Learn and practice the Greedy Techniques and Dynamic Programming patterns to solve coding interview problems.

Grokking the Blind 75 Problems in Python

This module teaches you two of the most important problem-solving techniques in the optimization domain: Greedy Techniques and Dynamic Programming. Not all problems require an exhaustive search through the solution space. Rather, consistently choosing the locally optimal solution can be proven to yield the globally optimal solution. In such situations, Greedy Techniques result in simple, highly efficient solutions. Other problems have a well-defined structure, where the same subproblem is encountered and solved multiple times. Dynamic Programming techniques allow us to compute once and reuse the solutions to recurring subproblems.

Optimization Techniques

# Statement
A professional robber plans to rob some houses along a street. These houses are arranged in a circle, which means that the first and the last house are neighbors. The robber cannot rob adjacent houses because they have security alarms installed.  

Following the constraints mentioned above and given an integer array `money` representing the amount of money in each house, return the maximum amount the robber can steal without alerting the police.

**Constraints:**
* $1\leq$ `money.length` $\leq 10^3$
* $0\leq$ `money[i]` $\leq 10^3$

Try to solve the House Robber II problem.