# Quadratic minimization with norm constraint

バージョン 1.3.0.0 (2.84 KB) 作成者:
Minimizes convex or non-convex quadratics subject to (in)equality constraint on norm(x)
ダウンロード: 410

ライセンスの表示

This routine minimizes an arbitrary quadratic function subject to a constraint on the l2-norm of the variables. The problem is of a form commonly encountered as a sub-problem in trust region algorithms, but undoubtedly has other applications as well.

USAGE:

[xmin,Jmin] = trustregprob(Q,b,w)
[xmin,Jmin] = trustregprob(Q,b,w,doEquality)

When doEquality=true (the default), the routine solves,

minimize J(x) = x.'*Q*x/2-dot(b,x) such that ||x|| = w

where ||x|| is the l2-norm of x. The variables returned xmin, Jmin are the minimizing x and its objective function value J(x).

When doEquality=false, the routine solves instead subject to ||x|| <= w .

Q is assumed symmetric, but not necessarily positive semi-definite. In other words, the objective function J(x) is potentially non-convex. Since the solution is based on eigen-decomposition, it is appropriate mainly for Q not too large. If multiple solutions exist, only one solution is returned.

### 引用

Matt J (2024). Quadratic minimization with norm constraint (https://www.mathworks.com/matlabcentral/fileexchange/53191-quadratic-minimization-with-norm-constraint), MATLAB Central File Exchange. 取得済み .

すべてのリリースと互換性あり
##### プラットフォームの互換性
Windows macOS Linux
##### 謝辞

ヒントを得たファイル: Least-square with 2-norm constraint

### Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!
バージョン 公開済み リリース ノート
1.3.0.0

Improved error checking
Edit title
Edit title

1.2.0.0

Fixed a bug that affected the special case b=zeros(N,1)

1.1.0.0

Improved numerical robustness
Fixed a numerical robustness issue

1.0.0.0

Minor polishes to file description
description edit
Minor edits to help text and description