What is the time complexity of Filter Restaurants by Vegan-Friendly, Price and Distance?

The optimal solution has a time complexity of O(n log n).

What data structures are used to solve Filter Restaurants by Vegan-Friendly, Price and Distance?

This problem uses Array, Sorting. Understanding these concepts is essential for solving this Medium problem efficiently.

What are the key insights for solving Filter Restaurants by Vegan-Friendly, Price and Distance?

Insight 1: The problem requires filtering based on multiple conditions, so efficient filtering is crucial. Insight 2: Sorting is a key operation, and the sorting criteria involve two levels of comparison: rating and ID.

Which companies ask LeetCode 1333. Filter Restaurants by Vegan-Friendly, Price and Distance in interviews?

This problem is frequently asked at Yelp.

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Filter Restaurants by Vegan-Friendly, Price and Distance - LeetCode 1333 Solution

Filter Restaurants by Vegan-Friendly, Price and Distance - Complete Solution Guide

Filter Restaurants by Vegan-Friendly, Price and Distance is LeetCode problem 1333, a Medium level challenge. This complete guide provides step-by-step explanations, multiple solution approaches, and optimized code in python3, java, cpp, c.

Problem Statement

Given the array restaurants where restaurants[i] = [id i , rating i , veganFriendly i , price i , distance i ] . You have to filter the restaurants using three filters. The veganFriendly filter will be either true (meaning you should only include restaurants with veganFriendly i set to true) or false (meaning you can include any restaurant). In addition, you have the filters maxPrice and maxDistance which are the maximum value for price and distance of restaurants you should consider respectivel

Detailed Explanation

The problem asks us to filter a list of restaurants based on three criteria: vegan-friendliness, maximum price, and maximum distance. Each restaurant is represented as a list of five integers: `[id, rating, veganFriendly, price, distance]`. We are given a `veganFriendly` filter (0 or 1), a `maxPrice`, and a `maxDistance`. The goal is to return a list of restaurant IDs that satisfy these filters, sorted first by rating in descending order, and then by ID in descending order for restaurants with the same rating.

Solution Approach

The solution involves two main steps: filtering and sorting. First, we iterate through the restaurants list and keep only the restaurants that satisfy the vegan-friendly, max price, and max distance criteria. Then, we sort the filtered restaurants based on the specified criteria (rating descending, then ID descending). Finally, we extract the IDs from the sorted list and return them.

Step-by-Step Algorithm

Step 1: Initialize an empty list `filtered_restaurants` to store the restaurants that pass the filter conditions.
Step 2: Iterate through the `restaurants` list. For each restaurant, check if it satisfies all the given conditions: `veganFriendly`, `maxPrice`, and `maxDistance`.
Step 3: If a restaurant satisfies all conditions, add it to the `filtered_restaurants` list.
Step 4: Sort the `filtered_restaurants` list according to the problem's criteria. Sort by rating in descending order first, and if ratings are equal, sort by ID in descending order.
Step 5: Create a list `result` to store the restaurant IDs.
Step 6: Iterate through the sorted `filtered_restaurants` list and add the ID of each restaurant to the `result` list.
Step 7: Return the `result` list.

Key Insights

Insight 1: The problem requires filtering based on multiple conditions, so efficient filtering is crucial.
Insight 2: Sorting is a key operation, and the sorting criteria involve two levels of comparison: rating and ID.
Insight 3: Representing the data as arrays allows for direct access and efficient comparison of restaurant attributes.

Complexity Analysis

Time Complexity: O(n log n)

Space Complexity: O(n)

Topics

This problem involves: Array, Sorting.

Companies

Asked at: Yelp.

Filter Restaurants by Vegan-Friendly, Price and Distance | LeetCode 1333

Medium

Array Sorting

Time: O(n log n)Space: O(n)

Solution Code

Available in:

import java.util.ArrayList;
import java.util.List;
import java.util.Arrays;
import java.util.Comparator;

class Solution {
    public List<Integer> filterRestaurants(int[][] restaurants, int veganFriendly, int maxPrice, int maxDistance) {
        List<int[]> filteredRestaurants = new ArrayList<>();
        
        for (int[] r : restaurants) {
            if (r[2] >= veganFriendly && r[3] <= maxPrice && r[4] <= maxDistance) {
                filteredRestaurants.add(r);
            }
        }
        
        filteredRestaurants.sort((r1, r2) -> {
            if (r1[1] != r2[1]) {
                return r2[1] - r1[1];
            } else {
                return r2[0] - r1[0];
            }
        });
        
        List<Integer> result = new ArrayList<>();
        for (int[] r : filteredRestaurants) {
            result.add(r[0]);
        }
        
        return result;
    }
}

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Problem Info

Description

Problem 1333: Filter Restaurants by Vegan-Friendly, Price and Distance - Detailed explanation and solution approaches.

Topics

Array Sorting

Companies

Yelp

Stats

Difficulty:Medium

Languages:4

Companies:1

Problem #:1333

Problem Explanation

💡

Think Before You Code

🤔 Ask Yourself

→What is the input? What output do I need?
→Can I solve a smaller version of this problem first?
→What patterns have I seen in similar problems?

✏️ Try This First

Before looking at the solution, try to solve it on paper with a simple example!

🚀 Try on LeetCode

Solution Approach

📋

Step-by-Step Solution

7 steps

1
Step 1: Initialize an empty list `filtered_restaurants` to store the restaurants that pass the filter conditions.
2
Step 2: Iterate through the `restaurants` list. For each restaurant, check if it satisfies all the given conditions: `veganFriendly`, `maxPrice`, and `maxDistance`.
3
Step 3: If a restaurant satisfies all conditions, add it to the `filtered_restaurants` list.
4
Step 4: Sort the `filtered_restaurants` list according to the problem's criteria. Sort by rating in descending order first, and if ratings are equal, sort by ID in descending order.
5
Step 5: Create a list `result` to store the restaurant IDs.
6
Step 6: Iterate through the sorted `filtered_restaurants` list and add the ID of each restaurant to the `result` list.
7
Step 7: Return the `result` list.

💡

Pro Tip for Students

Try tracing through each step with a small example on paper before coding. Understanding "why" each step happens is more important than memorizing the code!

⚡

Complexity Analysis

⏱️

Time Complexity

O(n log n)

✨ Log-linear - Great for sorting algorithms!

💾

Space Complexity

O(n)

📦 Linear space - Memory grows with input size.

📝

Why?

The time complexity is dominated by the sorting step. The space complexity is determined by the auxiliary space used to store the filtered restaurants, which can be at most n in the worst-case scenario.

📖New to Big O? Click to learn more

Big O Notation - Quick Guide

O(1)Best - Instant

O(log n)Excellent - Binary Search

O(n)Good - One loop

O(n log n)OK - Sorting

O(n²)Slow - Nested loops

O(2ⁿ)Very Slow - Recursion

Alternative Approaches

Using Priority Queue (Heap)

Instead of sorting the entire filtered list, we can maintain a priority queue (min-heap) of size k, where k is the number of restaurants we want to return (in this case, effectively unbounded as we're returning all filtered restaurants). This approach is beneficial when we only want to return the top k restaurants, but less efficient if k is close to the total number of filtered restaurants.

Time: O(n log n) in the general case since all restaurants might get added and later removed from the heap. If we needed only the top k restaurants this could be O(n log k).Space: O(n) in the worst case since the heap could potentially contain all restaurants

Trade-offs: The priority queue approach is advantageous when you only need to find the top 'k' restaurants. However, when 'k' is close to the total number of restaurants, sorting is usually faster and more straightforward to implement.

✨

Key Insights

1Insight 1: The problem requires filtering based on multiple conditions, so efficient filtering is crucial.
2Insight 2: Sorting is a key operation, and the sorting criteria involve two levels of comparison: rating and ID.
3Insight 3: Representing the data as arrays allows for direct access and efficient comparison of restaurant attributes.

📚

Key Terms Explained

For Students

Array

A collection of items stored at contiguous memory locations. Like a row of boxes.

Sorting

Arranging elements in a specific order (ascending/descending).

Edge Cases

Edge case 1: Empty restaurant list. The code handles this gracefully, as the loop will simply not execute, and an empty list will be returned.
Edge case 2: All restaurants satisfying the filters. The code correctly handles this by adding all restaurants to the `filtered_restaurants` list and sorting them.
Edge case 3: No restaurants satisfying the filters. The `filtered_restaurants` list will remain empty, and the code will correctly return an empty list after the final extraction step.

⚠️

Common Mistakes to Avoid

✗Common mistake 1: Incorrectly implementing the sorting criteria. It's important to sort by rating in descending order and then by ID in descending order as a tie-breaker.

✗Common mistake 2: Not handling the vegan-friendly filter correctly. The condition `r[2] >= veganFriendly` is crucial to ensure that only vegan-friendly restaurants are included when `veganFriendly` is 1.

✗Common mistake 3: Forgetting to handle the case where the filtered list is empty. Returning an empty list in this case is the expected behavior.

✅

Learning Tip

Every mistake is a learning opportunity! When you make an error, understand why it happened before moving on.

🌍

Where is This Used in Real Life?

💼Real-world scenario 1: Restaurant recommendation systems. This algorithm can be used to filter restaurants based on user preferences such as vegan-friendliness, price range, and distance, and then rank them based on rating.

🏦Real-world scenario 2: E-commerce product filtering. This approach can be generalized to filter products based on various attributes (e.g., price, brand, rating) and then sort them based on relevance.

💡Understanding real-world applications helps you see the "why" behind algorithms. It's not just about passing interviews—these concepts power the apps and websites you use every day!

Author's Notes

💡 Key Insight

When dealing with arrays, always consider if sorting would simplify the problem. Many array problems have O(n) solutions using hash maps or two-pointer technique.

🎯 Interview Tip

If stuck, try working through a small example by hand. The pattern often reveals itself.

⚠️ Watch Out

Off-by-one errors are the most common mistake. Double-check your loop bounds and array indices.

Written by Saswata Mondal

Software Engineer • Algorithms Enthusiast • Interview Coach

👨‍💻

About the Author

👨‍💻

Saswata MondalAuthor400+ LC Problems

B.Tech CSE, IEM Kolkata | LeetCode Rating: 1672 (Top 15%)

Certified: IBM Java Developer, NPTEL Programming in Java

Last updated: August 13, 2024LeetCode Profile GitHub

This solution has been crafted based on my experience solving 400+ LeetCode problems. Each explanation is designed to help you understand the underlying concepts, not just memorize the code.

Community Tips & Insights

Try solving this without using extra space first. It's a great exercise for understanding the underlying pattern.

@CodeNinjaJul 14

In a real interview, always clarify constraints first. Ask about input size and value ranges.

@TechLeadJul 7

Don't forget to handle the edge case when the input is empty. Many candidates miss this in interviews.

@DevExpert92Jun 30

❓

Frequently Asked Questions

When should I use in-place modification vs creating a new array?

Use in-place modification when space complexity matters (O(1) space requirement) and the original array can be modified. Create a new array when you need to preserve the original data or when the problem involves significant restructuring that would complicate in-place logic.

How much time should I spend on a medium problem in interviews?

In a 45-minute interview, aim for: 5-10 minutes understanding and clarifying, 5-10 minutes discussing approach, 20-25 minutes coding, and 5-10 minutes testing. If stuck after 15 minutes of thinking, verbalize your thought process and ask for hints.

What is the importance of time and space complexity analysis?

Complexity analysis is crucial because: 1) Interviewers always ask about it, 2) It helps you choose between approaches, 3) It demonstrates CS fundamentals. Always state both time and space complexity, and be prepared to explain how you derived them.

What is the optimal approach for "Filter Restaurants by Vegan-Friendly, Price and Distance"?

For this medium-level Array problem, the key is to identify the core pattern. Analyze the input constraints to determine acceptable time complexity. Consider whether sorting techniques can optimize the solution. Always handle edge cases and validate your approach with examples before coding.

🤖 Need help with this problem?