USACO: Score Inflation

本文介绍了一种解决USACO竞赛中分数最大化的算法问题,通过合理选择不同类别问题来确保在限定时间内获得最高分数。采用堆排序优化选择过程,并提供了一个C++实现示例。
Score Inflation

The more points students score in our contests, the happier we here at the USACO are. We try to design our contests so that people can score as many points as possible, and would like your assistance.

We have several categories from which problems can be chosen, where a "category" is an unlimited set of contest problems which all require the same amount of time to solve and deserve the same number of points for a correct solution. Your task is write a program which tells the USACO staff how many problems from each category to include in a contest so as to maximize the total number of points in the chosen problems while keeping the total solution time within the length of the contest.

The input includes the length of the contest, M (1 <= M <= 10,000) (don't worry, you won't have to compete in the longer contests until training camp) and N, the number of problem categories, where 1 <= N <= 10,000.

Each of the subsequent N lines contains two integers describing a category: the first integer tells the number of points a problem from that category is worth (1 <= points <= 10000); the second tells the number of minutes a problem from that category takes to solve (1 <= minutes <= 10000).

Your program should determine the number of problems we should take from each category to make the highest-scoring contest solvable within the length of the contest. Remember, the number from any category can be any nonnegative integer (0, one, or many). Calculate the maximum number of possible points.

PROGRAM NAME: inflate

INPUT FORMAT

Line 1: M, N -- contest minutes and number of problem classes
Lines 2-N+1: Two integers: the points and minutes for each class

SAMPLE INPUT (file inflate.in)

300 4
100 60
250 120
120 100
35 20

OUTPUT FORMAT

A single line with the maximum number of points possible given the constraints.

SAMPLE OUTPUT (file inflate.out)

605

(Take two problems from #2 and three from #4.)
 

思路:提供的每一套题目,都有不同的“价值”(分数/时间),我们用堆排序把这些题目先排好,然后选取价值从高到低的题目,数组best记录i分钟最多可以获得的分数为best[i]

内容概要:本文介绍了ENVI Deep Learning V1.0的操作教程,重点讲解了如何利用ENVI软件进行深度学习模型的训练与应用,以实现遥感图像中特定目标(如集装箱)的自动提取。教程涵盖了从数据准备、标签图像创建、模型初始化与训练,到执行分类及结果优化的完整流程,并介绍了精度评价与通过ENVI Modeler实现一键化建模的方法。系统基于TensorFlow框架,采用ENVINet5(U-Net变体)架构,支持通过点、线、面ROI或分类图生成标签数据,适用于多/高光谱影像的单一类别特征提取。; 适合人群:具备遥感图像处理基础,熟悉ENVI软件操作,从事地理信息、测绘、环境监测等相关领域的技术人员或研究人员,尤其是希望将深度学习技术应用于遥感目标识别的初学者与实践者。; 使用场景及目标:①在遥感影像中自动识别和提取特定地物目标(如车辆、建筑、道路、集装箱等);②掌握ENVI环境下深度学习模型的训练流程与关键参数设置(如Patch Size、Epochs、Class Weight等);③通过模型调优与结果反馈提升分类精度,实现高效自动化信息提取。; 阅读建议:建议结合实际遥感项目边学边练,重点关注标签数据制作、模型参数配置与结果后处理环节,充分利用ENVI Modeler进行自动化建模与参数优化,同时注意软硬件环境(特别是NVIDIA GPU)的配置要求以保障训练效率。
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