sublinear 0.2.0

/**
 * Optimized matrix operations with memory pooling and SIMD-friendly patterns
 * Target: 50% memory reduction and improved cache locality
 */
// Memory pool for vector allocations
class VectorPool {
    pools = new Map();
    maxPoolSize = 100;
    acquire(size) {
        const pool = this.pools.get(size);
        if (pool && pool.length > 0) {
            return pool.pop();
        }
        return new Array(size);
    }
    release(vector) {
        const size = vector.length;
        vector.fill(0); // Clear for reuse
        let pool = this.pools.get(size);
        if (!pool) {
            pool = [];
            this.pools.set(size, pool);
        }
        if (pool.length < this.maxPoolSize) {
            pool.push(vector);
        }
    }
    clear() {
        this.pools.clear();
    }
    getStats() {
        const poolSizes = {};
        let totalVectors = 0;
        for (const [size, pool] of this.pools) {
            poolSizes[size] = pool.length;
            totalVectors += pool.length;
        }
        return { poolSizes, totalVectors };
    }
}
// Compressed Sparse Row (CSR) format for JavaScript
export class CSRMatrix {
    values;
    colIndices;
    rowPtr;
    rows;
    cols;
    constructor(rows, cols, nnz) {
        this.rows = rows;
        this.cols = cols;
        this.values = new Float64Array(nnz);
        this.colIndices = new Uint32Array(nnz);
        this.rowPtr = new Uint32Array(rows + 1);
    }
    static fromCOO(matrix) {
        const { values, rowIndices, colIndices } = matrix;
        const nnz = values.length;
        const csr = new CSRMatrix(matrix.rows, matrix.cols, nnz);
        // Sort by row, then column
        const triplets = Array.from({ length: nnz }, (_, i) => ({
            row: rowIndices[i],
            col: colIndices[i],
            val: values[i],
            index: i
        }));
        triplets.sort((a, b) => a.row - b.row || a.col - b.col);
        // Build CSR structure
        let currentRow = 0;
        let nnzCount = 0;
        for (const triplet of triplets) {
            // Skip zeros
            if (triplet.val === 0)
                continue;
            // Update row pointers
            while (currentRow < triplet.row) {
                csr.rowPtr[++currentRow] = nnzCount;
            }
            csr.values[nnzCount] = triplet.val;
            csr.colIndices[nnzCount] = triplet.col;
            nnzCount++;
        }
        // Finalize row pointers
        while (currentRow < matrix.rows) {
            csr.rowPtr[++currentRow] = nnzCount;
        }
        return csr;
    }
    // Cache-friendly matrix-vector multiplication with SIMD hints
    multiplyVector(x, result) {
        result.fill(0);
        // Process 4 rows at a time for better cache locality
        const blockSize = 4;
        let rowBlock = 0;
        while (rowBlock < this.rows) {
            const endBlock = Math.min(rowBlock + blockSize, this.rows);
            for (let row = rowBlock; row < endBlock; row++) {
                const start = this.rowPtr[row];
                const end = this.rowPtr[row + 1];
                let sum = 0;
                // Unroll loop for SIMD optimization hints
                let i = start;
                for (; i < end - 3; i += 4) {
                    sum += this.values[i] * x[this.colIndices[i]] +
                        this.values[i + 1] * x[this.colIndices[i + 1]] +
                        this.values[i + 2] * x[this.colIndices[i + 2]] +
                        this.values[i + 3] * x[this.colIndices[i + 3]];
                }
                // Handle remaining elements
                for (; i < end; i++) {
                    sum += this.values[i] * x[this.colIndices[i]];
                }
                result[row] = sum;
            }
            rowBlock = endBlock;
        }
    }
    getEntry(row, col) {
        const start = this.rowPtr[row];
        const end = this.rowPtr[row + 1];
        // Binary search for column
        let left = start;
        let right = end - 1;
        while (left <= right) {
            const mid = Math.floor((left + right) / 2);
            const midCol = this.colIndices[mid];
            if (midCol === col) {
                return this.values[mid];
            }
            else if (midCol < col) {
                left = mid + 1;
            }
            else {
                right = mid - 1;
            }
        }
        return 0;
    }
    // Memory-efficient row iteration
    *rowEntries(row) {
        const start = this.rowPtr[row];
        const end = this.rowPtr[row + 1];
        for (let i = start; i < end; i++) {
            yield { col: this.colIndices[i], val: this.values[i] };
        }
    }
    getMemoryUsage() {
        return this.values.byteLength +
            this.colIndices.byteLength +
            this.rowPtr.byteLength;
    }
    getNnz() {
        return this.values.length;
    }
    getRows() {
        return this.rows;
    }
    getCols() {
        return this.cols;
    }
}
// Compressed Sparse Column (CSC) format for column-wise operations
export class CSCMatrix {
    values;
    rowIndices;
    colPtr;
    rows;
    cols;
    constructor(rows, cols, nnz) {
        this.rows = rows;
        this.cols = cols;
        this.values = new Float64Array(nnz);
        this.rowIndices = new Uint32Array(nnz);
        this.colPtr = new Uint32Array(cols + 1);
    }
    static fromCSR(csr) {
        const nnz = csr.getNnz();
        const csc = new CSCMatrix(csr.getRows(), csr.getCols(), nnz);
        // Convert CSR to triplets, then sort by column
        const triplets = [];
        for (let row = 0; row < csr.getRows(); row++) {
            for (const entry of csr.rowEntries(row)) {
                triplets.push({ row, col: entry.col, val: entry.val });
            }
        }
        triplets.sort((a, b) => a.col - b.col || a.row - b.row);
        // Build CSC structure
        let currentCol = 0;
        let nnzCount = 0;
        for (const triplet of triplets) {
            while (currentCol < triplet.col) {
                csc.colPtr[++currentCol] = nnzCount;
            }
            csc.values[nnzCount] = triplet.val;
            csc.rowIndices[nnzCount] = triplet.row;
            nnzCount++;
        }
        while (currentCol < csc.cols) {
            csc.colPtr[++currentCol] = nnzCount;
        }
        return csc;
    }
    // Column-wise matrix-vector multiplication
    multiplyVector(x, result) {
        result.fill(0);
        for (let col = 0; col < this.cols; col++) {
            const xCol = x[col];
            if (xCol === 0)
                continue;
            const start = this.colPtr[col];
            const end = this.colPtr[col + 1];
            // Vectorized accumulation
            for (let i = start; i < end; i++) {
                result[this.rowIndices[i]] += this.values[i] * xCol;
            }
        }
    }
    getMemoryUsage() {
        return this.values.byteLength +
            this.rowIndices.byteLength +
            this.colPtr.byteLength;
    }
    getNnz() {
        return this.values.length;
    }
    getRows() {
        return this.rows;
    }
    getCols() {
        return this.cols;
    }
}
// Memory streaming for large matrices
export class StreamingMatrix {
    chunks = new Map();
    chunkSize;
    rows;
    cols;
    maxCachedChunks;
    constructor(rows, cols, chunkSize = 1000, maxCachedChunks = 10) {
        this.rows = rows;
        this.cols = cols;
        this.chunkSize = chunkSize;
        this.maxCachedChunks = maxCachedChunks;
    }
    static fromMatrix(matrix, chunkSize = 1000) {
        const streaming = new StreamingMatrix(matrix.rows, matrix.cols, chunkSize);
        if (matrix.format === 'coo') {
            const sparse = matrix;
            const chunkData = new Map();
            for (let i = 0; i < sparse.values.length; i++) {
                const row = sparse.rowIndices[i];
                const chunkId = Math.floor(row / chunkSize);
                if (!chunkData.has(chunkId)) {
                    chunkData.set(chunkId, []);
                }
                chunkData.get(chunkId).push({
                    col: sparse.colIndices[i],
                    val: sparse.values[i]
                });
            }
            // Convert each chunk to CSR
            for (const [chunkId, entries] of chunkData) {
                const chunkRows = Math.min(chunkSize, streaming.rows - chunkId * chunkSize);
                const chunkCSR = new CSRMatrix(chunkRows, streaming.cols, entries.length);
                // Build CSR for this chunk
                const rowData = new Map();
                for (const entry of entries) {
                    const localRow = (chunkId * chunkSize) % chunkSize;
                    if (!rowData.has(localRow)) {
                        rowData.set(localRow, []);
                    }
                    rowData.get(localRow).push(entry);
                }
                // Fill CSR arrays
                let nnzCount = 0;
                for (let row = 0; row < chunkRows; row++) {
                    chunkCSR.rowPtr[row] = nnzCount;
                    const rowEntries = rowData.get(row) || [];
                    rowEntries.sort((a, b) => a.col - b.col);
                    for (const entry of rowEntries) {
                        chunkCSR.values[nnzCount] = entry.val;
                        chunkCSR.colIndices[nnzCount] = entry.col;
                        nnzCount++;
                    }
                }
                chunkCSR.rowPtr[chunkRows] = nnzCount;
                streaming.chunks.set(chunkId, chunkCSR);
            }
        }
        return streaming;
    }
    getChunk(chunkId) {
        return this.chunks.get(chunkId) || null;
    }
    // Streaming matrix-vector multiplication
    multiplyVector(x, result) {
        result.fill(0);
        const totalChunks = Math.ceil(this.rows / this.chunkSize);
        for (let chunkId = 0; chunkId < totalChunks; chunkId++) {
            const chunk = this.getChunk(chunkId);
            if (!chunk)
                continue;
            const startRow = chunkId * this.chunkSize;
            const chunkResult = new Array(chunk.getRows()).fill(0);
            chunk.multiplyVector(x, chunkResult);
            // Copy back to result
            for (let i = 0; i < chunkResult.length && startRow + i < this.rows; i++) {
                result[startRow + i] = chunkResult[i];
            }
            // Memory management: remove old chunks if cache is full
            if (this.chunks.size > this.maxCachedChunks) {
                const oldestChunk = Math.max(0, chunkId - this.maxCachedChunks);
                this.chunks.delete(oldestChunk);
            }
        }
    }
    getMemoryUsage() {
        let total = 0;
        for (const chunk of this.chunks.values()) {
            total += chunk.getMemoryUsage();
        }
        return total;
    }
}
// Optimized matrix operations with memory pooling
export class OptimizedMatrixOperations {
    static vectorPool = new VectorPool();
    static getVectorPool() {
        return this.vectorPool;
    }
    // SIMD-optimized vector operations
    static vectorAdd(a, b, result) {
        const n = a.length;
        const out = result || this.vectorPool.acquire(n);
        // Process 4 elements at a time for SIMD
        let i = 0;
        for (; i < n - 3; i += 4) {
            out[i] = a[i] + b[i];
            out[i + 1] = a[i + 1] + b[i + 1];
            out[i + 2] = a[i + 2] + b[i + 2];
            out[i + 3] = a[i + 3] + b[i + 3];
        }
        // Handle remaining elements
        for (; i < n; i++) {
            out[i] = a[i] + b[i];
        }
        return out;
    }
    static vectorScale(vector, scalar, result) {
        const n = vector.length;
        const out = result || this.vectorPool.acquire(n);
        // SIMD-friendly unrolled loop
        let i = 0;
        for (; i < n - 3; i += 4) {
            out[i] = vector[i] * scalar;
            out[i + 1] = vector[i + 1] * scalar;
            out[i + 2] = vector[i + 2] * scalar;
            out[i + 3] = vector[i + 3] * scalar;
        }
        for (; i < n; i++) {
            out[i] = vector[i] * scalar;
        }
        return out;
    }
    static vectorDot(a, b) {
        const n = a.length;
        let sum = 0;
        // Unrolled loop for SIMD optimization
        let i = 0;
        for (; i < n - 3; i += 4) {
            sum += a[i] * b[i] +
                a[i + 1] * b[i + 1] +
                a[i + 2] * b[i + 2] +
                a[i + 3] * b[i + 3];
        }
        for (; i < n; i++) {
            sum += a[i] * b[i];
        }
        return sum;
    }
    static vectorNorm2(vector) {
        return Math.sqrt(this.vectorDot(vector, vector));
    }
    // Memory-efficient matrix format conversion
    static convertToOptimalFormat(matrix) {
        if (matrix.format === 'coo') {
            const sparse = matrix;
            // Choose format based on sparsity pattern and expected access
            const sparsity = sparse.values.length / (matrix.rows * matrix.cols);
            // CSR is generally better for row-wise access and matrix-vector multiplication
            return CSRMatrix.fromCOO(sparse);
        }
        else {
            // Convert dense to sparse first
            const sparse = this.denseToSparse(matrix);
            return CSRMatrix.fromCOO(sparse);
        }
    }
    static denseToSparse(dense, tolerance = 1e-15) {
        const values = [];
        const rowIndices = [];
        const colIndices = [];
        for (let i = 0; i < dense.rows; i++) {
            for (let j = 0; j < dense.cols; j++) {
                const value = dense.data[i][j];
                if (Math.abs(value) > tolerance) {
                    values.push(value);
                    rowIndices.push(i);
                    colIndices.push(j);
                }
            }
        }
        return {
            rows: dense.rows,
            cols: dense.cols,
            values,
            rowIndices,
            colIndices,
            format: 'coo'
        };
    }
    // Memory usage profiling
    static profileMemoryUsage(matrix) {
        const memoryUsed = matrix.getMemoryUsage();
        let nnz;
        let rows;
        let cols;
        if (matrix instanceof CSRMatrix || matrix instanceof CSCMatrix) {
            nnz = matrix.getNnz();
            rows = matrix.getRows();
            cols = matrix.getCols();
        }
        else {
            nnz = 0;
            rows = matrix['rows'];
            cols = matrix['cols'];
        }
        const denseMemory = rows * cols * 8; // 8 bytes per double
        const compressionRatio = denseMemory / memoryUsed;
        return {
            matrixSize: rows * cols,
            nnz,
            memoryUsed,
            compressionRatio
        };
    }
    // Cleanup memory pools
    static cleanup() {
        this.vectorPool.clear();
    }
}