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Release Notes

This document contains detailed release notes for each version of LinAlgKit.

v0.2.1 - Performance Optimizations (2025-12-24)

šŸš€ Performance Improvements

This release introduces significant performance optimizations using Numba JIT compilation.

Benchmark Results (5M elements)

Function Standard Fast (JIT) Speedup
mse_loss 33.75ms 2.82ms 12.0x
mae_loss 34.86ms 2.67ms 13.1x
leaky_relu 30.16ms 6.81ms 4.4x
gelu 200.78ms 76.44ms 2.6x
tanh 37.33ms 15.29ms 2.4x
swish 100.27ms 54.62ms 1.8x
elu 65.14ms 40.06ms 1.6x
sigmoid 80.98ms 54.71ms 1.5x
softplus 108.80ms 70.88ms 1.5x

New Features

fast Module

A new high-performance module with Numba JIT-compiled functions:

from LinAlgKit import fast

# 12x faster loss computation
loss = fast.fast_mse_loss(predictions, targets)

# 4.4x faster activation
output = fast.fast_leaky_relu(x, alpha=0.01)

Available fast functions: - fast_sigmoid, fast_relu, fast_leaky_relu, fast_elu - fast_gelu, fast_swish, fast_tanh, fast_softplus - fast_mse_loss, fast_mae_loss, fast_normalize

In-Place Operations

New in-place methods that avoid memory allocation:

A.add_(B)      # A += B in-place
A.sub_(B)      # A -= B in-place
A.mul_(2.0)    # A *= 2.0 in-place
A.hadamard_(B) # Element-wise multiply in-place

Zero-Copy Access

  • .T property - Transpose view (no copy)
  • to_numpy_view() - Direct array access (no copy)
  • transpose(copy=False) - Optional zero-copy transpose

Dependencies

  • Optional: numba>=0.57.0 (for JIT acceleration)

v0.2.0 - Deep Learning Expansion (2025-12-24)

šŸ§  Deep Learning Functions

Major expansion with 50+ mathematical functions for deep learning.

Activation Functions

  • sigmoid, relu, leaky_relu, elu, gelu, swish
  • softplus, tanh, softmax, log_softmax
  • Derivative functions for backpropagation

Loss Functions

  • mse_loss - Mean Squared Error
  • mae_loss - Mean Absolute Error
  • huber_loss - Robust loss
  • cross_entropy_loss - Multi-class classification
  • binary_cross_entropy - Binary classification

Normalization

  • batch_norm - Batch normalization
  • layer_norm - Layer normalization (transformers)
  • instance_norm - Instance normalization (style transfer)

Convolution Operations

  • conv2d - 2D convolution
  • max_pool2d, avg_pool2d - Pooling
  • global_avg_pool2d - Global average pooling

Weight Initialization

  • xavier_uniform, xavier_normal - For tanh/sigmoid
  • he_uniform, he_normal - For ReLU networks

Utility Functions

  • dropout, one_hot, clip, flatten, reshape
  • normalize, cosine_similarity, euclidean_distance
  • pairwise_distances, numerical_gradient

Matrix Decompositions

  • lu() - LU decomposition with partial pivoting
  • qr() - QR decomposition
  • cholesky() - Cholesky decomposition
  • svd() - Singular Value Decomposition

Eigenvalue Methods

  • eig() - Eigenvalues and eigenvectors
  • eigvals() - Eigenvalues only
  • eigh() - Symmetric eigenvalue decomposition

Linear Solvers

  • solve() - Solve Ax = b
  • inv() - Matrix inverse
  • pinv() - Moore-Penrose pseudoinverse
  • lstsq() - Least-squares solution

Matrix Analysis

  • norm() - Multiple norm types
  • cond() - Condition number
  • rank() - Matrix rank

v0.1.0 - Initial Release (2025-12-24)

Core Features

Matrix Classes

  • Matrix - Double-precision (float64)
  • MatrixF - Single-precision (float32)
  • MatrixI - Integer matrix

Static Constructors

Matrix.identity(3)   # 3x3 identity
Matrix.zeros(2, 3)   # 2x3 zeros
Matrix.ones(4, 4)    # 4x4 ones

Matrix Operations

  • transpose() - Transposition
  • trace() - Sum of diagonal
  • determinant() - LU-based O(nĀ³)
  • determinant_naive() - Recursive (small matrices)

Arithmetic Operators

  • A + B - Element-wise addition
  • A - B - Element-wise subtraction
  • A * B - Matrix multiplication
  • 2 * A - Scalar multiplication

NumPy Interoperability

# From NumPy
A = Matrix.from_numpy(np_array)

# To NumPy
np_array = A.to_numpy()

Element Access

val = A[i, j]    # Get element
A[i, j] = 5.0    # Set element

Upgrade Guide

From v0.1.0 to v0.2.0

No breaking changes. All v0.1.0 code works unchanged.

New imports available:

import LinAlgKit as lk

# New activation functions
output = lk.relu(x)
output = lk.softmax(logits)

# New matrix methods
L = A.cholesky()
eigenvalues, eigenvectors = A.eig()
x = A.solve(b)

From v0.2.0 to v0.2.1

No breaking changes. Install numba for automatic acceleration:

pip install numba

Use fast module for best performance:

from LinAlgKit import fast

# Check if Numba is available
import LinAlgKit as lk
print(lk.HAS_NUMBA)  # True if numba installed

Roadmap

See EXPANSION_PLAN.md for the complete roadmap.

Upcoming Features

v0.3.0 (Q1 2025) - Sparse matrix support (CSR, COO) - Iterative solvers (CG, GMRES) - Matrix functions (expm, logm, sqrtm)

v0.4.0 (Q2 2025) - C++/Rust native core - BLAS/LAPACK integration - GPU acceleration (CUDA)

v1.0.0 (2027) - Production-ready - Multi-backend support - Distributed computing