority votes and find a Hamiltonian Path. The nice thing about the above version of the algorithm is that it gives you a lot of control over where you start your search.
techtariat  liner-notes  papers  tcs  algorithms  machine-learning  acm  optimization  approximation  local-global  orders  graphs  graph-theory  explanation  iteration-recursion  time-complexity  nibble
september 2017 by nhaliday
How to Escape Saddle Points Efficiently – Off the convex path
A core, emerging problem in nonconvex optimization involves the escape of saddle points. While recent research has shown that gradient descent (GD) generically escapes saddle points asymptotically (see Rong Ge’s and Ben Recht’s blog posts), the critical open problem is one of efficiency — is GD able to move past saddle points quickly, or can it be slowed down significantly? How does the rate of escape scale with the ambient dimensionality? In this post, we describe our recent work with Rong Ge, Praneeth Netrapalli and Sham Kakade, that provides the first provable positive answer to the efficiency question, showing that, rather surprisingly, GD augmented with suitable perturbations escapes saddle points efficiently; indeed, in terms of rate and dimension dependence it is almost as if the saddle points aren’t there!
acmtariat  org:bleg  nibble  liner-notes  machine-learning  acm  optimization  gradient-descent  local-global  off-convex  time-complexity  random  perturbation  michael-jordan  iterative-methods  research  learning-theory  math.DS  iteration-recursion
july 2017 by nhaliday
Local max-cut in smoothed polynomial time | I’m a bandit
local = maximal wrt to flipping a single vertex (algorithm is to just flip as long as improvement possible)
tidbits  optimization  algorithms  papers  acm  exposition  acmtariat  sebastien-bubeck  local-global  liner-notes  org:bleg  nibble  average-case  tcs  time-complexity  iteration-recursion  elegance
october 2016 by nhaliday