https://arshadhs.github.io/tags/convex-optimisation/Recent content in Convex Optimisation on Arshad SiddiquiHugoen-usThu, 28 May 2026 00:00:00 +0000https://arshadhs.github.io/docs/ai/maths/010-linear-algebra/07-dimensionality-reduction/14-mathematical-preliminaries-for-svm/Thu, 28 May 2026 00:00:00 +0000https://arshadhs.github.io/docs/ai/maths/010-linear-algebra/07-dimensionality-reduction/14-mathematical-preliminaries-for-svm/<h1 id="mathematical-preliminaries-for-svm"> Mathematical Preliminaries for SVM <a class="anchor" href="#mathematical-preliminaries-for-svm">#</a> </h1> <p>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.</p> <blockquote class="book-hint info"> <p><strong>Key takeaway:</strong> 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.</p>