How to pick the best learning rate and optimizer using LearningRateScheduler

I have known the LearningRateScheduler from Coursera course but copying it the same way will result in poor model performance. Perhaps due to the range I set. The instructions from Keras website is limited.

def duo_LSTM_model(X_train, y_train, X_test,y_test,num_classes,batch_size=68,units=128, learning_rate=0.005, epochs=20, dropout=0.2, recurrent_dropout=0.2 ):

    model = tf.keras.models.Sequential()
    model.add(tf.keras.layers.Masking(mask_value=0.0, input_shape=(X_train.shape[1], X_train.shape[2])))
    model.add(tf.keras.layers.Bidirectional(LSTM(units, dropout=dropout, recurrent_dropout=recurrent_dropout,return_sequences=True)))
    model.add(tf.keras.layers.Bidirectional(LSTM(units, dropout=dropout, recurrent_dropout=recurrent_dropout)))
    model.add(Dense(num_classes, activation='softmax'))

    adamopt = tf.keras.optimizers.Adam(lr=learning_rate, beta_1=0.9, beta_2=0.999, epsilon=1e-8)
    RMSopt = tf.keras.optimizers.RMSprop(lr=learning_rate, rho=0.9, epsilon=1e-6)
    SGDopt = tf.keras.optimizers.SGD(lr=learning_rate, momentum=0.9, decay=0.1, nesterov=False)


    lr_schedule = tf.keras.callbacks.LearningRateScheduler(
    lambda epoch: 1e-8 * 10**(epoch / 20))

    model.compile(loss='binary_crossentropy',
                  optimizer=adamopt,
                  metrics=['accuracy'])

    history = model.fit(X_train, y_train,
                        batch_size=batch_size,
                        epochs=epochs,
                        validation_data=(X_test, y_test),
                        verbose=1,
                        callbacks=[lr_schedule])

    score, acc = model.evaluate(X_test, y_test,
                                batch_size=batch_size)

    yhat = model.predict(X_test)

    return history, that

I have two questions.

1. How 1e-8 * 10**(epoch / 20) does this work?

2. How should we choose the range for the 3 different optimizers?

from How to pick the best learning rate and optimizer using LearningRateScheduler