Beginner applied math

A small set of recognizable math problems that show what a matten::Tensor can represent and how short vector/matrix algorithms look in matten. They use only the default numeric Tensor API — no extra features, no external crates, and small hard-coded inputs with stable output.

These examples are teaching examples, not a production algorithm package. They sit in a 30+ filename band so the established 00_–28_ suite stays untouched.

Examples

30_magic_square_checker.rs

Difficulty: Beginner. Checks whether a square matrix is a magic square — every row, column, and both diagonals share one sum. Demonstrates 2-D Tensor::new, shape, and element access with get(&[row, col]). Uses the classic 3×3 Lo Shu square (magic constant 15).

cargo run --example 30_magic_square_checker

Source: 30_magic_square_checker.rs

31_fibonacci_matrix_power.rs

Difficulty: Beginner. Computes Fibonacci numbers from the identity Q^n = [[F(n+1), F(n)], [F(n), F(n-1)]] with Q = [[1, 1], [1, 0]]. Demonstrates repeated Tensor::matmul (recall that * is element-wise, never a matrix product) and reading one element with get. A demonstration of the identity, not a big-integer routine.

cargo run --example 31_fibonacci_matrix_power

Source: 31_fibonacci_matrix_power.rs

32_graph_path_counting.rs

Difficulty: Beginner. Counts walks in a directed graph using the fact that (A^k)[i, j] is the number of walks of length k from node i to node j. Demonstrates representing a graph as an adjacency Tensor and taking matrix powers via matmul. Note the distinction between a walk (may repeat nodes/edges) and a simple path (may not).

cargo run --example 32_graph_path_counting

Source: 32_graph_path_counting.rs

Already covered (cross-references)

Two classic beginner problems already ship as examples, so this band does not add duplicates:

• Vector distance — 54_pairwise_distance.rs (and 25_normalize_vector.rs).
• Cosine similarity — 26_cosine_similarity.rs.

What this is not

These examples do not imply that matten is a graph library, a number-theory package, or an ML framework. They are single-file demonstrations of accepted APIs.