Matrix Operations Calculator (java, written by Codex)

Codex wrote this java project from the task below. It installed and ran on a clean Ubuntu 22.04 machine as written.

Task given to the agent:

TASK: Matrix Operations Calculator

Write a program that performs common matrix operations including addition, multiplication, transposition, determinant calculation, inversion, and eigenvalue decomposition on matrices loaded from files.

FUNCTIONAL REQUIREMENTS:
- Accept a matrix data file (CSV or JSON format) as a command-line argument
- Support operations selectable via --operation flag: add, subtract, multiply, transpose, determinant, inverse, eigenvalues, rank, trace, and LU decomposition
- For binary operations (add, subtract, multiply), accept a second matrix file via --matrix2 flag
- Support scalar operations: scalar multiplication via --scalar flag applied to the matrix
- Compute matrix properties: dimensions, rank, trace, is-symmetric, is-positive-definite, condition number
- Handle matrices of arbitrary size (up to practical memory limits)
- Support sparse matrix representation for large matrices with many zeros via --sparse flag
- Display results formatted as aligned matrices to console with configurable decimal precision via --precision flag (default: 4)
- Support both integer and floating-point matrix elements
- Verify results where possible: multiplying a matrix by its inverse should yield the identity matrix (within floating-point tolerance)
- Save results as JSON or CSV with --output flag (default: result_matrix.json)
- If no input is given, generate sample matrices and demonstrate each operation: add two 3x3 matrices, multiply a 3x3 by a 3x4, compute determinant and inverse of a 4x4, find eigenvalues of a symmetric 3x3, and show LU decomposition of a 4x4
- Handle errors: dimension mismatches, singular matrices (non-invertible), non-square matrices for operations requiring square input, and malformed input files

Create a complete Java project for a clean Ubuntu 22.04 machine with only JDK 17+ installed. Include:
- Source code
- pom.xml with all dependencies (direct and transitive) pinned to exact versions
- README.md with setup instructions, dependency explanations, build steps, run commands, and expected output

[]

<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
  <modelVersion>4.0.0</modelVersion>
  <groupId>org.tmlr</groupId>
  <artifactId>matrix-operations-calculator</artifactId>
  <version>1.0.0</version>
  <properties>
    <maven.compiler.source>17</maven.compiler.source>
    <maven.compiler.target>17</maven.compiler.target>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
  </properties>
  <dependencies>
    <dependency>
      <groupId>org.apache.commons</groupId>
      <artifactId>commons-math3</artifactId>
      <version>3.6.1</version>
    </dependency>
  </dependencies>
  <build>
    <plugins>
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-assembly-plugin</artifactId>
        <version>3.7.1</version>
        <configuration>
          <archive>
            <manifest>
              <mainClass>MatrixOperationsCalculator</mainClass>
            </manifest>
          </archive>
          <descriptorRefs>
            <descriptorRef>jar-with-dependencies</descriptorRef>
          </descriptorRefs>
        </configuration>
      </plugin>
    </plugins>
  </build>
</project>

# Matrix Operations Calculator (Java)

## Requirements
- Ubuntu 22.04
- JDK 17+
- Maven 3.8+

## Build
```bash
mvn -q -DskipTests package assembly:single
```

## Run
```bash
java -cp target/matrix-operations-calculator-1.0.0-jar-with-dependencies.jar MatrixOperationsCalculator matrix.csv --operation inverse --precision 6
java -cp target/matrix-operations-calculator-1.0.0-jar-with-dependencies.jar MatrixOperationsCalculator matrix.json --operation multiply --matrix2 matrix2.json
```

If no input is passed, sample matrices are generated and demonstrated.

import org.apache.commons.math3.linear.*;

import java.io.IOException;
import java.nio.charset.StandardCharsets;
import java.nio.file.*;
import java.time.Instant;
import java.util.*;

public class MatrixOperationsCalculator {
    private static class Config {
        String matrix1;
        String matrix2;
        String operation = "transpose";
        Double scalar;
        boolean sparse;
        int precision = 4;
        String output = "result_matrix.json";
    }

    public static void main(String[] args) {
        try {
            Config cfg = parseArgs(args);
            if (cfg.matrix1 == null) {
                Map<String, Object> demo = demo();
                Files.writeString(Paths.get(cfg.output), toJson(demo), StandardCharsets.UTF_8);
                System.out.println(toJson(demo));
                return;
            }

            RealMatrix A = readMatrix(Paths.get(cfg.matrix1));
            RealMatrix B = cfg.matrix2 != null ? readMatrix(Paths.get(cfg.matrix2)) : null;

            Map<String, Object> report = new LinkedHashMap<>();
            report.put("generatedAt", Instant.now().toString());
            report.put("operation", cfg.operation);
            report.put("properties", props(A));
            report.putAll(compute(cfg, A, B));

            Object result = report.get("result");
            if (result instanceof List<?> list && !list.isEmpty() && list.get(0) instanceof List<?>) {
                System.out.println(formatMatrix((List<List<Double>>) result, cfg.precision));
            } else {
                System.out.println(toJson(result));
            }

            Files.writeString(Paths.get(cfg.output), toJson(report), StandardCharsets.UTF_8);
        } catch (Exception ex) {
            System.err.println("Error: " + ex.getMessage());
            System.exit(1);
        }
    }

    private static Config parseArgs(String[] args) {
        Config cfg = new Config();
        List<String> pos = new ArrayList<>();
        for (int i = 0; i < args.length; i++) {
            String a = args[i];
            if (!a.startsWith("--")) { pos.add(a); continue; }
            switch (a) {
                case "--operation" -> cfg.operation = args[++i];
                case "--matrix2" -> cfg.matrix2 = args[++i];
                case "--scalar" -> cfg.scalar = Double.parseDouble(args[++i]);
                case "--sparse" -> cfg.sparse = true;
                case "--precision" -> cfg.precision = Integer.parseInt(args[++i]);
                case "--output" -> cfg.output = args[++i];
                default -> throw new IllegalArgumentException("Unknown option: " + a);
            }
        }
        if (!pos.isEmpty()) cfg.matrix1 = pos.get(0);
        return cfg;
    }

    private static RealMatrix readMatrix(Path p) throws IOException {
        String txt = Files.readString(p, StandardCharsets.UTF_8).trim();
        if (p.toString().toLowerCase().endsWith(".json")) {
            return MatrixUtils.createRealMatrix(parseJsonMatrix(txt));
        }
        String[] lines = txt.split("\\R");
        double[][] m = new double[lines.length][];
        for (int i = 0; i < lines.length; i++) {
            String[] parts = lines[i].split(",");
            m[i] = new double[parts.length];
            for (int j = 0; j < parts.length; j++) m[i][j] = Double.parseDouble(parts[j].trim());
        }
        return MatrixUtils.createRealMatrix(m);
    }

    private static double[][] parseJsonMatrix(String txt) {
        // Minimal JSON matrix parser for [[...], [...]]
        txt = txt.replaceAll("\\s+", "");
        if (!txt.startsWith("[[") || !txt.endsWith("]]")) throw new IllegalArgumentException("Invalid JSON matrix");
        txt = txt.substring(2, txt.length() - 2);
        String[] rows = txt.split("\\],\\[");
        double[][] out = new double[rows.length][];
        for (int i = 0; i < rows.length; i++) {
            String[] vals = rows[i].split(",");
            out[i] = new double[vals.length];
            for (int j = 0; j < vals.length; j++) out[i][j] = Double.parseDouble(vals[j]);
        }
        return out;
    }

    private static Map<String, Object> compute(Config cfg, RealMatrix A, RealMatrix B) {
        Map<String, Object> out = new LinkedHashMap<>();

        if (cfg.scalar != null) out.put("scalarMultiply", toList(A.scalarMultiply(cfg.scalar)));

        switch (cfg.operation) {
            case "add" -> out.put("result", toList(A.add(B)));
            case "subtract" -> out.put("result", toList(A.subtract(B)));
            case "multiply" -> out.put("result", toList(A.multiply(B)));
            case "transpose" -> out.put("result", toList(A.transpose()));
            case "determinant" -> out.put("result", new LUDecomposition(A).getDeterminant());
            case "inverse" -> {
                RealMatrix inv = new LUDecomposition(A).getSolver().getInverse();
                out.put("result", toList(inv));
                out.put("inverseVerification", toList(A.multiply(inv)));
            }
            case "eigenvalues" -> {
                EigenDecomposition ed = new EigenDecomposition(A);
                double[] vals = ed.getRealEigenvalues();
                List<Double> v = new ArrayList<>();
                for (double x : vals) v.add(x);
                out.put("result", v);
            }
            case "rank" -> out.put("result", new SingularValueDecomposition(A).getRank());
            case "trace" -> out.put("result", A.getTrace());
            case "lu" -> {
                LUDecomposition lu = new LUDecomposition(A);
                out.put("result", Map.of("L", toList(lu.getL()), "U", toList(lu.getU()), "P", toList(lu.getP())));
            }
            default -> throw new IllegalArgumentException("Unsupported operation: " + cfg.operation);
        }
        return out;
    }

    private static Map<String, Object> props(RealMatrix A) {
        int r = A.getRowDimension(), c = A.getColumnDimension();
        boolean sym = r == c && A.subtract(A.transpose()).getNorm() < 1e-9;
        boolean posDef = false;
        if (sym) {
            try {
                new CholeskyDecomposition(A);
                posDef = true;
            } catch (Exception ignored) {}
        }
        Double cond = r == c ? new SingularValueDecomposition(A).getConditionNumber() : null;

        Map<String, Object> m = new LinkedHashMap<>();
        m.put("dimensions", List.of(r, c));
        m.put("rank", new SingularValueDecomposition(A).getRank());
        m.put("trace", r == c ? A.getTrace() : null);
        m.put("isSymmetric", sym);
        m.put("isPositiveDefinite", posDef);
        m.put("conditionNumber", cond);
        return m;
    }

    private static List<List<Double>> toList(RealMatrix M) {
        List<List<Double>> out = new ArrayList<>();
        for (int i = 0; i < M.getRowDimension(); i++) {
            List<Double> row = new ArrayList<>();
            for (int j = 0; j < M.getColumnDimension(); j++) row.add(M.getEntry(i, j));
            out.add(row);
        }
        return out;
    }

    private static String formatMatrix(List<List<Double>> data, int precision) {
        int rows = data.size();
        int cols = data.get(0).size();
        String[][] text = new String[rows][cols];
        int[] w = new int[cols];
        for (int i = 0; i < rows; i++) {
            for (int j = 0; j < cols; j++) {
                text[i][j] = String.format(Locale.ROOT, "%." + precision + "f", data.get(i).get(j));
                w[j] = Math.max(w[j], text[i][j].length());
            }
        }
        StringBuilder sb = new StringBuilder();
        for (int i = 0; i < rows; i++) {
            if (i > 0) sb.append('\n');
            for (int j = 0; j < cols; j++) {
                if (j > 0) sb.append("  ");
                sb.append(String.format("%" + w[j] + "s", text[i][j]));
            }
        }
        return sb.toString();
    }

    private static Map<String, Object> demo() {
        RealMatrix A = MatrixUtils.createRealMatrix(new double[][] {{4,2,1},{0,5,3},{2,1,6}});
        RealMatrix B = MatrixUtils.createRealMatrix(new double[][] {{1,2,3},{4,5,6},{7,8,9}});
        RealMatrix C = MatrixUtils.createRealMatrix(new double[][] {{2,1,0,3},{1,0,2,2},{3,1,1,0}});
        RealMatrix D = MatrixUtils.createRealMatrix(new double[][] {{1,2,3},{0,1,4},{5,6,0},{1,0,2}});

        EigenDecomposition ed = new EigenDecomposition(A);
        LUDecomposition lu = new LUDecomposition(A);

        Map<String, Object> out = new LinkedHashMap<>();
        out.put("add", toList(A.add(B)));
        out.put("multiply", toList(C.multiply(D)));
        out.put("determinant", lu.getDeterminant());
        out.put("inverse", toList(lu.getSolver().getInverse()));
        out.put("eigenvalues", Arrays.stream(ed.getRealEigenvalues()).boxed().toList());
        out.put("lu", Map.of("L", toList(lu.getL()), "U", toList(lu.getU())));
        return out;
    }

    private static String toJson(Object obj) {
        if (obj == null) return "null";
        if (obj instanceof String s) return '"' + s.replace("\\", "\\\\").replace("\"", "\\\"") + '"';
        if (obj instanceof Number || obj instanceof Boolean) return obj.toString();
        if (obj instanceof Map<?, ?> m) {
            StringBuilder sb = new StringBuilder("{");
            boolean first = true;
            for (Map.Entry<?, ?> e : m.entrySet()) {
                if (!first) sb.append(',');
                first = false;
                sb.append(toJson(String.valueOf(e.getKey()))).append(':').append(toJson(e.getValue()));
            }
            sb.append('}');
            return sb.toString();
        }
        if (obj instanceof Iterable<?> it) {
            StringBuilder sb = new StringBuilder("[");
            boolean first = true;
            for (Object x : it) {
                if (!first) sb.append(',');
                first = false;
                sb.append(toJson(x));
            }
            sb.append(']');
            return sb.toString();
        }
        return toJson(String.valueOf(obj));
    }
}