摘要：The article discusses methods of unconditional optimization to solve the problem of choosing the most effective energy-saving technology in construction. The optimization condition has chosen the value of the rate of reduction of energy consumption during operation of the facility. The task of determining the most effective energy-saving technology is to evaluate how quickly the reduction of consumption of the i-type of energy occurs. For the solution, unconditional optimization methods were used: the steepest descent method and the gradient method. An algorithm has been developed to search for the minimum value of the function when solving the problem using the coordinate-wise descent method. The article presents an algorithm for determining the unconditional minimum using the Nelder-Mead method, which is not a gradient method of spatial search for the optimal solution. The methods considered are classic optimization methods. If there is a difficulty in finding a function on which the functional reaches its minimum, then these methods may not be effective in terms of convergence. In many problems, in particular, when sufficiently complex functions with a large number of parameters are used, it is most advisable to use methods that have a high convergence rate. Such methods are methods for finding the extremum of a function when moving along a gradient, i.e. gradient descent. The task of finding the minimum function of energy consumption is defined as the task of determining the anti-gradient of the objective function, i.e. function decreases in the opposite direction to the gradient. The direction of the anti-gradient is the direction of the steepest descent.
其他摘要：The article discusses methods of unconditional optimization to solve the problem of choosing the most effective energy-saving technology in construction. The optimization condition has chosen the value of the rate of reduction of energy consumption during operation of the facility. The task of determining the most effective energy-saving technology is to evaluate how quickly the reduction of consumption of the i -type of energy occurs. For the solution, unconditional optimization methods were used: the steepest descent method and the gradient method. An algorithm has been developed to search for the minimum value of the function when solving the problem using the coordinate-wise descent method. The article presents an algorithm for determining the unconditional minimum using the Nelder-Mead method, which is not a gradient method of spatial search for the optimal solution. The methods considered are classic optimization methods. If there is a difficulty in finding a function on which the functional reaches its minimum, then these methods may not be effective in terms of convergence. In many problems, in particular, when sufficiently complex functions with a large number of parameters are used, it is most advisable to use methods that have a high convergence rate. Such methods are methods for finding the extremum of a function when moving along a gradient, i.e. gradient descent. The task of finding the minimum function of energy consumption is defined as the task of determining the anti-gradient of the objective function, i.e. function decreases in the opposite direction to the gradient. The direction of the anti-gradient is the direction of the steepest descent.
