Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from http://reference.wolfram.com/language/ref/InterpolationOrder.html below:

InterpolationOrder—Wolfram Language Documentation

We deliver solutions for the AI era—combining symbolic computation, data-driven insights and deep technology expertise.

• Data & Computational Intelligence
• Model-Based Design
• Algorithm Development
• Wolfram|Alpha for Business
• Blockchain Technology
• Education Technology
• Quantum Computation

WolframConsulting.com

• See Also
  • Interpolation
  • FunctionInterpolation
  • InterpolatingFunction
  • ListLinePlot
• Related Guides
  • Plotting Options
  • System Model Simulation

• InterpolationOrder->n specifies that polynomials of degree n should be fitted between data points.
• For multidimensional data, the polynomials are taken to be of degree n in each variable.
• InterpolationOrder->None specifies that data points in plots should be joined without interpolation.
• InterpolationOrder->0 yields a collection of flat regions, with steps at each data point.
• InterpolationOrder->1 joins data points with straight lines in 2D, and with piecewise polygonal surface elements in 3D.
• Higher interpolation orders generally lead to increasingly smooth curves or surfaces.
• In functions such as NDSolve, InterpolationOrder->All specifies that the interpolation order should be chosen to be the same as the order of the underlying solution method.
• InterpolationOrder can also be used in functions like Manipulate, to specify the smoothness of animations between control points such as bookmarks.

Use different interpolation orders for curves:

Use different interpolation orders for surfaces:

Use different interpolation orders when constructing an InterpolatingFunction:

Use piecewise quintic interpolation to approximate the sine function:

Show the approximation error:

Show the smoothing effect of higher interpolation order in plotting:

Show the smoothing effect of higher interpolation order for GCD data:

Get a solution that uses interpolation of the same order as the method from NDSolve:

This is more time consuming than the default interpolation order used:

It is much better in between steps:

Very high-order interpolation can lead to large errors:

Interpolate with order 20:

Piecewise interpolation with lower order makes a much better approximation:

Show the approximation error for different interpolation orders:

Zero-order interpolation, with Voronoi cells having the constant value:

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4