alglib BLEIC优化器

我目前使用BLEIC进行最小化解决方案。我在以下链接中实现了一个MSDN示例案例http://msdn.microsoft.com/en-us/library/ff628587%28v=vs.93%29.aspx alglib BLEIC优化器

以下是我的源代码。

void function1_grad(const real_1d_array &x, double &func, real_1d_array &grad, void *ptr) 
{ 
// 
// this callback calculates f(x0,x1) = 20*x0 + 15*x1 
// and its derivatives df/d0 and df/dx1 
// set goal (20 * sa + 15 * vz); 

func = 20 * x[0] + 15 * x[1]; 
grad[0] = 20; 
grad[1] = 15; 
} 

int main(int argc, char **argv) 
{ 

// using BLEIC optimizer. 

//set initial point, wild guess middle point 
real_1d_array x = "[3000.0,4500.0]"; 

//set scale 
real_1d_array s = "[10.0,10.0]"; 

//set boundry 
// 0 <= vz <= 9000, 
// 0 <= sa <= 6000); 

real_1d_array bndl = "[+0.0,+0.0]"; 
real_1d_array bndu = "[+6000.0,+9000.0]"; 

//set linear constrain 
// 0.3 * sa + 0.4 * vz >= 2000, 
// 0.4 * sa + 0.2 * vz >= 1500, 
// 0.2 * sa + 0.3 * vz >= 500); 

real_2d_array c = "[[0.3,0.4,2000.0],[0.4,0.2,1500.0],[0.2,0.3,500.0]]"; 

//set >= (1), = (0), <= (-1) 
integer_1d_array ct = "[1,1,1]"; 

minbleicstate state; 
minbleicreport rep; 

// 
// These variables define stopping conditions for the underlying CG algorithm. 
// They should be stringent enough in order to guarantee overall stability 
// of the outer iterations. 
// 
// We use very simple condition (gradian base) - |g|<=epsg 
// 
double epsg = 0.00001; 
double epsf = 0; 
double epsx = 0; 

// 
// These variables define stopping conditions for the outer iterations: 
// * epso controls convergence of outer iterations; algorithm will stop 
// when difference between solutions of subsequent unconstrained problems 
// will be less than 0.0001 
// * epsi controls amount of infeasibility allowed in the final solution 
// 
double epso = 0.0001; 
double epsi = 0.0001; 

// 
// Now we are ready to actually optimize something: 
// * first we create optimizer 
// * we add boundary constraints 
// * we add linear constraints 
// * we set scale 
// * we tune stopping conditions 
// * and, finally, optimize and obtain results... 
// 
minbleiccreate(x, state); 
minbleicsetbc(state, bndl, bndu); 
minbleicsetlc(state, c, ct); 
minbleicsetscale(state,s); 
minbleicsetinnercond(state, epsg, epsf, epsx); 
minbleicsetoutercond(state, epso, epsi); 
alglib::minbleicoptimize(state, function1_grad); 
minbleicresults(state, x, rep); 

// 
// ...and evaluate these results 
// 
printf("%d\n", int(rep.terminationtype)); // EXPECTED: 4 
printf("%s\n", x.tostring(2).c_str()); // EXPECTED: [2,4] 
//Sleep(5000); 
return 0; 
}

我的问题是，当我设置不同的初始点，我得到不同的答案，有时会返回“NAN” 案例1：设置的初始点，real_1d_array X =“[3000.0,4500.0]”，返回正确的答案[2000 ，3500] case 2：set real_1d_array x =“[1000.0,1000.0]”，return [NAN，NAN]

由此产生了什么问题？以及如何解决它？

来源

2012-01-13 user1146820