IPO

Explore how to maximize your final capital by choosing up to k projects given initial capital and profit constraints. Understand how to approach the IPO problem by assessing project profits and capital requirements to optimize your investment strategy, using heap data structures and efficient selection methods.

We'll cover the following...

Statement
Examples
Understand the problem
Figure it out!
Try it yourself

Statement

An investor is looking to maximize their capital by undertaking a set of profitable projects. Due to limited time and resources, they can complete at most k distinct projects.

There are $n$ available projects. Each project i has:

A profit of profits[i] earned upon completion.
A minimum capital requirement of capital[i] needed to start the project.

The investor starts with an initial capital of c. After completing a project, its profit is immediately added to the investor's current capital.

The goal is to choose up to k different projects in a way that maximizes the investor’s final capital. Return the maximum capital achievable after completing these projects.

It is guaranteed that the answer fits within a 32-bit signed integer.

Constraints:

$1 \leq$ k $\leq 10^3$
$0 \leq$ c $\leq$ $10^9$
$n==$ profits.length
$n==$ capitals.length
$1 \leq n \leq 10^3$
$0 \leq$ profits[i] $\leq 10^4$
$0 \leq$ capitals[i] $\leq 10^9$

Examples

JavaScript

usercode > Solution.js

import { MinHeap, MaxHeap } from "./Heap.js";

/* The following definition is for MinHeap.

  You can use the same methods for MaxHeap.
  class MinHeap {

    size();          // return number of elements
    peek();          // return top element without removing
    push(val);       // insert element
    pop();           // remove and return top element
  }
*/

function maximumCapital(c, k, capitals, profits){

    // Replace this placeholder return statement with your code

    return -1
}

export{maximumCapital};

IPO

IPO

Statement

Examples

Understand the problem

Figure it out!

Try it yourself