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

Educative-99 in Python: Accelerate Your Coding Interview Prep

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
Given a number `n`, calculate the corresponding Tribonacci number. 
The Tribonacci sequence $T_n$ is defined as: 

|  $T_0 = 0,\space T_1 = 1,\space T_2 = 1$,  and $\space T_{n+3} = T_n + T_{n+1} + T_{n+2},\space$ for $n >= 0$ | 
|------------|------------|------------|------------------------------------------------------| 

The input number, `n`, is a non-negative integer.

**Constraints:**

- $0 \leq$ `n` $\leq 37$
- The answer is guaranteed to fit within a 32-bit integer, i.e., 
answer $\leq 2 ^{31} - 1$

# Examples

Try to solve the  N-th Tribonacci Number problem.