10014 - Simple calculations

本文介绍了一个使用C语言编写的程序,该程序通过输入序列的初始值和系数来计算特定数学序列的下一个元素。程序首先读取序列的长度和初始值,然后接收序列的系数,并基于这些输入数据计算出序列的下一个值。
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <stdbool.h>

int main ( int argc, char * argv[] ) {
	int m, M;
	scanf( "%d", &M );
	for( m = 0; m < M; m++) {
		if( m > 0 ) printf( "\n" );

		int n;
		scanf( "%d", &n );
		double a_0, a_np1;
		double * c_n = malloc( sizeof( double ) * n );

		
		scanf( "%lf %lf", &a_0, &a_np1 );
		int i;
		for( i = 0; i < n; i++ ) scanf( "%lf", c_n + i );

		int c_a = n + 1;
		double constant = 0, last_constant = 0;

		constant = a_0 * 2;

		last_constant = constant;
		constant = ( constant + c_n [ 0 ] ) * 2 - a_0;
		
		for( i = 1; i < n; i++) {
			double t = constant;
			constant = ( constant + c_n[ i ] ) * 2 - last_constant;
			last_constant = t;
		}

		double a = (constant - a_np1) / c_a;
		double a_1 = a_0 * 2 - a;
		/* printf("const = %f, c_a = %d, a=%f, a_1=%f\n", constant, c_a, a, a_1); */
		printf( "%.2lf\n", a_1 );

		free( c_n );
	}
	return 0;
}

(base) karma@DESKTOP-PNUPFSC:~/train/cu4$ $vasp_std running 16 mpi-ranks, on 1 nodes distrk: each k-point on 8 cores, 2 groups distr: one band on 1 cores, 8 groups vasp.6.4.2 20Jul23 (build Sep 12 2025 21:37:29) complex POSCAR found type information on POSCAR Cu POSCAR found : 1 types and 32 ions scaLAPACK will be used ----------------------------------------------------------------------------- | | | W W AA RRRRR N N II N N GGGG !!! | | W W A A R R NN N II NN N G G !!! | | W W A A R R N N N II N N N G !!! | | W WW W AAAAAA RRRRR N N N II N N N G GGG ! | | WW WW A A R R N NN II N NN G G | | W W A A R R N N II N N GGGG !!! | | | | The value NCORE = 3 specified in the INCAR file was overwritten, | | because it was not compatible with the 8 processes available: | | NCORE = 1 | | was used instead, please check that this makes sense for your | | machine. | | | ----------------------------------------------------------------------------- ----------------------------------------------------------------------------- | | | W W AA RRRRR N N II N N GGGG !!! | | W W A A R R NN N II NN N G G !!! | | W W A A R R N N N II N N N G !!! | | W WW W AAAAAA RRRRR N N N II N N N G GGG ! | | WW WW A A R R N NN II N NN G G | | W W A A R R N N II N N GGGG !!! | | | | For optimal performance we recommend to set | | NCORE = 2 up to number-of-cores-per-socket | | NCORE specifies how many cores store one orbital (NPAR=cpu/NCORE). | | This setting can greatly improve the performance of VASP for DFT. | | The default, NCORE=1 might be grossly inefficient on modern | | multi-core architectures or massively parallel machines. Do your | | own testing! More info at https://www.vasp.at/wiki/index.php/NCORE | | Unfortunately you need to use the default for GW and RPA | | calculations (for HF NCORE is supported but not extensively tested | | yet). | | | ----------------------------------------------------------------------------- ----------------------------------------------------------------------------- | | | ----> ADVICE to this user running VASP <---- | | | | You have a (more or less) 'large supercell' and for larger cells it | | might be more efficient to use real-space projection operators. | | Therefore, try LREAL= Auto in the INCAR file. | | Mind: If you want to do very accurate calculations, keep the | | reciprocal projection scheme (i.e. LREAL=.FALSE.). | | | ----------------------------------------------------------------------------- LDA part: xc-table for Pade appr. of Perdew ----------------------------------------------------------------------------- | | | W W AA RRRRR N N II N N GGGG !!! | | W W A A R R NN N II NN N G G !!! | | W W A A R R N N N II N N N G !!! | | W WW W AAAAAA RRRRR N N N II N N N G GGG ! | | WW WW A A R R N NN II N NN G G | | W W A A R R N N II N N GGGG !!! | | | | Your reciprocal lattice and k-lattice belong to different lattice | | classes: | | | | The reciprocal lattice is simple cubic, | | whereas your k-lattice is simple tetragonal. | | | | Results are often still useful ... | | | ----------------------------------------------------------------------------- POSCAR, INCAR and KPOINTS ok, starting setup FFT: planning ... GRIDC FFT: planning ... GRID_SOFT FFT: planning ... GRID WAVECAR not read WARNING: random wavefunctions but no delay for mixing, default for NELMDL prediction of wavefunctions initialized - no I/O entering main loop
09-16
基于NSGA-III算法求解微电网多目标优化调度研究(Matlab代码实现)内容概要:本文围绕基于NSGA-III算法的微电网多目标优化调度展开研究,重点介绍了如何利用该先进多目标进化算法解决微电网系统中多个相互冲突的目标(如运行成本最小化、碳排放最低、供电可靠性最高等)的协同优化问题。文中结合Matlab代码实现,详细阐述了NSGA-III算法的基本原理、在微电网调度模型中的建模过程、约束条件处理、目标函数设计以及仿真结果分析,展示了其相较于传统优化方法在求解高维、非线性、多目标问题上的优越性。同时,文档还提供了丰富的相关研究案例和技术支持背景,涵盖电力系统优化、智能算法应用及Matlab仿真等多个方面。; 适合人群:具备一定电力系统基础知识和Matlab编程能力的研究生、科研人员及从事能源优化领域的工程技术人员;尤其适合正在进行微电网调度、多目标优化算法研究或撰写相关论文的研究者。; 使用场景及目标:①掌握NSGA-III算法的核心思想及其在复杂能源系统优化中的应用方式;②学习如何构建微电网多目标调度模型并利用Matlab进行仿真求解;③为科研项目、毕业论文或实际工程提供算法实现参考和技术支撑。; 阅读建议:建议读者结合文中提供的Matlab代码实例,逐步调试运行并深入理解算法流程与模型构建细节,同时可参考文档中列出的其他优化案例进行横向对比学习,以提升综合应用能力。
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