Optimization Algorithm Added

Author: rishabh-chaturvedi

Optimization/GradientDescent.py

@@ -0,0 +1,21 @@
+def GradientDescent(inputValue, learningRate, precision, maximumIterations, gradientFunction):
+    previousStepSize = 1 
+    iterationCounter = 0 
+    while previousStepSize > precision and iterationCounter < maximumIterations:
+        previousX = inputValue #Storing current x value in previousX
+        inputValue = inputValue - learningRate * gradientFunction(previousX) #Grad descent
+        previousStepSize = abs(inputValue - previousX) #Change in x
+        iterationCounter = iterationCounter+1 #iteration count
+        print("Iteration",iterationCounter,"\nX value is",inputValue) #Print iterations
+    return inputValue
+
+inputValue = float(input("Initial Value: "))
+learningRate = float(input("Learning Rate Alpha: "))
+precision = float(input("Precision Needed: "))
+maximumIterations = float(input("Maximum Iterations: "))
+print(float(input("Trial Number: ")))
+gradientFunction = lambda x: 2*(x+5) #Gradient of our function 
+
+minimum = GradientDescent(inputValue, learningRate, precision, maximumIterations, gradientFunction)
+
+print("The local minimum value is", minimum)