Complexity can be objectively measured to some extent, especially in the context of information theory and algorithmic complexity. In information theory, complexity can be measured using metrics such as entropy and Kolmogorov complexity, which provide objective measures of the amount of information or computational resources required to describe a system. However, when it comes to measuring the complexity of real-world systems or phenomena, there is often a subjective element involved, as different observers may prioritize different aspects of complexity. Therefore, while certain aspects of complexity can be objectively measured, the overall assessment of complexity may still involve some degree of subjectivity. **

The savings bank may be blocking online banking customers for security reasons. They may have detected suspicious activity on certain accounts and are taking precautionary measures to protect their customers' funds and personal information. Additionally, the bank may be conducting routine maintenance or updates to their online banking system, temporarily restricting access to ensure a smooth and secure user experience once the maintenance is complete. It is also possible that there may be technical issues or glitches with the online banking platform that need to be resolved before customers can access their accounts again. **

The time complexity of Mergesort is O(n log n) in the worst-case scenario, where n is the number of elements in the array. This complexity arises from the fact that Mergesort divides the array into halves recursively and then merges them back together in sorted order. The space complexity of Mergesort is O(n) due to the need for additional space to store the divided subarrays during the sorting process. Overall, Mergesort is an efficient sorting algorithm that performs well on large datasets. **

Savings bank transactions can typically be deleted by accessing the account statement or transaction history on the bank's online portal or mobile app. From there, you can select the specific transaction you wish to delete and look for an option to remove or delete it. If you are unable to delete the transaction yourself, you can contact your bank's customer service for assistance in removing the transaction from your account history. It's important to note that some transactions may not be able to be deleted, especially if they are already processed or finalized. **

One can get rid of complexity by breaking down the problem or situation into smaller, more manageable parts. This can help to identify the root causes of the complexity and address them individually. Additionally, simplifying processes, communication, and decision-making can help reduce complexity. It is also important to prioritize and focus on the most important aspects, while letting go of unnecessary details. Finally, seeking input and collaboration from others can provide fresh perspectives and help to streamline complex situations. **

Composing two functions has a complexity of O(1), as it involves simply applying one function to the output of the other. The time complexity does not depend on the size of the input, as the functions are applied sequentially. Therefore, the complexity of composing two functions is constant and does not increase with the size of the input. **

To use online banking with your savings account, you will first need to set up online banking with your bank. This usually involves creating an online account and linking your savings account to it. Once your account is set up, you can access your savings account online to check your balance, transfer funds, set up automatic transfers, and even pay bills. Online banking provides a convenient way to manage your savings account from anywhere with an internet connection. **

The Big O notations for time complexity are used to describe the upper bound on the growth rate of an algorithm's running time as the input size increases. Some common Big O notations include O(1) for constant time complexity, O(log n) for logarithmic time complexity, O(n) for linear time complexity, O(n^2) for quadratic time complexity, and O(2^n) for exponential time complexity. These notations help in analyzing and comparing the efficiency of different algorithms. **