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RREF Calculator

Convert a matrix to Reduced Row Echelon Form (RREF) using Gauss-Jordan elimination. Get pivots, rank, solution type, and step-by-step row operations you can follow.

Gauss-Jordan Exact Fractions Rank + Pivots Steps + CSV

Reduced Row Echelon Form Solver

Pick a matrix size, enter values (including fractions), and compute RREF with optional step-by-step elimination and history export.

Use an extra column if you’re entering an augmented matrix.

Matrix Input

Enter integers, decimals, or fractions like 3/4. Spaces are ignored.
Tip: For augmented systems, the last column is the constants vector. Watch for a row like [0 … 0 | 1], which means no solution.
Row Operation Notation Meaning Example
Swap rows Ri ↔ Rj Exchange two rows R1 ↔ R3
Scale a row Ri ← k·Ri Multiply a row by nonzero k R2 ← (1/3)·R2
Row replacement Ri ← Ri − k·Rj Eliminate entries using another row R3 ← R3 − 2·R1

What RREF Tells You

  1. Pivots: the leading 1s identify pivot columns (basic variables).
  2. Rank: the number of pivots.
  3. Consistency: a row of all zeros except the last augmented value indicates no solution.
  4. Free variables: non-pivot columns correspond to free variables and infinite solutions (when consistent).
If your matrix is not augmented, “solution type” is inferred using general rules (rank vs number of columns), while inconsistency checks apply only when an augmented last column exists.
Your RREF calculation history will appear here after you compute a matrix.

What Is RREF and Why It’s Useful

Reduced Row Echelon Form (RREF) is a “cleaned up” version of a matrix where the structure makes the math easy to read. Once a matrix is in RREF, you can instantly identify pivot columns, determine rank, and—when the matrix represents a system of equations—see whether the system has a unique solution, infinitely many solutions, or no solution.

RREF matters because it turns messy algebra into a consistent, repeatable process. Instead of guessing the next step, you apply the same three row operations over and over: swap rows, multiply a row by a nonzero constant, and add a multiple of one row to another. Gauss-Jordan elimination uses these operations to reach RREF.

RREF vs REF: The Key Difference

Row Echelon Form (REF) is partially simplified: pivots step to the right as you go down, and entries below pivots are zero. Reduced Row Echelon Form goes further. In RREF, each pivot is 1 and all other entries in that pivot column are 0, both above and below the pivot. That “reduced” requirement makes the matrix especially readable.

How Gauss-Jordan Elimination Produces RREF

Gauss-Jordan elimination works pivot by pivot. For each pivot column, you:

  • Find a nonzero pivot entry (swap rows if necessary).
  • Scale the pivot row so the pivot becomes 1.
  • Eliminate all other entries in the pivot column by row replacement.

Repeat until no more pivots can be found. The result is RREF, and the number of pivots equals the rank.

Pivot Columns, Rank, and What They Mean

A pivot is the first nonzero entry in a row after reduction. In RREF, each pivot is a leading 1. Pivot columns correspond to basic variables in a system and indicate which columns are linearly independent. The rank is the count of pivot rows (and pivot columns).

Rank is one of the most important summaries of a matrix because it describes how many independent directions the matrix spans. In systems, rank helps you predict solution behavior.

Using RREF to Solve Linear Systems

If you represent a linear system as an augmented matrix, RREF can reveal solutions directly. A consistent system has no contradictory row. An inconsistent system produces a row that represents an impossible statement, like 0 = 1.

Unique solution

When every variable column is a pivot (no free variables), the solution is unique. In an augmented matrix, the final column gives the solved values.

Infinitely many solutions

If the system is consistent but there are fewer pivots than variable columns, at least one variable is free. That creates infinitely many solutions.

No solution

If RREF contains a row like [0 0 … 0 | 1], the system is inconsistent and has no solution. This is one of the fastest things to spot in RREF.

Why Exact Fractions Can Be Better Than Decimals

Decimal arithmetic is fast, but it can introduce rounding noise, especially when pivots are small or when intermediate values grow. Exact rational arithmetic keeps results clean, which is helpful for homework, proofs, and symbolic reasoning. That’s why this calculator lets you enter values like 5/6 and compute exactly.

If you’re working with measured data (engineering, statistics), decimals may be more appropriate. For exact math, fractions often read better.

Common Mistakes When Doing RREF by Hand

Forgetting to clear above the pivot

REF only requires zeros below pivots. RREF requires zeros above pivots too. If you stop early, you might be in REF, not RREF.

Scaling by zero or using a near-zero pivot

You can only scale a row by a nonzero constant. If a pivot candidate is zero, swap with a lower row that has a nonzero entry in that column.

Mixing up row operations

The only allowed moves are swap, scale, and row replacement. Anything else changes the underlying system.

What If Your Matrix Is Not Augmented?

You can compute RREF for any matrix, not only augmented systems. For non-augmented matrices, pivots and rank still matter, and they tell you about independence, null space dimension, and how many constraints the matrix encodes. The “solution type” shown here is a helpful hint, but the strongest interpretation comes from whether you intended the matrix to represent equations.

Examples You Can Try Right Away

  • Augmented system (3×4): [1 2 −1 | 3; 2 4 1 | 7; 3 6 0 | 9]
  • Rank test (3×3): [1 2 3; 2 4 6; 1 1 1]
  • Fractions: [1/2 1/3 | 1; 2/3 3/4 | 0]

Limitations and Safe Use Notes

This calculator performs row reduction in a controlled way in your browser. For very large matrices, step-by-step output may be long. If you only need the final RREF, turn off steps for faster results. Always verify that your matrix matches the intended system and that the last column truly represents constants when using an augmented matrix.

FAQ

RREF Calculator – Frequently Asked Questions

Quick answers about reduced row echelon form, pivots, rank, and solution types.

RREF stands for Reduced Row Echelon Form. It is a standardized form of a matrix where each pivot is 1, pivot columns have zeros everywhere else, and all-zero rows (if any) appear at the bottom.

REF (row echelon form) has leading pivots with zeros below them. RREF goes further by making each pivot equal to 1 and clearing both above and below each pivot so pivot columns contain zeros everywhere except the pivot.

Yes. Converting an augmented matrix to RREF can directly show whether a system has a unique solution, infinitely many solutions, or no solution.

The rank is the number of pivots (leading 1s) in RREF. It tells you the dimension of the column space and how many independent equations or variables the system effectively has.

Pivot columns are the columns that contain a leading 1 (pivot) in some row. In RREF, these are easy to spot because each pivot column has zeros everywhere else.

Yes. Turn on step-by-step mode to see each row operation used in Gauss-Jordan elimination, including row swaps, scaling, and row replacement operations.

An augmented matrix appends the constants (right-hand side) of a linear system as an extra column. RREF of an augmented matrix can reveal the solution type.

A row like [0 0 0 … | 1] indicates inconsistency because it represents 0 = 1. That means the system has no solution.

You can enter integers, decimals, or fractions like 3/4. The calculator can compute using exact rational arithmetic for cleaner results.

No. Calculations run in your browser. History is kept only in the current session and clears when you refresh or clear it.

Results are for education and planning. For graded work, confirm whether your course expects REF or RREF and whether answers should be exact fractions or decimals.