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-# optimization_algorithms
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+# optimization_algorithms
+
+## Steepest Descent
+
+1- We need starting solution x^t. Zeroise the iteration which is t. Specify tolerance value as ε.
+2- at x^t point calculate g^t gradient and ||g^t|| then if ||g^t|| <= ε stop it, else continue.
+3- Specfify road direction as d^t = -g^t.
+4- Calculate f(x^t + a^t*d^t) as like a^t (step size) is minimum.
+5- Calculate new solution point based on: x^(t+1) = x^t + a^t*d^t.
+6- Increase iteration counter by 1 and go to 2.step.
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