diff --git a/orx-gradient-descent/README.md b/orx-gradient-descent/README.md
new file mode 100644
index 00000000..37c00b2c
--- /dev/null
+++ b/orx-gradient-descent/README.md
@@ -0,0 +1,36 @@
+# orx-gradient-descent
+
+A gradient descent based minimizer that is incredibly
+easy to use.
+
+## Usage
+
+```kotlin
+// define a model
+class Model {
+    var x = 0.0
+    var y = 0.0 
+}
+
+val model = Model()
+minimizeModel(model) { m ->
+    (m.x-4.0)*(m.x-4.0) + (m.y-3.0)*(m.y-3.0)
+}
+
+// model.x is close to 4 and model y is close to 3 at this point
+```
+
+## Data binding
+
+Currently we support minimizing model classes that contain 
+`Double`, `Vector2`, `Vector3` and `Vector4` typed properties, 
+other types are silently ignored.
+
+An example of a supported model:
+```kotlin
+class Model {
+    var x = 0.0
+    var y = 0.0
+    var v2 = Vector2.ZERO
+}
+```
\ No newline at end of file
diff --git a/orx-gradient-descent/src/main/kotlin/GradientDescent.kt b/orx-gradient-descent/src/main/kotlin/GradientDescent.kt
index 58574959..2a447f2b 100644
--- a/orx-gradient-descent/src/main/kotlin/GradientDescent.kt
+++ b/orx-gradient-descent/src/main/kotlin/GradientDescent.kt
@@ -96,7 +96,7 @@ fun dot(x: Array<DoubleArray>, y: DoubleArray): DoubleArray = DoubleArray(x.size
 class MinimizationResult(val solution: DoubleArray, val value: Double, val gradient: DoubleArray,
                          val inverseHessian: Array<DoubleArray>, val iterations: Int)
 
-fun minimize(_x0: DoubleArray, endOnLineSearch: Boolean = true, tol: Double = 1e-8, maxIterations: Int = 1000, f: (DoubleArray) -> Double): MinimizationResult {
+fun minimize(_x0: DoubleArray, endOnLineSearch: Boolean = false, tol: Double = 1e-8, maxIterations: Int = 1000, f: (DoubleArray) -> Double): MinimizationResult {
     val grad = { a: DoubleArray -> gradient(a, f) }
     var x0 = _x0.copyOf()
     var g0 = grad(x0)
@@ -108,8 +108,8 @@ fun minimize(_x0: DoubleArray, endOnLineSearch: Boolean = true, tol: Double = 1e
     while (iteration < maxIterations) {
         require(g0.all { it == it && it != Double.POSITIVE_INFINITY && it != Double.NEGATIVE_INFINITY })
         val pstep = dot(H1, g0)
-        require(pstep.all { it == it }) { "pstep contains NaNs"}
-        require(pstep.all { it != Double.POSITIVE_INFINITY && it != Double.NEGATIVE_INFINITY }) { "pstep contains infs"  }
+        require(pstep.all { it == it }) { "pstep contains NaNs" }
+        require(pstep.all { it != Double.POSITIVE_INFINITY && it != Double.NEGATIVE_INFINITY }) { "pstep contains infs" }
         val step = neg(pstep)
 
         val nstep = norm2(step)
@@ -127,7 +127,7 @@ fun minimize(_x0: DoubleArray, endOnLineSearch: Boolean = true, tol: Double = 1e
             x1 = add(x0, s)
             f1 = f(x1)
 
-            require(f1 == f1) { "f1 is NaN"}
+            require(f1 == f1) { "f1 is NaN" }
             if (!(f1 - f0 >= 0.1 * t * df0)) {
                 break
             }
@@ -144,7 +144,7 @@ fun minimize(_x0: DoubleArray, endOnLineSearch: Boolean = true, tol: Double = 1e
         require(g1.all { it == it })
         val y = sub(g1, g0)
         val ys = dot(y, s)
-        if (ys==0.0) {
+        if (ys == 0.0) {
             break
         }
         val Hy = dot(H1, y)
@@ -172,3 +172,12 @@ fun minimize(_x0: DoubleArray, endOnLineSearch: Boolean = true, tol: Double = 1e
 
     return MinimizationResult(x0, f0, g0, H1, iteration)
 }
+
+fun <T : Any> minimizeModel(model: T, endOnLineSearch: Boolean = false, tol: Double = 1e-8, maxIterations: Int = 1000, function: (T) -> Double) {
+    val doubles = modelToArray(model)
+    val solution = minimize(doubles, endOnLineSearch, tol, maxIterations) {
+        arrayToModel(it, model)
+        function(model)
+    }
+    arrayToModel(solution.solution, model)
+}
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diff --git a/orx-gradient-descent/src/test/kotlin/TestMinimizeModel.kt b/orx-gradient-descent/src/test/kotlin/TestMinimizeModel.kt
new file mode 100644
index 00000000..47a885fc
--- /dev/null
+++ b/orx-gradient-descent/src/test/kotlin/TestMinimizeModel.kt
@@ -0,0 +1,34 @@
+import org.amshove.kluent.shouldBeNear
+import org.openrndr.extra.gradientdescent.minimizeModel
+import org.openrndr.math.Vector2
+import org.spekframework.spek2.Spek
+import org.spekframework.spek2.style.specification.describe
+
+object TestMinimizeModel : Spek({
+    describe("a model") {
+        val m = object {
+            var x = 0.0
+            var y = 0.0
+        }
+        it("can be minimized") {
+            minimizeModel(m) { m->
+                (m.x - 4.0) * (m.x - 4.0) + (m.y - 3.0) * (m.y - 3.0)
+            }
+            m.x.shouldBeNear(4.0, 0.01)
+            m.y.shouldBeNear(3.0, 0.01)
+        }
+    }
+
+    describe("a model with a Vector2 property") {
+        val m = object {
+            var position = Vector2.ZERO
+        }
+        it("can be minimized") {
+            minimizeModel(m) { m->
+                (m.position.x - 4.0) * (m.position.x - 4.0) + (m.position.y - 3.0) * (m.position.y - 3.0)
+            }
+            m.position.x.shouldBeNear(4.0, 0.01)
+            m.position.y.shouldBeNear(3.0, 0.01)
+        }
+    }
+})
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