Stereo-image depth reconstruction with different matching costs and matching algorithms in Python using Numpy and Numba

Automated sum-of-squares (SOS) Prover for Algebraic Inequalities | Python-based tool with GUI & API | Generates readable sum-of-squares proofs for Olympiad-level problems. Built on SymPy.

CaΣoS is a nonlinear sum-of-squares optimization suite. It provides symbolic polynomial expressions, nonconvex optimization algorithms, and interfaces to conic (semidefinite) solvers.

A Julia package for the computation of hard, theoretically guaranteed bounds on the moments of jump-diffusion processes with polynomial data

An open source first-order MATLAB solver for conic programs with row sparsity.

Analysis of Variance (ANOVA)

A sum-of-squares toolbox for stability analysis

MATLAB code with examples of SOS chordal decomposition for polynomial matrix inequalities with sparsity

PRoTECT is an open-source software tool, with a user-friendly GUI, for the purposes of formal verification using polynomial-type barrier certificates for safety specifications. PRoTECT can handle discrete-time stochastic systems, continuous-time stochastic systems, discrete-time deterministic systems and continuous-time deterministic systems.

SOSOpt is a Python library designed for solving sums-of-squares (SOS) optimization problems.

A proof of Fermat's theorem on sum of two squares with mathcom using gaussian integers.

Code for reproducing the experiments in the ICLR 2026 paper "Neural Sum-of-Squares: Certifying the Nonnegativity of Polynomials with Transformers"

Harrison's sum-of-squares decision procedure for nonlinear real arithmetic in Lean 4

Computing approximate optimal designs for multivariate polynomial regressions

Code for the paper "The speed of traveling waves in a FKPP-Burgers system"

Sum of the squares of two consecutive numbers

The code produces Stock price model in a discrete time line and Running sum-of-square returns

Sum of digits web app

Code for the paper "Minimum wave speeds in monostable reaction-diffusion equations: sharp bounds by polynomial optimization"

Unsupervised-ML---K-Means-Clustering-Non-Hierarchical-Clustering-Univ. Use Elbow Graph to find optimum number of clusters (K value) from K values range. The K-means algorithm aims to choose centroids that minimise the inertia, or within-cluster sum-of-squares criterion WCSS. Plot K values range vs WCSS to get Elbow graph for choosing K (no. of c…