diff --git a/kmath-noa/src/main/java/space/kscience/kmath/noa/JNoa.java b/kmath-noa/src/main/java/space/kscience/kmath/noa/JNoa.java
index 5f1d56d34..c5ca892d2 100644
--- a/kmath-noa/src/main/java/space/kscience/kmath/noa/JNoa.java
+++ b/kmath-noa/src/main/java/space/kscience/kmath/noa/JNoa.java
@@ -315,6 +315,38 @@ class JNoa {
     public static native void stepAdamOptim(long adamOptHandle);
 
     public static native void zeroGradAdamOptim(long adamOptHandle);
+    
+    public static native long rmsOptim(long jitModuleHandle, double learningRate);
+
+    public static native void disposeRmsOptim(long rmsOptHandle);
+
+    public static native void stepRmsOptim(long rmsOptHandle);
+
+    public static native void zeroGradRmsOptim(long rmsOptHandle);
+
+    public static native long adamWOptim(long jitModuleHandle, double learningRate);
+
+    public static native void disposeAdamWOptim(long adamWOptHandle);
+
+    public static native void stepAdamWOptim(long adamWOptHandle);
+
+    public static native void zeroGradAdamWOptim(long adamWOptHandle);
+
+    public static native long adagradOptim(long jitModuleHandle, double learningRate);
+
+    public static native void disposeAdagradOptim(long adagradOptHandle);
+
+    public static native void stepAdagradOptim(long adagradOptHandle);
+
+    public static native void zeroGradAdagradOptim(long adagradOptHandle);
+
+    public static native long sgdOptim(long jitModuleHandle, double learningRate);
+
+    public static native void disposeSgdOptim(long sgdOptHandle);
+
+    public static native void stepSgdOptim(long sgdOptHandle);
+
+    public static native void zeroGradSgdOptim(long sgdOptHandle);
 
     public static native void swapTensors(long lhsHandle, long rhsHandle);
 
diff --git a/kmath-noa/src/main/kotlin/space/kscience/kmath/noa/algebras.kt b/kmath-noa/src/main/kotlin/space/kscience/kmath/noa/algebras.kt
index edf380f24..f9113acf7 100644
--- a/kmath-noa/src/main/kotlin/space/kscience/kmath/noa/algebras.kt
+++ b/kmath-noa/src/main/kotlin/space/kscience/kmath/noa/algebras.kt
@@ -342,6 +342,18 @@ protected constructor(scope: NoaScope) :
 
     public fun NoaJitModule.adamOptimiser(learningRate: Double): AdamOptimiser =
         AdamOptimiser(scope, JNoa.adamOptim(jitModuleHandle, learningRate))
+
+    public fun NoaJitModule.rmsOptimiser(learningRate: Double): RMSpropOptimiser =
+        RMSpropOptimiser(scope, JNoa.rmsOptim(jitModuleHandle, learningRate))
+
+    public fun NoaJitModule.adamWOptimiser(learningRate: Double): AdamWOptimiser =
+        AdamWOptimiser(scope, JNoa.adamWOptim(jitModuleHandle, learningRate))
+
+    public fun NoaJitModule.adagradOptimiser(learningRate: Double): AdagradOptimiser =
+        AdagradOptimiser(scope, JNoa.adagradOptim(jitModuleHandle, learningRate))
+
+    public fun NoaJitModule.sgdOptimiser(learningRate: Double): SgdOptimiser =
+        SgdOptimiser(scope, JNoa.sgdOptim(jitModuleHandle, learningRate))
 }
 
 public sealed class NoaDoubleAlgebra
@@ -722,3 +734,4 @@ protected constructor(scope: NoaScope) :
     override fun NoaIntTensor.set(dim: Int, slice: Slice, array: IntArray): Unit =
         JNoa.setSliceBlobInt(tensorHandle, dim, slice.first, slice.second, array)
 }
+
diff --git a/kmath-noa/src/main/kotlin/space/kscience/kmath/noa/optim.kt b/kmath-noa/src/main/kotlin/space/kscience/kmath/noa/optim.kt
index bf91968dc..ca29d5fe3 100644
--- a/kmath-noa/src/main/kotlin/space/kscience/kmath/noa/optim.kt
+++ b/kmath-noa/src/main/kotlin/space/kscience/kmath/noa/optim.kt
@@ -22,4 +22,37 @@ internal constructor(scope: NoaScope, internal val optimiserHandle: OptimiserHan
     override fun dispose(): Unit = JNoa.disposeAdamOptim(optimiserHandle)
     override fun step(): Unit = JNoa.stepAdamOptim(optimiserHandle)
     override fun zeroGrad(): Unit = JNoa.zeroGradAdamOptim(optimiserHandle)
-}
\ No newline at end of file
+}
+
+public class RMSpropOptimiser
+internal constructor(scope: NoaScope, internal val optimiserHandle: OptimiserHandle)
+    : NoaOptimiser(scope) {
+    override fun dispose(): Unit = JNoa.disposeRmsOptim(optimiserHandle)
+    override fun step(): Unit = JNoa.stepRmsOptim(optimiserHandle)
+    override fun zeroGrad(): Unit = JNoa.zeroGradRmsOptim(optimiserHandle)
+}
+
+
+public class AdamWOptimiser
+internal constructor(scope: NoaScope, internal val optimiserHandle: OptimiserHandle)
+    : NoaOptimiser(scope) {
+    override fun dispose(): Unit = JNoa.disposeAdamWOptim(optimiserHandle)
+    override fun step(): Unit = JNoa.stepAdamWOptim(optimiserHandle)
+    override fun zeroGrad(): Unit = JNoa.zeroGradAdamWOptim(optimiserHandle)
+}
+
+public class AdagradOptimiser
+internal constructor(scope: NoaScope, internal val optimiserHandle: OptimiserHandle)
+    : NoaOptimiser(scope) {
+    override fun dispose(): Unit = JNoa.disposeAdagradOptim(optimiserHandle)
+    override fun step(): Unit = JNoa.stepAdagradOptim(optimiserHandle)
+    override fun zeroGrad(): Unit = JNoa.zeroGradAdagradOptim(optimiserHandle)
+}
+
+public class SgdOptimiser
+internal constructor(scope: NoaScope, internal val optimiserHandle: OptimiserHandle)
+    : NoaOptimiser(scope) {
+    override fun dispose(): Unit = JNoa.disposeSgdOptim(optimiserHandle)
+    override fun step(): Unit = JNoa.stepSgdOptim(optimiserHandle)
+    override fun zeroGrad(): Unit = JNoa.zeroGradSgdOptim(optimiserHandle)
+}
diff --git a/kmath-noa/src/test/kotlin/space/kscience/kmath/noa/TestJitModules.kt b/kmath-noa/src/test/kotlin/space/kscience/kmath/noa/TestJitModules.kt
index 0d6fbdbdc..2f4c81847 100644
--- a/kmath-noa/src/test/kotlin/space/kscience/kmath/noa/TestJitModules.kt
+++ b/kmath-noa/src/test/kotlin/space/kscience/kmath/noa/TestJitModules.kt
@@ -17,7 +17,7 @@ class TestJitModules {
     private val lossPath = resources.resolve("loss.pt").absolutePath
 
     @Test
-    fun testOptimisation() = NoaFloat {
+    fun testOptimisationAdam() = NoaFloat {
 
         setSeed(SEED)
 
@@ -52,4 +52,147 @@ class TestJitModules {
         assertTrue(loss.value() < 0.1)
     }!!
 
-}
\ No newline at end of file
+    @Test
+    fun testOptimisationRms() = NoaFloat {
+
+        setSeed(SEED)
+
+        val dataModule = loadJitModule(dataPath)
+        val netModule = loadJitModule(netPath)
+        val lossModule = loadJitModule(lossPath)
+
+        val xTrain = dataModule.getBuffer("x_train")
+        val yTrain = dataModule.getBuffer("y_train")
+        val xVal = dataModule.getBuffer("x_val")
+        val yVal = dataModule.getBuffer("y_val")
+
+        netModule.train(true)
+        lossModule.setBuffer("target", yTrain)
+
+        val yPred = netModule.forward(xTrain)
+        val loss = lossModule.forward(yPred)
+        val optimiser = netModule.rmsOptimiser(0.005)
+
+        repeat(250){
+            optimiser.zeroGrad()
+            netModule.forwardAssign(xTrain, yPred)
+            lossModule.forwardAssign(yPred, loss)
+            loss.backward()
+            optimiser.step()
+        }
+
+        netModule.forwardAssign(xVal, yPred)
+        lossModule.setBuffer("target", yVal)
+        lossModule.forwardAssign(yPred, loss)
+
+        assertTrue(loss.value() < 0.1)
+    }!!
+
+    @Test
+    fun testOptimisationAdamW() = NoaFloat {
+
+        setSeed(SEED)
+
+        val dataModule = loadJitModule(dataPath)
+        val netModule = loadJitModule(netPath)
+        val lossModule = loadJitModule(lossPath)
+
+        val xTrain = dataModule.getBuffer("x_train")
+        val yTrain = dataModule.getBuffer("y_train")
+        val xVal = dataModule.getBuffer("x_val")
+        val yVal = dataModule.getBuffer("y_val")
+
+        netModule.train(true)
+        lossModule.setBuffer("target", yTrain)
+
+        val yPred = netModule.forward(xTrain)
+        val loss = lossModule.forward(yPred)
+        val optimiser = netModule.adamWOptimiser(0.005)
+
+        repeat(250){
+            optimiser.zeroGrad()
+            netModule.forwardAssign(xTrain, yPred)
+            lossModule.forwardAssign(yPred, loss)
+            loss.backward()
+            optimiser.step()
+        }
+
+        netModule.forwardAssign(xVal, yPred)
+        lossModule.setBuffer("target", yVal)
+        lossModule.forwardAssign(yPred, loss)
+
+        assertTrue(loss.value() < 0.1)
+    }!!
+
+    @Test
+    fun testOptimisationAdagrad() = NoaFloat {
+
+        setSeed(SEED)
+
+        val dataModule = loadJitModule(dataPath)
+        val netModule = loadJitModule(netPath)
+        val lossModule = loadJitModule(lossPath)
+
+        val xTrain = dataModule.getBuffer("x_train")
+        val yTrain = dataModule.getBuffer("y_train")
+        val xVal = dataModule.getBuffer("x_val")
+        val yVal = dataModule.getBuffer("y_val")
+
+        netModule.train(true)
+        lossModule.setBuffer("target", yTrain)
+
+        val yPred = netModule.forward(xTrain)
+        val loss = lossModule.forward(yPred)
+        val optimiser = netModule.adagradOptimiser(0.005)
+
+        repeat(250){
+            optimiser.zeroGrad()
+            netModule.forwardAssign(xTrain, yPred)
+            lossModule.forwardAssign(yPred, loss)
+            loss.backward()
+            optimiser.step()
+        }
+
+        netModule.forwardAssign(xVal, yPred)
+        lossModule.setBuffer("target", yVal)
+        lossModule.forwardAssign(yPred, loss)
+
+        assertTrue(loss.value() < 0.1)
+    }!!
+
+    @Test
+    fun testOptimisationSgd() = NoaFloat {
+
+        setSeed(SEED)
+
+        val dataModule = loadJitModule(dataPath)
+        val netModule = loadJitModule(netPath)
+        val lossModule = loadJitModule(lossPath)
+
+        val xTrain = dataModule.getBuffer("x_train")
+        val yTrain = dataModule.getBuffer("y_train")
+        val xVal = dataModule.getBuffer("x_val")
+        val yVal = dataModule.getBuffer("y_val")
+
+        netModule.train(true)
+        lossModule.setBuffer("target", yTrain)
+
+        val yPred = netModule.forward(xTrain)
+        val loss = lossModule.forward(yPred)
+        val optimiser = netModule.sgdOptimiser(0.005)
+
+        repeat(250){
+            optimiser.zeroGrad()
+            netModule.forwardAssign(xTrain, yPred)
+            lossModule.forwardAssign(yPred, loss)
+            loss.backward()
+            optimiser.step()
+        }
+
+        netModule.forwardAssign(xVal, yPred)
+        lossModule.setBuffer("target", yVal)
+        lossModule.forwardAssign(yPred, loss)
+
+        assertTrue(loss.value() < 0.1)
+    }!!
+}