The prospect geological disaster geographic information system is based on the visual basic , and make use of mapobject of esri corporation to do then quadratic programming of cis , connected with the mapobject prepotency of geological information system 地质灾害勘察综合地球物理信息管理与解释系统是在visualbasic的基础上,利用esri公司的mapobject软件进行gis的二次开发,把visualbasic的强大的平台和mapobject地理信息系统方面的优势紧密的结合在一起。
For nonlinear l1 problem based on the conditions for optimality of the nonlinear l1 problem in [ 1 ] , we first discuss the descent direction of the objective function f ( x ) in theory , further more , we study the relation between the optimal solution of nonlinear l1 problem and the optimal solution of some kind of quadratic programming problem with box constrains . hence , we construct a descent algorithm for nonlinear l1 problem and prove the convergence of the algorithm 在文[ 1 ]所给的最优性条件的基础上,对非线性l _ 1问题从理论上研究了f ( x )的下降方向、最优解与某种框式约束最小二乘问题的最优解之间的关系,进而构造了一个非线性l _ 1问题的下降算法,并证明了该算法的收敛性。
When solving the problems , we use the support vector regression ( svr ) . first assuming the formula of function , then according to the differential and boundary conditions we transform the original problem to the quadratic programming problem . finally , use the learning algorithm of svr to decide the parameters 只要事先假设出所求函数的表达式,然后根据已知的微分关系和边界条件对待求函数进行约束将原问题转化为二次规划问题,再采用支持向量机回归算法对样本进行学习即可求出参数,确定待定函数的关系式。
Such methods are generally decreasing method , such as , feasible direction methods , constrained variable metric methods , etc . another class is sub - problems method , which approximates the optimal solution by solving a series of simple sub - problems , such as penalty function methods , trust region methods , and successive quadratic programming sub - problems , etc . the same property of two classes of methods is that they determine whether the next iterative point is " good " or " bad " by comparing the objective function value or merit function value at the current point and next iterative point 另一类叫做子问题算法,这种算法是通过一系列简单子问题的解来逼近原问题的最优解,如罚函数法、信赖域算法、逐步二次规划算法等。这两类算法的一个共同特点是,通过比较当前点和下一个迭代点的目标函数值或评价函数值来确定迭代点的“优”或“劣” ,若迭代点比当前点“优”则该迭代点可以被接受,否则须继续搜索或调整子问题。
The separating plane with maximal margin is the optimal separating hyperplane which has good generation ability . to find a optimal separating hyperplane leads to a quadratic programming problem which is a special optimization problem . after optimization all vectors are evaluated a weight . the vector whose weight is not zero is called support vector 而寻找最优分类超平面需要解决二次规划这样一个特殊的优化问题,通过优化,每个向量(样本)被赋予一个权值,权值不为0的向量称为支持向量,分类超平面是由支持向量构造的。
Sequential quadratic programming ( sqp ) method is developed to schedule the time intervals between each pair of adjacent knots such that the total traveling time is minimized subject to the physical constraints on joint velocities , accelerations , and jerk . algorithm comparison of flexible polyhedron and sqp is done to show the good quality of sqp 在速度、加速度、加速度变化率的约束条件下,使用二次规划法获得了运动总时间最短时的关节轨迹,将所用算法与现有算法对比表明了所用算法的优化性。
This paper applies generalized multipler method to translate convex quadratic programs with equal constraints and non - negative constraints into simple convex quadratic programs with non - negative constraints . the new algorithm is gotten by solving the simple quadratic program . it avoids the computation of inverse matrix and exploits sparsity structure in the matrix of the quadratic form . the results of numerical experiments show the effectiveness of the algorithm on large scale problems 根据广义乘子法的思想,将具有等式约束和非负约束的凸二次规划问题转化为只有非负约束的简单凸二次规划,通过解简单凸二次规划来得到解等式约束和非负约束的凸二次规划新算法,新算法不用求逆矩阵,这样可充分保持矩阵的稀疏性,用来解大规模稀疏问题.数值结果表明:在微机486 / 33上就能解较大规模的凸二次规划
Quadratic programming (QP) is a special type of mathematical optimization problem. It is the problem of optimizing (minimizing or maximizing) a quadratic function of several variables subject to linear constraints on these variables.