2022-08-12：方案1 : {7, 10}；
xxxx : {a , b}；
1 2 3 4；
FunnyGoal = 100；
OffenseGoal = 130。
找到一个最少方案数，让FunnyGoal、OffenseGoal，都大于等于。
定义如下尝试过程：
贴纸数组stickers，
stickers[i][0] : i号贴纸的Funny值，
stickers[i][1] : i号贴纸的Offense值。
index…所有的贴纸，随便选择。index之前的贴纸不能选择，
在让restFunny和restOffense都小于等于0的要求下，返回最少的贴纸数量。
来自弗吉尼亚理工大学(VT)，算法考试卷。
答案2022-08-12：
递归，从左往右，要i还是不要i。
动态规划。
代码用rust编写。代码如下：
fn main() {
let mut stickers: Vec<Vec<i32>> =
vec![vec![1, 5], vec![2, 4], vec![3, 3], vec![4, 2], vec![5, 1]];
let ans1 = min_stickers1(&mut stickers, 3, 5);
let ans2 = min_stickers2(&mut stickers, 3, 5);
let ans3 = min_stickers3(&mut stickers, 3, 5);
println!("ans1 = {}", ans1);
println!("ans2 = {}", ans2);
println!("ans3 = {}", ans3);
}
fn min_stickers1(stickers: &mut Vec<Vec<i32>>, funny_goal: i32, offense_goal: i32) -> i32 {
return process1(stickers, 0, funny_goal, offense_goal);
}
const MAX_VALUE: i32 = 1 << 31 - 1;
fn process1(stickers: &mut Vec<Vec<i32>>, index: i32, rest_funny: i32, rest_offense: i32) -> i32 {
if rest_funny <= 0 && rest_offense <= 0 {
return 0;
}
if index == stickers.len() as i32 {
return MAX_VALUE;
}
// 不选当前的贴纸
let p1 = process1(stickers, index + 1, rest_funny, rest_offense);
// 选当前贴纸
let mut p2 = MAX_VALUE;
let next2 = process1(
stickers,
index + 1,
get_max(0, rest_funny - stickers[index as usize][0]),
get_max(0, rest_offense - stickers[index as usize][1]),
);
if next2 != MAX_VALUE {
p2 = next2 + 1;
}
return get_min(p1, p2);
}
fn get_max<T: Clone + Copy + std::cmp::PartialOrd>(a: T, b: T) -> T {
if a > b {
a
} else {
b
}
}
fn get_min<T: Clone + Copy + std::cmp::PartialOrd>(a: T, b: T) -> T {
if a < b {
a
} else {
b
}
}
// 改动态规划
fn min_stickers2(stickers: &mut Vec<Vec<i32>>, funny_goal: i32, offense_goal: i32) -> i32 {
let mut dp: Vec<Vec<Vec<i32>>> = vec![];
for i in 0..stickers.len() as i32 {
dp.push(vec![]);
for j in 0..funny_goal + 1 {
dp[i as usize].push(vec![]);
for _ in 0..offense_goal + 1 {
dp[i as usize][j as usize].push(0);
}
}
}
for i in 0..stickers.len() as i32 {
for j in 0..=funny_goal {
for k in 0..=offense_goal {
dp[i as usize][j as usize][k as usize] = -1;
}
}
}
return process2(stickers, 0, funny_goal, offense_goal, &mut dp);
}
fn process2(
stickers: &mut Vec<Vec<i32>>,
index: i32,
rest_funny: i32,
rest_offense: i32,
dp: &mut Vec<Vec<Vec<i32>>>,
) -> i32 {
if rest_funny <= 0 && rest_offense <= 0 {
return 0;
}
if index == stickers.len() as i32 {
return MAX_VALUE;
}
if dp[index as usize][rest_funny as usize][rest_offense as usize] != -1 {
return dp[index as usize][rest_funny as usize][rest_offense as usize];
}
// 不选当前的贴纸
let p1 = process2(stickers, index + 1, rest_funny, rest_offense, dp);
// 选当前贴纸
let mut p2 = MAX_VALUE;
let next2 = process2(
stickers,
index + 1,
get_max(0, rest_funny - stickers[index as usize][0]),
get_max(0, rest_offense - stickers[index as usize][1]),
dp,
);
if next2 != MAX_VALUE {
p2 = next2 + 1;
}
let ans = get_min(p1, p2);
dp[index as usize][rest_funny as usize][rest_offense as usize] = ans;
return ans;
}
// 严格位置依赖的动态规划
fn min_stickers3(stickers: &mut Vec<Vec<i32>>, funny_goal: i32, offense_goal: i32) -> i32 {
let n = stickers.len() as i32;
let mut dp: Vec<Vec<Vec<i32>>> = vec![];
for i in 0..n + 1 {
dp.push(vec![]);
for j in 0..funny_goal + 1 {
dp[i as usize].push(vec![]);
for _ in 0..offense_goal + 1 {
dp[i as usize][j as usize].push(0);
}
}
}
for f in 0..=funny_goal {
for o in 0..=offense_goal {
if f != 0 || o != 0 {
dp[n as usize][f as usize][o as usize] = MAX_VALUE;
}
}
}
let mut i = n - 1;
while i >= 0 {
for f in 0..=funny_goal {
for o in 0..=offense_goal {
if f != 0 || o != 0 {
let p1 = dp[(i + 1) as usize][f as usize][o as usize];
let mut p2 = MAX_VALUE;
let next2 = dp[(i + 1) as usize]
[get_max(0, f - stickers[i as usize][0]) as usize]
[get_max(0, o - stickers[i as usize][1]) as usize];
if next2 != MAX_VALUE {
p2 = next2 + 1;
}
dp[i as usize][f as usize][o as usize] = get_min(p1, p2);
}
}
}
i -= 1;
}
return dp[0][funny_goal as usize][offense_goal as usize];
}
执行结果如下：
左神java代码
