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Testing Minimum Path Sum Algorithm Implementations in hello-algo

This test suite evaluates implementations of the minimum path sum algorithm using different approaches in Go. It validates path finding solutions using brute force search, memoization, dynamic programming, and space-optimized dynamic programming methods.

Test Coverage Overview

The test suite provides comprehensive coverage of minimum path sum calculations in a grid matrix environment.

Tests brute force DFS implementation
Validates memoization-enhanced path finding
Examines dynamic programming solution
Verifies space-optimized DP implementation

Implementation Analysis

The testing approach implements multiple algorithmic strategies to solve the same problem, enabling performance comparison. The test utilizes Go’s testing framework with println outputs for result verification.

Implements grid initialization and matrix handling
Uses different memory optimization techniques
Compares various implementation approaches

Technical Details

Uses Go’s built-in testing package
Implements 2D slice manipulation
Utilizes fmt package for output formatting
Employs dynamic memory allocation for memoization

Best Practices Demonstrated

The test demonstrates strong algorithm implementation practices by comparing multiple solution approaches.

Progressive optimization techniques
Clear separation of different implementation strategies
Consistent output formatting
Proper memory management

krahets/hello-algo

codes/go/chapter_dynamic_programming/min_path_sum_test.go

            
// File: min_path_sum_test.go
// Created Time: 2023-07-23
// Author: Reanon ([email protected])

package chapter_dynamic_programming

import (
	"fmt"
	"testing"
)

func TestMinPathSum(t *testing.T) {
	grid := [][]int{
		{1, 3, 1, 5},
		{2, 2, 4, 2},
		{5, 3, 2, 1},
		{4, 3, 5, 2},
	}
	n, m := len(grid), len(grid[0])

	// 暴力搜索
	res := minPathSumDFS(grid, n-1, m-1)
	fmt.Printf("从左上角到右下角的最小路径和为  %d\n", res)

	// 记忆化搜索
	mem := make([][]int, n)
	for i := 0; i < n; i++ {
		mem[i] = make([]int, m)
		for j := 0; j < m; j++ {
			mem[i][j] = -1
		}
	}
	res = minPathSumDFSMem(grid, mem, n-1, m-1)
	fmt.Printf("从左上角到右下角的最小路径和为  %d\n", res)

	// 动态规划
	res = minPathSumDP(grid)
	fmt.Printf("从左上角到右下角的最小路径和为  %d\n", res)

	// 空间优化后的动态规划
	res = minPathSumDPComp(grid)
	fmt.Printf("从左上角到右下角的最小路径和为  %d\n", res)
}