paper.md

title:  'Adaptive, tools for adaptive parallel sampling of mathematical functions'
journal: 'PeerJ'
author:
- name: Tinkerer
  affiliation:
    - Kavli Institute of Nanoscience, Delft University of Technology, P.O. Box 4056, 2600 GA Delft, The Netherlands
  email: not_anton@antonakhmerov.org
abstract: |
  Adaptive is an open-source Python library designed to make adaptive parallel function evaluation simple. One supplies a function with its bounds and it will be evaluated at the optimal points in parameter space by analyzing existing data and planning ahead on the fly. With just a few lines of code, you can evaluate functions on a computing cluster, live-plot the data as it returns, and benefit from a significant speedup.
acknowledgements: |
  We'd like to thank ...
contribution: |
  Bla

Introduction

Simulations are costly and often require sampling a region in parameter space.

In the computational sciences, one often does costly simulations---represented by a function

$f$

---where a certain region in parameter space

$X$

is sampled, mapping to a codomain

$Y$

:

$f \colon X \to Y$

. Frequently, the different points in

$X$

can be independently calculated. Even though it is suboptimal, one usually resorts to sampling

$X$

on a homogeneous grid because of its simple implementation.

Choosing new points based on existing data improves the simulation efficiency.

A better alternative which improves the simulation efficiency is to choose new, potentially interesting points in

$X$

based on existing data. [@gramacy2004parameter; @de1995adaptive; @castro2008active; @chen2017intelligent] Baysian optimization works well for high-cost simulations where one needs to find a minimum (or maximum). [@@takhtaganov2018adaptive] If the goal of the simulation is to approximate a contineous function with the least amount of points, the continuity of the approximation is achieved by a greedy algorithm that samples mid-points of intervals with the largest Euclidean distance or curvature[@mathematica_adaptive]. Such a sampling strategy would trivially speedup many simulations. One of the most significant complications here is to parallelize this algorithm, as it requires a lot of bookkeeping and planning ahead.

We describe a class of algorithms relying on local criteria for sampling, which allow for easy parallelization and have a low overhead.

Due to parallelization, the algorithm should be local, meaning that the information updates are only in a region around the newly calculated point. Additionally, the algorithm should also be fast in order to handle many parallel workers that calculate the function and request new points. A simple example is greedily optimizing continuity of the sampling by selecting points according to the distance to the largest gaps in the function values. For a one-dimensional function this is to (1) construct intervals containing neighboring data points, (2) calculate the Euclidean distance of each interval and assign it to the candidate point inside that interval, and finally (3) pick the candidate point with the largest Euclidean distance. In this paper, we describe a class of algorithms that rely on local criteria for sampling, such as in the previous mentioned example. Here we associate a local loss to each of the candidate points within an interval, and choose the points with the largest loss. Using this loss we we can then quantify how well an interpolation of the data is describing the underlying function. The most significant advantage of these algorithms is that they allow for easy parallelization and have a low computational overhead.

We provide a reference implementation, the Adaptive package, and demonstrate its performance.

We provide a reference implementation, the open-source Python package called Adaptive[@Nijholt2019a], which has previously been used in several scientific publications[@vuik2018reproducing; @laeven2019enhanced; @bommer2019spin; @melo2019supercurrent]. It has algorithms for:

$f \colon \R^N \to \R^M$

, where

$N, M \in \mathbb{Z}^+$

but which work best when

$N$

is small; integration in

$\R$

; and the averaging of stochastic functions. Most of our algorithms allow for a customizable loss function. In this way, one can adapt the sampling algorithm to work optimally for a specific function codomain

$Y$

. It easily integrates with the Jupyter notebook environment and provides tools for trivially upscaling a simulation to a computational cluster, live-plotting and inspecting the data as the calculation is in progress, automatically saving and loading of the data, and more.

Review of adaptive sampling

Experiment design uses Bayesian sampling because the computational costs are not a limitation.

Optimal experiment design (OED) is a field of statistics that minimizes the number of experimental runs needed to estimate specific parameters, and thereby, it reduces the costs of experimentation. It works with many degrees of freedom and can consider constraints, for example, when the sample space contains settings that are practically infeasible. One form of OED is response-adaptive design, which concerns adaptive sampling designs for statistical experiments. Here the acquired data (i.e., the observations) are used to adjust the experiment as it is in process. In a typical non-adaptive experiment, decisions on how to sample are made and fixed in advance.

Plotting and low dimensional integration uses local sampling.

PDE solvers and computer graphics use adaptive meshing.

Design constraints and the general algorithm

We aim to sample low dimensional low to intermediate cost functions in parallel.

We propose to use a local loss function as a criterion for choosing the next point.

As an example interpoint distance is a good loss function in one dimension.

In general local loss functions only have a logarithmic overhead.

With many points, due to the loss being local, parallel sampling incurs no additional cost.

Loss function design

A failure mode of such algorithms is sampling only a small neighborhood of one point.

A solution is to regularize the loss such that this would be avoided.

Adding loss functions allows for balancing between multiple priorities.

A desirable property is that eventually, all points should be sampled.

Examples

Line simplification loss

The line simplification loss is based on an inverse Visvalingam’s algorithm.

Inspired by a method commonly employed in digital cartography for coast line simplification, we construct a loss function that does its reverse. [@visvalingam1990douglas]

A parallelizable adaptive integration algorithm based on cquad

The cquad algorithm belongs to a class that is parallelizable.

isosurface sampling

Implementation and benchmarks

The learner abstracts a loss based priority queue.

The runner orchestrates the function evaluation.

Possible extensions

Anisotropic triangulation would improve the algorithm.

Learning stochastic functions is a promising direction.

Experimental control needs to deal with noise, hysteresis, and the cost for changing parameters.