diff --git a/src/ops/linalg_solve.ts b/src/ops/linalg_solve.ts
new file mode 100644
index 0000000000..d660c26fd4
--- /dev/null
+++ b/src/ops/linalg_solve.ts
@@ -0,0 +1,193 @@
+/**
+ * @license
+ * Copyright 2018 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+
+/**
+ * Linear algebra resolution.
+ */
+
+import { scalar, tensor1d } from './tensor_ops';
+import { split } from './array_ops';
+import {ENV} from '../environment';
+import {Scalar, Tensor, Tensor2D} from '../tensor';
+import {assert} from '../util';
+
+import {eye, stack, unstack} from './array_ops';
+import {op} from './operation';
+
+/**
+ *
+ * @param a is a square matrix M (Tensor2d with shape `[r, c]` such that `r ===
+ * c`)
+ * @param b is a Tensor2d with shape `[r2, c2]`
+ * @desc `r === r2`
+ * @returns a matrix of shape `[r, c+c2]` after a [jordan-gauss
+ * elimination](https://en.wikipedia.org/wiki/Gaussian_elimination) on the
+ * matrix given by the concatenation `[a, b]`. The first r or c columns is an
+ * upper triangular matrix
+ */
+function gaussJordanTriangular(
+    a: Tensor2D, b: Tensor2D): {upperM: Tensor2D, det: Scalar} {
+  const [r, c] = a.shape;
+  const [r2, c2] = b.shape;
+  assert(r === r2, 'Second dimension size does not match');
+  let inv: Tensor = a.concat(b, 1);
+  const rows = Array.from({length: r}, (v, i) => i);
+  let coef = scalar(1);
+  for (let i = 0; i < r; i++) {
+    ({inv, coef} = ENV.engine.tidy(() => {
+      for (let j = i + 1; j < r; j++) {
+        const elt = inv.slice([j, i], [1, 1]).as1D().asScalar();
+        const pivot = inv.slice([i, i], [1, 1]).as1D().asScalar();
+        if (elt.dataSync()[0] !== 0) {
+          const factor = pivot.div(elt);
+          coef = coef.mul(factor).mul(scalar(-1));
+          const newrow =
+              inv.gather(tensor1d([i], 'int32'))
+                  .sub(inv.gather(tensor1d([j], 'int32')).mul(factor))
+                  .as1D();
+          const sli = inv.gather(tensor1d(rows.filter(e => e !== j), 'int32'));
+          const arr: Tensor[] = [];
+          if (j === 0) {
+            arr.push(newrow);
+          }
+          unstack(sli).forEach((t, ind) => {
+            if (ind !== j) {
+              arr.push(t);
+            } else {
+              arr.push(newrow);
+              arr.push(t);
+            }
+          });
+          if (j === r - 1) {
+            arr.push(newrow);
+          }
+          inv = stack(arr);
+        }
+      }
+      //  the first c colomns of inv is an upper triangular matrix
+      return {inv, coef};
+    }));
+  }
+  const determinant =
+      diagonalMul(split(inv, [c, c2], 1)[0] as Tensor2D).div(coef).asScalar();
+  return {upperM: inv as Tensor2D, det: determinant};
+}
+
+/**
+ *
+ * @param m Tensor2d or matrix
+ * @returns the product of the diagonal elements of @param m as a `tf.scalar`
+ */
+function diagonalMul(m: Tensor2D): Scalar {
+  const [r, c] = m.shape;
+  assert(r === c, 'Input is not a square matrix');
+  let mul = m.slice([0, 0], [1, 1]).as1D().asScalar();
+  for (let i = 0; i < r; i++) {
+    mul = m.slice([i, i], [1, 1]).as1D().asScalar();
+  }
+  return mul;
+}
+
+/**
+ *
+ * @param a is a unique square matrix M or a tensor of shape `[..., M, M]` whose
+ * inner-most 2 dimensions form square matrices
+ * @param b is a unique matrix M or a tensor of shape `[..., M, M]`
+ * @param adjoint is a boolean.
+ * If adjoint is false then each output matrix satisfies `a * output = b`
+ * (respectively `a[..., :, :] * output[..., :, :] = b[..., :, :]` if the inputs
+ * are arrays of matrixes) . If adjoint is true then each output matrix
+ * satisfies `adjoint(a) * output = b` (respectively `adjoint(a[..., :, :]) *
+ * output[..., :, :] = b[..., :,
+ * :]`).
+ */
+function solve_(
+    a: Tensor2D[]|Tensor2D, b: Tensor2D[]|Tensor2D,
+    adjoint = false): Tensor2D[]|Tensor2D {
+  if (Array.isArray(a) || Array.isArray(b)) {
+    assert(
+        (a as Tensor2D[]).length === (b as Tensor2D[]).length,
+        'Second dimension size does not match');
+    const sol: Tensor2D[] = [];
+    (a as Tensor2D[]).forEach((m, i) => {
+      sol.push(solve_unique_equation(m, (b as Tensor2D[])[i], adjoint));
+    });
+    return sol;
+  } else {
+    return solve_unique_equation(a, b, adjoint);
+  }
+}
+
+// helper to the solve equation
+function solve_unique_equation(
+    a: Tensor2D, b: Tensor2D, adjoint = false): Tensor2D {
+  return ENV.engine.tidy(() => {
+    const [r, c] = a.shape;
+    const [r2, c2] = b.shape;
+    assert(r === r2, 'Second dimension size does not match');
+    if (adjoint) {
+      a = adjM(a);
+    }
+    const {upperM, det} = gaussJordanTriangular(a, b);
+    assert(det.dataSync()[0] !== 0, 'Input matrix is not inversible');
+    const trian = unstack(upperM);
+    const len = trian.length;
+    trian[len - 1] =
+        trian[len - 1].div(trian[len - 1].slice(r - 1, 1).asScalar());
+    for (let i = r - 2; i > -1; i--) {
+      for (let j = r - 1; j > i; j--) {
+        trian[i] = trian[i].sub(trian[j].mul(trian[i].slice(j, 1).asScalar()));
+      }
+      trian[i] = trian[i].div(trian[i].slice(i, 1).asScalar());
+    }
+    return split(stack(trian), [c, c2], 1)[1] as Tensor2D;
+  });
+}
+
+/**
+ *
+ * @param x square matrix to invert
+ * @returns the invert matrix of @param m if inversible
+ */
+function invertMatrix_(m: Tensor2D): Tensor2D {
+  const [r, c] = m.shape;
+  assert(r === c, 'Input is not a square matrix');
+  return solve(m, eye(r)) as Tensor2D;
+}
+
+/**
+ *
+ * @param m Tensor2d or matrix
+ * @returns the determinant of @param m as a `tf.scalar`
+ */
+function det_(m: Tensor2D): Scalar {
+  return gaussJordanTriangular(m, eye(m.shape[0]) as Tensor2D).det;
+}
+
+/**
+ *
+ * @param m Tensor2d or matrix
+ * @returns the adjoint of @param m if inversible
+ */
+function adjointM_(m: Tensor2D): Tensor2D {
+  return invertMatrix(m).mul(det(m));
+}
+
+export const solve = op({solve_});
+export const invertMatrix = op({invertMatrix_});
+export const adjM = op({adjointM_});
+export const det = op({det_});
diff --git a/src/ops/linalg_solve_test.ts b/src/ops/linalg_solve_test.ts
new file mode 100644
index 0000000000..08875ce583
--- /dev/null
+++ b/src/ops/linalg_solve_test.ts
@@ -0,0 +1,69 @@
+/**
+ * @license
+ * Copyright 2017 Google Inc. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+
+import * as tf from '../index';
+import {concat} from '../index';
+import {describeWithFlags} from '../jasmine_util';
+import {ALL_ENVS, expectArraysClose} from '../test_util';
+
+import {adjM, invertMatrix} from './linalg_solve';
+
+describeWithFlags('solve_linear', ALL_ENVS, () => {
+  it('solve equation with a: the matrix eye', () => {
+    const a = tf.eye(3);
+    const b = tf.tensor2d([1, 2, 3], [3, 1]);
+    const x = tf.solve(a, b) as tf.Tensor2D;
+    expect(() => expectArraysClose(x, [1, 2, 3]));
+  });
+
+  it('solve equation with a: an array of matrix eye', () => {
+    const a = [tf.eye(3), tf.eye(3), tf.eye(3)];
+    const b = Array.from({length: 3}, (v, i) => tf.tensor2d([1, 2, 3], [3, 1]));
+    const x = concat(tf.solve(a, b) as tf.Tensor2D[]);
+    (tf.solve(a, b) as tf.Tensor2D[]).forEach(e => e.print());
+    expect(() => expectArraysClose(x, [1, 2, 3]));
+  });
+
+  it('solve equation with a the matrix eye times 2', () => {
+    const a = tf.eye(3).mul(tf.scalar(2)) as tf.Tensor2D;
+    const b = tf.tensor2d([1, 2, 3], [3, 1]);
+    const x = tf.solve(a, b) as tf.Tensor2D;
+    expect(() => expectArraysClose(x, [1, 2, 3]));
+  });
+
+  it('should throw error if a is not inversible', () => {
+    const a = tf.ones([3, 3]) as tf.Tensor2D;
+    const b = tf.tensor2d([1, 2, 3], [3, 1]);
+    expect(() => tf.solve(a, b)).toThrowError('Input matrix is not inversible');
+  });
+});
+
+describeWithFlags('invert_matrix', ALL_ENVS, () => {
+  it('invert a matrix', () => {
+    const m = tf.tensor2d([1, 3, 3, 1, 4, 3, 1, 3, 4], [3, 3]);
+    const inv = invertMatrix(m);
+    expect(() => expectArraysClose(inv, [7, -3, -3, -1, 1, 0, -1, 0, 1]));
+  });
+});
+
+describeWithFlags('adjoint_matrix', ALL_ENVS, () => {
+  it('computes the adjoint of a matrix', () => {
+    const m = tf.tensor2d([1, 3, 3, 1, 4, 3, 1, 3, 4], [3, 3]);
+    const a = adjM(m);
+    expect(() => expectArraysClose(a, [7, -3, -3, -1, 1, 0, -1, 0, 1]));
+  });
+});
diff --git a/src/ops/ops.ts b/src/ops/ops.ts
index 2c9be6f47f..3f88d4f812 100644
--- a/src/ops/ops.ts
+++ b/src/ops/ops.ts
@@ -39,6 +39,7 @@ export * from './lstm';
 export * from './moving_average';
 export * from './strided_slice';
 export * from './topk';
+export * from './linalg_solve';
 
 export {op} from './operation';