A linear Support Vector Machine finds a hyperplane that separates two classes with the maximum possible margin.
The primal view gives the direct geometric optimisation problem.
The dual view rewrites the problem using Lagrange multipliers and reveals why only support vectors matter.
Key takeaway: Linear SVM maximises the margin by minimising
\( \frac{1}{2}\|w\|^2 \)
subject to correct-classification constraints.
The dual solution expresses
( w )
Machine-learning algorithms expose different kinds of parallel work. The correct decomposition depends on whether independent work occurs across records, features, trees, clusters, kernel entries, or optimisation steps.
Course coverage:
Problem decomposition for parallel machine learning
Ensemble methods and XGBoost-style tree ensembles
Parallel k-means assignment and centroid reduction
Distributed decision trees and random forests
Support vector machine parallelisation using block and MapReduce-style computation
Support Vector Machines use optimisation, geometry and kernels.
Before deriving SVM, we need constrained optimisation, Lagrange multipliers, primal and dual problems, KKT conditions, hyperplanes and kernel functions.
Key takeaway: SVM is built on constrained optimisation.
The hard-margin SVM primal problem is a quadratic optimisation problem with linear inequality constraints.
The dual problem uses Lagrange multipliers and leads naturally to support vectors and kernels.
A linear SVM works well when the data can be separated by a straight line or hyperplane.
When the data is not linearly separable in the original input space, nonlinear SVM maps the data to a higher-dimensional feature space where a linear separator may exist.
Key takeaway: Nonlinear SVM uses the kernel trick.
Instead of explicitly mapping
May 28, 2026