Fading Coder

One Final Commit for the Last Sprint

Multi-Dimensional Dynamic Programming for Optimal Path Sums in Grids

Problems that ask for a maximum or minimum sum along a path in a 2D grid can often be solved with dynamic programming where each cell’s optimal value depends on the best values of its predecessors. The recurrence follows a common pattern: best(r, c) = candidate(r, c) + max/min(best(prev1), best(prev...

Contest Problem Analysis: Maximization, Fragmentation, Permutations, and River Crossing

Maximizing Triples Product Given three integers, you can increment any of them exactly five times. The goal is to maximize their final product. To achieve the maximum product, we should always increment the smallest of the three integers. This minimizes the disparity between the values, which yields...

Inverse Dynamic Programming for Backpack Deletion

In standard 0/1 knapsack problems, the objective is often to determine the number of ways to fill a specific capacity by adding items. However, challlenges arise when we need to compute the number of valid combinations after excluding a specific item from an already calculated set. Let $F[j]$ repres...

Solving Unmemorable via Cartesian Tree Construction and Combinatorial DP

This problem requires an insightful observation into the relationship between interval boundaries and Cartesian tree topology. Key Insight: Interval Transformation First, transform the given intervals by incrementing each $l_i$ and decrementing each $r_i$. These adjusted ranges correspond precisely...

Advanced Competitive Programming Patterns in Interval Dynamic Programming

Merging Strings for Maximum Palindromes Given two strings $S1$ and $S2$, we aim to merge them into a single string $S3$ while maintaining the relative order of characters from the original strings. The goal is to find the maximum possible length of a palindromic substring within any such $S3$. A fou...

Dynamic Programming Solution for ARC162F Matrix Problem

Matrix is not easy to handle, so consider deriving the next row from the current row. Let the previous row have selected columns at positions (p_1, p_2, \ldots, p_k) as 1. Consider the choices of (x) in the current row. (The following requires drawing a diagram for understanding) Case Analysis Case...

Algorithmic Solutions for Grid Isosceles Triangle Counting and Interval Dynamic Programming

Problem 1: Counting Isosceles Triangles (Easy Variant) Description Given a grid containing characters, determine the total number of isosceles triangles that can be formed by connected asterisk (*) symbols. Solution Strategy Iterate through every cell that contains an asterisk, treating it as the po...

Solutions to 3 Classic Dynamic Programming Problems: Fibonacci Number, Climbing Stairs, Minimum Cost Climbing Stairs

Fibonacci Number (LeetCode 509) Problem Statement The Fibonacci sequence, denoted as F(n), starts with base values F(0) = 0 and F(1) = 1. For all integers n greater than 1, each term is the sum of the two preceding terms, following the formula F(n) = F(n-1) + F(n-2). Given an integer n, return the v...

Dynamic Programming Techniques and Problem Solutions

Jury Compromise Define dp[i][j][k] as whether it's feasible to consider the i-th candidate with a total defense score of j and prosecution score of k. Transition: dp[i][j][k] = dp[i][j][k] OR dp[i-1][j-defense[i]][k-prosecution[i]]. After DP, enumerate absolute differences to check feasibility. Spac...

Dynamic Programming Solutions for Knapsack Problems

0/1 Knapsack Model Given a knapsack with capacity V and n items, each item has a value v and weight w. Each item can be taken at most once. Determine the maximum total value that can be placed in the knapsack. There are two states for each item: take or not take, leading too 2^n possibilities. Defin...