# NDWasmBlas

## Namespace

#### NDWasmBlas

Description:

  - NDWasmBlas: BLAS (Basic Linear Algebra Subprograms) Handles O(n^2) and O(n^3) matrix-matrix and matrix-vector operations.

Source:
  - ndwasm_blas.js, line 3

### Methods

#### (static) ger(x, y) → {NDArray}

Description:

  - Vector Outer Product (Rank-1 Update): A = x * y^T. Complexity: O(m * n)

Source:
  - ndwasm_blas.js, line 200

##### Parameters:

Name | Type | Description
--- | --- | ---
`x` | NDArray | Vector of shape [m].
`y` | NDArray | Vector of shape [n].

##### Returns:

Result matrix of shape [m, n].

Type
  NDArray

#### (static) matMul(a, b) → {NDArray}

Description:

  - General Matrix Multiplication (GEMM): C = A * B. Complexity: O(m * n * k)

Source:
  - ndwasm_blas.js, line 56

##### Parameters:

Name | Type | Description
--- | --- | ---
`a` | NDArray | Left matrix of shape [m, n].
`b` | NDArray | Right matrix of shape [n, k].

##### Returns:

Result matrix of shape [m, k].

Type
  NDArray

#### (static) matMulBatch(a, b) → {NDArray}

Description:

  - Batched Matrix Multiplication: C[i] = A[i] * B[i]. Common in deep learning inference. Complexity: O(batch * m * n * k)

Source:
  - ndwasm_blas.js, line 104

##### Parameters:

Name | Type | Description
--- | --- | ---
`a` | NDArray | Batch of matrices of shape [batch, m, n].
`b` | NDArray | Batch of matrices of shape [batch, n, k].

##### Returns:

Result batch of shape [batch, m, k].

Type
  NDArray

#### (static) matPow(a) → {NDArray}

Description:

  - matPow computes A^k (Matrix Power). Matrix Functions (O(n^3))

Source:
  - ndwasm_blas.js, line 81

##### Parameters:

Name | Type | Description
--- | --- | ---
`a` | NDArray | Matrix of shape [n, n].

##### Returns:

Result matrix of shape [n, n].

Type
  NDArray

#### (static) matVecMul(a, x) → {NDArray}

Description:

  - Matrix-Vector Multiplication: y = A * x. Complexity: O(m * n)

Source:
  - ndwasm_blas.js, line 179

##### Parameters:

Name | Type | Description
--- | --- | ---
`a` | NDArray | Matrix of shape [m, n].
`x` | NDArray | Vector of shape [n].

##### Returns:

Result vector of shape [m].

Type
  NDArray

#### (static) syrk(a) → {NDArray}

Description:

  - Symmetric Rank-K Update: C = alpha * A * A^T + beta * C. Used for efficiently computing covariance matrices or Gram matrices. Complexity: O(n^2 * k)

Source:
  - ndwasm_blas.js, line 131

##### Parameters:

Name | Type | Description
--- | --- | ---
`a` | NDArray | Input matrix of shape [n, k].

##### Returns:

Symmetric result matrix of shape [n, n].

Type
  NDArray

#### (static) trace(a) → {number}

Description:

  - Calculates the trace of a 2D square matrix (sum of diagonal elements). Complexity: O(n)

Source:
  - ndwasm_blas.js, line 17

##### Parameters:

Name | Type | Description
--- | --- | ---
`a` | NDArray |

##### Throws:

If the array is not 2D or not a square matrix.

Type
  Error

##### Returns:

The sum of the diagonal elements.

Type
  number

#### (static) trsm(a, b) → {NDArray}

Description:

  - Triangular System Solver: Solves A * X = B for X, where A is a triangular matrix. Complexity: O(m^2 * n)

Source:
  - ndwasm_blas.js, line 150

##### Parameters:

Name | Type | Description
--- | --- | ---
`a` | NDArray | Triangular matrix of shape [m, m].
`b` | NDArray | Right-hand side matrix/vector of shape [m, n].

##### Returns:

Solution matrix X of shape [m, n].

Type
  NDArray