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

Speedrun the Microsoft Coding Interview: Top Problems in C++

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

Given a matrix, `mat` = [[0, 0, 0], [0, 1, 0], [1, 0, 0]], find the matrix that contains the distance of the nearest 0 for each cell.

Given a matrix, <code>mat</code> = [[0, 0, 0], [0, 1, 0], [1, 0, 0]], find the matrix that contains the distance of the nearest 0 for each cell.

Given a matrix, `mat` = [[0, 0, 0], [0, 1, 0], [1, 1, 1]], find the matrix that contains the distance of the nearest 0 for each cell.

Given a matrix, <code>mat</code> = [[0, 0, 0], [0, 1, 0], [1, 1, 1]], find the matrix that contains the distance of the nearest 0 for each cell.

Given a matrix, `mat` = [[0, 0, 1], [1, 1, 1], [1, 0, 1]], find the matrix that contains the distance of the nearest 0 for each cell.

Given a matrix, <code>mat</code> = [[0, 0, 1], [1, 1, 1], [1, 0, 1]], find the matrix that contains the distance of the nearest 0 for each cell.

01 Matrix

01 Matrix

std::vector<std::vector<int>> UpdateMatrix(std::vector<std::vector<int>> mat) {
 
 // Replace this placeholder return statement with your code
 return {};
}

# Quick Fix\nYou are Edward, a Senior Software Engineer who specializes in coding interview patterns, data structures and algorithms, and is great at debugging issues in code.\n\n- Edward, please keep in mind that you do not have to print the data of the placeholders. Also, do not mention anything about yourself.\n\nEdward, you are given the following data in the placeholders:\n- Function name: `UpdateMatrix`\n- Candidate's submitted solution code: USER_CODE\n- Error: ERROR_DESCRIPTION\n- Failed test case: FAILED_TEST_CASE\n- Coding language: `C++`\n\n- Edward, your task is to understand the type of error that is occurring in the coding language provided.\n- Once you've understood the error as per the error’s description, and why the test case failed, you will suggest how the candidate can improve their solution code to fix the error.\n- Edward, you will not give the corrected code, just a hint on what the error tells, and how to fix it.\n- Edward, please limit your response strictly to 100 words. No more words than the limit.

# Review Code\nEdward, your current task is to review the `UpdateMatrix` function(s) and the other function(s) invoked in it/them.\n\n-Take your time and review the candidate's solution code.\n- Do not review any extra/helper files or functions not invoked in the solution function `UpdateMatrix`.\n- Do not review the code of data structure objects instantiated like a binary tree, linked list, trie, union find, etc., in the solution functions. Just review the solution/algorithm function of the problem. \n- Also, if the solution code doesn’t use the coding pattern stated above, then please encourage the candidate to reattempt the problem using that pattern corectly.

# Review time complexity\n- Please provide a brief time complexity analysis of the function(s) `UpdateMatrix`. \n- While calculating time complexity, include the time complexity of built-in C++ functions invoked by the code. \n- The answer should be to the point in approximately 60 words.

# Review space complexity\n- Please provide a brief space complexity analysis of the function(s) `UpdateMatrix`. \n- While calculating space complexity, include the space complexity of built-in C++ functions invoked by the code. \n- Do not include the space used for the input parameters and the space used for just returning an output (not used as a scratch space). \n- The answer should be to the point in approximately 60 words.

TwoDArray

#include <string>

using namespace std;

class TwoDArray : public DataStructure
{

 // define class attributes

private:
 void generateError(string data)
 {
 cerr << "TwoDArray is not parsable: " << data;
 exit(-1);
 }

public:
 std::vector<vector<int>> val;

 TwoDArray()
 {
 // constructor
 }
 TwoDArray(std::vector<vector<int>> v)
 {
 this->val = v;
 // constructor
 }
 
 // return value will be passed to user program
 void *construct(string new_value, int typeOfDs)
 {
 json j_complete = json::parse(new_value);
 std::vector<std::vector<int>> temp = j_complete;
 this->val = temp;
 // cout << this->val.size();
 return this;
 // parse test case in json string 'value' to your desired data structure
 }

 // input param contains return value of user program
 string traverse(void *input, int typeOfDs)
 {
 TwoDArray * custom_ds =(TwoDArray *)input; 
 json result = (json)(custom_ds->val);
 return result.dump();
 // TwoDArray your data structure 'input' to a json string representing a test case output
 }

 // override operator<< for your data structure if required
};

def evaluate_results(self) -> object:
 import math

 def update_matrix(mat):
 m, n = len(mat), len(mat[0])

 for r in range(m):
 for c in range(n):

 if mat[r][c] > 0:

 above = mat[r - 1][c] if r > 0 else math.inf

 left = mat[r][c - 1] if c > 0 else math.inf

 mat[r][c] = min(above, left) + 1

 for r in range(m - 1, -1, -1):
 for c in range(n - 1, -1, -1):

 if mat[r][c] > 0:

 below = mat[r + 1][c] if r < m - 1 else math.inf

 right = mat[r][c + 1] if c < n - 1 else math.inf

 mat[r][c] = min(mat[r][c], below + 1, right + 1)
 return mat
 """
 self.__inputs is the list of inputs that are passed as input to function
 self.__actual_output is a list of outputs that is produced by the learner's code
 return : self.__edu__result object
 """
 # multiple outputs will be added at each index respectively for a single test_case
 expected_outputs = []
 inp = self.__inputs[0]
 expected = update_matrix(inp)
 # store the 1st expected output that should be displayed to the user 
 expected_outputs.append(expected)
 actual = self.__actual_outputs[0]
 if json.dumps(expected) == json.dumps(actual):
 self.__edu_result.store_result(expected_outputs, TestCaseResult.PASS)
 else:
 self.__edu_result.store_result(expected_outputs, TestCaseResult.FAIL)

 return self.__edu_result

#include "TwoDArray.cpp"
#include "UpdateMatrix.cpp"

TwoDArray UpdateMatrixMain(TwoDArray v)
{
 std::vector<std::vector<int>> result = UpdateMatrix(v.val);
 TwoDArray res = *(new TwoDArray(result));
 return res;
}

PrivateTestCases.yaml

testcases:
  - name: "testcase 1"
    inputs:
    - 1 : [[0]]
    output: 
    - 1 : [[0]]
  - name: "testcase 2"
    inputs:
    - 1: [[0,1],[1,1]]
    output:
    - 1 : [[0, 1], [1, 2]]
  - name: "testcase 3"
    inputs:
    - 1: [[0,0,1],[0,1,1],[1,0,1]]
    output:
    - 1 : [[0, 0, 1], [0, 1, 2], [1, 0, 1]]
  - name: "testcase 4"
    inputs:
    - 1: [[0,1,0,1], [1,0,1,0],[0,1,0,1],[1,0,1,0]]
    output:
    - 1 : [[0,1,0,1], [1,0,1,0],[0,1,0,1],[1,0,1,0]]
  - name: "testcase 5"
    inputs:
    - 1: [[0,1,0,1], [1,1,1,0],[0,1,1,1],[1,0,1,1]]
    output:
    - 1 : [[0, 1, 0, 1], [1, 2, 1, 0], [0, 1, 2, 1], [1, 0, 1, 2]]

def sum(input_1, input_2):
    return input_1+input_2

def validate_inputs(self) -> None:
 """
 self.__inputs is the list of inputs that are passed as input to function
 store the result using the self.store_result(error_msg, status)
 return : void
 """
 def is_valid_2d_array(arr):
 if not isinstance(arr, list) or not all(isinstance(row, list) for row in arr):
 return False # Not a valid 2D array if it's empty or contains non-list elements
 
 for row in arr:
 if any(elem is None or isinstance(elem, bool) for elem in row):
 return False # Not a valid 2D array if any element in a row is None
 if not all(isinstance(elem, int) for elem in row):
 return False # Not a valid 2D array if any element in a row is not an integer
 
 return True # Valid 2D array

 
 input1 = self.__inputs[0]
 # inputs can be accessed using self.__inputs starting from 0 to len(self.__inputs) - 1
 if not is_valid_2d_array(input1):
 self.store_result ("Not a valid test case", ValidationFunction.FAIL)
 elif not (1 <= len(input1) <= 10000):
 self.store_result ("expected 'mat' to have 1 <= size <= 10000", ValidationFunction.FAIL)
 elif any(len(row) not in range(1, 101) for row in input1) or any(len(input1) not in range(1, 101) for _ in range(len(input1[0]))):
 self.store_result("expected 'mat' rows and columns to be in the range 1-100", ValidationFunction.FAIL)
 elif any(any(num not in {0, 1} for num in row) for row in input1):
 self.store_result("expected all values in 'mat' to be 0 or 1", ValidationFunction.FAIL)
 elif all(all(num != 0 for num in row) for row in input1):
 self.store_result("expected at least one number in 'mat' to be 0", ValidationFunction.FAIL)
 elif len(set(len(row) for row in input1)) != 1:
 self.store_result("The number of columns in each row should be same.", ValidationFunction.FAIL)
 else:
 self.store_result("", ValidationFunction.PASS)

Greedy Techniques

Dynamic Programming

Conclusion

01 Matrix

Statement

Example

Understand the problem

Figure it out!

Try it yourself