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Sequential convex optimization vs Projected gradient descent
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$$textbf1) Projected Gradient Descent $$
$$min_x space f(x), text subject to x∈C $$
$$y_k+1=x_k−t_k∇f(x_k)$$
$$x_k+1=operatorname*argmin_x∈C‖y_k+1−x‖$$
$$textbf 2) Sequential convex programming$$
Constructs a sequence of convex optimization problems locally, and solves them to obtain a locally optimal solution.
What are the primary disadvantages and advantages of both? I know that generally interior point methods, which is commonly used in SQP are second-order methods. Does this affect the quality and speed of the solution in comparison?
convex-optimization nonlinear-optimization numerical-optimization non-convex-optimization
edited Jul 17 at 12:40
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asked Jul 17 at 12:01
Sridhar Thiagarajan
132214
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$$textbf1) Projected Gradient Descent $$
$$min_x space f(x), text subject to x∈C $$
$$y_k+1=x_k−t_k∇f(x_k)$$
$$x_k+1=operatorname*argmin_x∈C‖y_k+1−x‖$$
$$textbf 2) Sequential convex programming$$
Constructs a sequence of convex optimization problems locally, and solves them to obtain a locally optimal solution.
What are the primary disadvantages and advantages of both? I know that generally interior point methods, which is commonly used in SQP are second-order methods. Does this affect the quality and speed of the solution in comparison?
convex-optimization nonlinear-optimization numerical-optimization non-convex-optimization
edited Jul 17 at 12:40
Community♦
1
asked Jul 17 at 12:01
Sridhar Thiagarajan
132214
add a comment |Â
up vote
0
down vote
favorite
up vote
0
down vote
favorite
$$textbf1) Projected Gradient Descent $$
$$min_x space f(x), text subject to x∈C $$
$$y_k+1=x_k−t_k∇f(x_k)$$
$$x_k+1=operatorname*argmin_x∈C‖y_k+1−x‖$$
$$textbf 2) Sequential convex programming$$
Constructs a sequence of convex optimization problems locally, and solves them to obtain a locally optimal solution.
What are the primary disadvantages and advantages of both? I know that generally interior point methods, which is commonly used in SQP are second-order methods. Does this affect the quality and speed of the solution in comparison?
convex-optimization nonlinear-optimization numerical-optimization non-convex-optimization
edited Jul 17 at 12:40
Community♦
1
asked Jul 17 at 12:01
Sridhar Thiagarajan
132214
$$textbf1) Projected Gradient Descent $$
$$min_x space f(x), text subject to x∈C $$
$$y_k+1=x_k−t_k∇f(x_k)$$
$$x_k+1=operatorname*argmin_x∈C‖y_k+1−x‖$$
$$textbf 2) Sequential convex programming$$
Constructs a sequence of convex optimization problems locally, and solves them to obtain a locally optimal solution.
What are the primary disadvantages and advantages of both? I know that generally interior point methods, which is commonly used in SQP are second-order methods. Does this affect the quality and speed of the solution in comparison?
convex-optimization nonlinear-optimization numerical-optimization non-convex-optimization
edited Jul 17 at 12:40
Community♦
1
asked Jul 17 at 12:01
Sridhar Thiagarajan
132214
edited Jul 17 at 12:40
Community♦
1
edited Jul 17 at 12:40
Community♦
1
edited Jul 17 at 12:40
Community♦
1
1
asked Jul 17 at 12:01
Sridhar Thiagarajan
132214
asked Jul 17 at 12:01
Sridhar Thiagarajan
132214
asked Jul 17 at 12:01
Sridhar Thiagarajan
132214
132214
add a comment |Â
add a comment |Â
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