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This approach uses a greedy algorithm in conjunction with priority queues to select the projects maximizing profits. The core idea is to iteratively choose the most profitable project that can be initiated with the current available capital.
Initially, projects are stored in a list along with their capital and profit requirements. We prioritize by try fetching the most profitable projects. We maintain available projects in a max-heap (or priority queue) to achieve this efficiently.
We attempt to start at most k projects, updating the available capital every time we include a new project. For every iteration, once we identify the most feasible projects that can be executed given the current capital, we pick and execute the one with the highest profit and update our available capital. This process repeats until no more projects can be started or we've reached k projects.
Time Complexity: O(N log N + K log N), where N is the number of projects. This involves an initial sort and effectively K max operations on a priority-like structure.
Space Complexity: O(N) due to storage used for auxiliary lists and heaps.
1#include <stdio.h>
2#include <stdlib.h>
3#include <stdbool.h>
4
5struct Project {
6 int capital
The solution uses two sorting steps and a manually managed max-heap (priority queue) to maintain the list of feasible projects based on the current capital. Given that C lacks inbuilt data structures for a priority queue, manual sorting is required whenever accessing the max element.
Initially, projects are sorted based on their capital requirements; during each main loop iteration, feasible projects are added to a mock priority queue (sorted array). If projects are listable, the topmost is selected and capital updated accordingly.
This dynamic programming approach delineates with the constraints arising from the number of projects executable. We maintain an accruing structure documenting the maximum possible capital achievable for the number of projects done. The dynamic programming strategy opts for inherent flexibility where feasible, owing to recursive checks across all projects capable of initiating.
Memoization ensures repeated subproblem results are accessed without re-computation, improving overall efficiency particularly in scenarios involving expedient overlap of capital ranges per project.
Despite the computationally exhaustive exploration of all project combinations, dynamic memorized caching dramatically curtails avoidant efforts during feasibility analysis—gaining crucial strategic leverage with convenience in considering bounded project completion restrictions.
Time Complexity: O(N * W * K) — represents total options reviewed in DP tableau per states, characterized by projects count, capital capability, projects aimed for completion.
Space Complexity: O(N * W) — retains memory for tailored subproblems caching overseeing potential states across projects under capital explored known projects.
1using System;
2using System.Collections.Generic;
3using System.Linq;
4
5class Solution {
6 private int[,] dp;
7
8 private int Maximum(int k, int w, Tuple<int, int>[] projects, int idx) {
9 if (k == 0 || idx >= projects.Length)
10 return w;
11
12 if (dp[idx, k] != -1)
13 return dp[idx, k];
14
15 int notTake = Maximum(k, w, projects, idx + 1);
16 int take = 0;
17
18 if (w >= projects[idx].Item1)
19 take = Maximum(k - 1, w + projects[idx].Item2, projects, idx + 1);
20
21 dp[idx, k] = Math.Max(take, notTake);
22 return dp[idx, k];
23 }
24
25 public int FindMaximizedCapital(int k, int w, int[] profits, int[] capital) {
26 var projects = new Tuple<int, int>[profits.Length];
27 for (int i = 0; i < profits.Length; i++)
28 projects[i] = new Tuple<int, int>(capital[i], profits[i]);
29
30 dp = new int[profits.Length, k + 1];
31 for (int i = 0; i < profits.Length; ++i)
32 for (int j = 0; j <= k; ++j)
33 dp[i, j] = -1;
34
35 return Maximum(k, w, projects, 0);
36 }
37}
38
39class Program {
40 static void Main(string[] args) {
41 Solution sol = new Solution();
42 int k = 2;
43 int w = 0;
44 int[] profits = {1, 2, 3};
45 int[] capital = {0, 1, 1};
46 int result = sol.FindMaximizedCapital(k, w, profits, capital);
47 Console.WriteLine("The final maximized capital is: " + result);
48 }
49}C# solution outlines similar recursion with memorization tendencies favoring high-yield project evaluation across dynamic lot projects able to function given project communications. Project conditions remain noted, memoizing viable recursion choices maintained in previously linked memorization channels for optimum results.